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Studies on drug switchability showed heterogeneity in methodological approaches: a scoping review
Accepted Manuscript Title: Studies on drug switchability showed heterogeneity in methodological approaches: a scoping review Valeria Belleudi, Francesco Trotta, Simona Vecchi, Laura Amato, Antonio Addis, Marina Davoli PII:
S0895-4356(18)30174-4
DOI:
10.1016/j.jclinepi.2018.05.003
Reference:
JCE 9650
To appear in:
Journal of Clinical Epidemiology
Received Date: 26 February 2018 Revised Date:
18 April 2018
Accepted Date: 9 May 2018
Please cite this article as: Belleudi V, Trotta F, Vecchi S, Amato L, Addis A, Davoli M, Title: Studies on drug switchability showed heterogeneity in methodological approaches: a scoping review, Journal of Clinical Epidemiology (2018), doi: 10.1016/j.jclinepi.2018.05.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Title: Studies on drug switchability showed heterogeneity in methodological approaches: a
Running title: Review on observational designs on switchability Authors:
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Belleudi Valeria1 Trotta Francesco1
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Vecchi Simona1 Amato Laura1 Addis Antonio1
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Davoli Marina1 Affiliation:
Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
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scoping review
Corresponding author: Valeria Belleudi
Department of Epidemiology, Lazio Regional Health Service Via Cristoforo Colombo, 112 00147 Rome, Italy Phone: +39 06 99722133 – Fax: +39 06 99722111 1
ACCEPTED MANUSCRIPT Email: [email protected] Abstract Background: Several drugs share the same therapeutic indication, including those undergoing
the evaluation of switchability through observational research.
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patent expiration. Concerns on the interchangeability are frequent in clinical practice, challenging
Aim: To conduct a scoping review of observational studies on drug switchability to identify
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methodological strategies adopted to deal with bias and confounding.
Methods: We searched PubMed, EMBASE, and Web of Science (updated 1/31/2017) to identify
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studies evaluating switchability in terms of effectiveness/safety outcomes or compliance. Three reviewers independently screened studies extracting all characteristics. Strategies to address confounding, particularly, previous drug use and switching reasons were considered. All findings were summarized in descriptive analyses.
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Results: Thirty-two studies, published in the last 10 years, met the inclusion criteria. Epilepsy, cardiovascular and rheumatology were the most frequently represented clinical areas. 75% of the
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studies reported data on effectiveness/safety outcomes. The most frequent study design was cohort (65.6%) followed by case-control (21.9%) and self-controlled (12.5%). Case-control and case-
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crossover studies showed homogeneous methodological strategies to deal with bias and confounding. Among cohort studies, the confounding associated with previous drug use was addressed introducing variables in multivariate model (47.3%) or selecting only adherent patients (14.3%). Around 30% of cohort studies did not report reasons for switching. In the remaining 70%, clinical parameters or previous occurrence of outcomes were measured to identify switching connected with lack of effectiveness or adverse events.
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ACCEPTED MANUSCRIPT Conclusion: This study represents a starting point for researchers and administrators who are approaching the investigation and assessment of issues related to interchangeability of drugs.
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Keywords: drug switchability, comparator group, previous drug use, switching reasons, multiple
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switching; scoping review
What is new?
No standard methods have been established to evaluate switchability in observational research.
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The most frequent study design identified in this scoping review was cohort (65.6%) followed
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by case-control (21.9%) and self-controlled (12.5%). •
Case-control and case-crossover studies showed homogeneous methodological strategies to
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deal with bias and confounding, while heterogeneity in methodological approaches was observed for cohort studies. •
Specific factors should be considered when planning switchability studies to mitigate
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confounding and bias: i) the choice of comparator group; ii) previous drug use; iii) switching reasons; iv) multiple switching.
This study represents a starting point for researchers and administrators who are approaching
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the investigation and assessment of issues related to interchangeability of drugs.
Statement on prior posting/presentations: An abstract of this study has been submitted at the “IX Congress of the Italian Society for Medical Statistics and Clinical Epidemiology” to be held on 1416 September 2017 and it has been accepted as oral communication.
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Declarations of interest: none
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Word count (main text): 3279
Introduction
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Scientific progress has positively affected drug discovery and development leading to approval of different therapeutic alternatives in the same clinical setting. Furthermore, in recent years many
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blockbuster or innovative drugs have undergone patent expiration with consequent marketing authorization of generic or biosimilar versions with the same therapeutic indications.1-3 In this context, the availability of several drugs on the market sharing the same therapeutic indication raises concerns on the interchangeability, ie, the medical practice of changing one
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medicine for another that is expected to achieve the same clinical effect in a given clinical setting. More specifically, two different aspects should be considered when evaluating the drug interchangeability: drug prescribability and drug switchability.4 In the first scenario, new users are
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considered, and the prescription of one or another drug lead to the same clinical result. In the
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second scenario, only prevalent (experienced) users are considered, and this implies a change in therapy: a drug is substituted with another for various reasons, maintaining comparable efficacy and safety.
At the time of approval, for new drugs, only the prescribability should be demonstrated (whether it is a new chemical entity or a generic/biosimilar agent), while the switchability is not a requirement for granting marketing authorization and hence, a matter for pharmacovigilance and post-marketing studies. In fact, a recent document on biosimilars issued by the European Medicines Agency (EMA)
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ACCEPTED MANUSCRIPT emphasized that the switchability is not regulated by the Agency, but rather falls within the remit of the EU Member States.5 However, the lack of evidence on switchability is usually recognized by prescribers and health economists as a key limiting factor for the take up of generics/biosimilars in
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the market, reducing also the competition within therapeutic classes. Therefore, the demand for studies aimed at evaluating switchability between different drugs sharing the same indications as well as between different manufacturers of the same substance (whether they are originators or off-
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patent) is very high. Specifically, in the last case, the narrow therapeutic index of some category of drugs (eg, antiepileptic drugs), potential allergies to excipients, different shapes or colors, and a
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more or less comfortable drug intakes could be source of concern which may impact on outcomes. In the experimental context, individual bioequivalence model is needed to assess switchability, leading to develop new statistical approaches/models that take into account both the type of switch (transition, single, or multiple) and the drugs involved (generic versus branded, biosimilar versus
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originator, new generation drug versus old generation).6-15
Despite crossover trials being identified as the gold standard to evaluate switchability, the following
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methodological issues are still present:16
heterogeneity in variabilities between the test and reference product (within individual
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effects);6-9 •
different strategies to evaluate multiple alternation/switching;10
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difficulties in defining independent risk windows to evaluate the association between drug and outcome (carry-over effect).14
The evaluation of switchability through experimental design brings together classical concerns related to the generalizability of findings, the impossibility to study long-term clinical outcomes, and limited follow-up period. Therefore, there is a high demand for observational studies based on
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ACCEPTED MANUSCRIPT routinely collected data. However, in the observational context, the methodological approaches to evaluate prescribability (in naïve patients) are well established,17-19 while little is known about designs for assessing drug switchability in real practice. As the major concerns in observational
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context are related to confounding, when approaching evaluation of switchability, peculiar issues should be considered in planning the phase of the study such as the choice of comparator group, reasons for switching, and time in treatment before switching. Moreover, possible distortions due to
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multiple switching and carry-over effect should be adequately addressed.
The aim of this study was to conduct a scoping review on drug switchability to synthesize research
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evidence available in the scientific literature describing the use and reporting of the adopted methodological strategies to deal with bias and confounding.
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Methods
The review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.20
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Eligibility criteria and data sources
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All observational studies based on electronic medical records or those that claimed data assessing switchability of drugs with a comparator group and evaluated effectiveness/safety or compliance outcomes were eligible. Only studies conducted on human subjects published in English after the year 2000 were included. We excluded studies on device switching or those conducted in an ophthalmology setting, and studies with sample size ≤500 patients. Reviews, case reports, commentaries, cross-sectional studies, letters to the editor, and short communications were also excluded.
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ACCEPTED MANUSCRIPT We performed a systematic search using electronic databases including MEDLINE (via PubMed) (from January 1, 2000 to January 31, 2017); EMBASE (Elsevier, EMBASE.com) (from January 1, 2000 to January 31, 2017); and Web of Science (Thomson Reuters) (from January 1, 2006 to
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January 31, 2017). For each database, we developed a specific search strategy combining a specific subject heading (such as MeSH terms) and free terms (see Appendix 1 Figure 1 for details). We also
bibliography. Selection of the studies and data extraction
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conducted additional searches screening review articles and relevant editorials to identify useful
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Three reviewers independently screened the titles and abstracts obtained through the search strategy. All potentially eligible studies were obtained as full articles and were evaluated in-depth. In doubtful or controversial cases, all identified discrepancies were discussed until a consensus was reached on all items.
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A number of variables related to study characteristics and switching were extracted using a predefined template: publication year, country of data source, study design, data source, characteristics
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of the population (condition, sample size), investigated drugs, comparator group, outcomes, risk window/follow up, statistical methods/approaches, covariates for risk adjustment, and results. More
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specifically, the previous drug use was retrieved as: i) number of prescriptions; ii) duration of treatment; and iii) adherence/persistence. Switching reasons were extracted as clinical or nonclinical.
In particular, efforts to deal with previous drug use, switching reasons, and multiple switching were also collected (for further details see Figure 1). As the aim of the study focused on methodological issues (and not pooling of data), we did not conduct a risk of bias evaluation of each study included in this scoping review.
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ACCEPTED MANUSCRIPT We extracted and tabulated the study information and used this to perform a descriptive analysis.
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Results A total of 7,316 articles were identified from the literature search. After excluding duplicates, a total of 5,500 citations remained. The PRISMA flow diagram (Figure 2) reports the number of citations screened based on titles and abstracts and the reasons for the exclusion, leading to a sample of 56
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articles that were evaluated in full-text form. Then, 24 out of 56 studies were excluded as they did
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not satisfy our exclusion/inclusion criteria.21-44 A final sample of 32 studies evaluating drug switchability were judged eligible for inclusion in our review (see appendix Table 1 for full details). 45-76
Table 1 shows the main characteristics of the studies included in this review. The 32 studies were
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published between 2009 and 2016 (65.6% in the last 5 years), and the majority were carried out in USA (81.3%). The sample size ranged from 616 to 105,751 (median: 7,662) patients and data were mainly gathered from claims database (87.5%). The most frequent study design was cohort (65.6%)
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followed by case-control (21.9%) and self-controlled (12.5%). In cohort studies, two different comparator groups were considered: no switcher (81%) or another switching category (19%). For
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the other designs, only no-switcher comparator was considered. The clinical areas most frequently studied were epilepsy (37.5%), cardiovascular (25.0%), and rheumatology (12.5%). In particular, there were a high number of subjects to be involved in future meta-analyses in these areas. Clinical outcome was evaluated in 75% (28 out of 32) of the studies, (effectiveness, 18/28; safety, 4/28; or both 6/28), while only 12.5% of the studies (8/28) focused on adherence.
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ACCEPTED MANUSCRIPT Different switching categories were identified according to the Anatomical Therapeutic Chemical (ATC) classification system (Figure 2). In 40.6% of the studies, the switching has been analyzed considering the same substance was produced by different manufacturers (eg, originators and
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related off-patented version, ATC V level) and in 34.4%, the switching occurred among different substances belonging to the same therapeutic category (ATC IV level); in the 9% of the cases the switch occurred toward a combination of ATC IV and V level. Indeed, in 12.5% of the cases,
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switching was explored among drugs sharing the same indication but leading to different therapeutic categories. Twenty-two studies evaluating switchability involved small molecules,
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while the remaining considered biologics or/and peptides.
Case-control and case-crossover studies were based on claim data and showed homogeneous methodological strategies to deal with bias and confounding. In both study designs, to control for previous drug use and switching reasons, only adherent/persistent patients without previous
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outcome in two possible risk windows (1–3 or 3–6 months) were selected. The selection of patients without prior outcomes led to the exclusion of patients with a history of lack of effectiveness or side effects; thus, no switching due to these reasons can be included. Moreover, in the case-control
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design, matching and multivariate logistic models were used to control the effect of other potential reasons for switching; for example, in three studies, a variable to identify disease worsening pre-
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switch was evaluated as a confounder. In the case control studies, the switching nearest to the date of outcome was considered without any evaluation of prior switches. On the contrary, in the casecrossover studies, subjects with multiple switching (one in the case period and the other in the control period) did not contribute to the analysis. For cohort studies, heterogeneity in methodological approaches was observed (Figure 3A-D). The most frequently used adjustment technique was multivariate regression (52.4%), followed by propensity score matching (33.3%) (Figure 3A). Median duration of follow-up to evaluate outcomes
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ACCEPTED MANUSCRIPT was 12 months. However, only 57.4% of the studies used a regression model that took into account time post switch (person-time approach) to evaluate the association between switching and outcomes, rather than defining the outcome as a binary variable. The as-treated analysis (multiple
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switches were censored) was adopted in 57.1% of the studies, while in 33.3%, the analysis was intention-to-treat (Figure 3B). The majority of studies considered previous drug use as a key factor for the adjustment (81.0% of the studies quantified exposure to the drug from which patients were
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switching). Confounding associated with previous drugs was addressed in 14.3% of the cases by selecting adherent patients, while 47.6% of the studies considered previous drug use as a
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confounder in terms of the number of prescriptions, duration of treatment, or adherence/persistence (Figure 3C). In 19% of the cases, both inclusion criteria and adjustment were applied to control for previous drug use.
Around 30% of cohort studies did not report any information that allow direct or indirect reasons
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for switching. In the remaining studies, indirect variables (38.1%) or clinical parameters (33.3%) were used to identify switching connected with lack of effectiveness or adverse events (Figure 3D). When no switcher comparator was used, the index date (of no switcher) was defined using the first
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(7%), the last (43%), or a random (43%) date among all dispensing dates available in the enrolment
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period or considering an exposure time similar to the switcher (7%) (data not shown).
Discussion
Our scoping review shows that the evidence on observational studies evaluating switchability of drugs is recent, related to specific clinical areas and focused on effectiveness outcomes. Similar percentages of studies evaluated switching between the same substance (produced by different
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ACCEPTED MANUSCRIPT manufactures) or between different substances leading to the same therapeutic category. The most used study design is the cohort, although high heterogeneity was observed in terms of the methodological approaches adopted. On the contrary, the few studies with case-control and case-
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crossover designs used more homogeneous methodologies. Methodological approaches
Our review shows that different methodological approaches were attempted also when investigating
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similar research questions within the same clinical setting. For example, cohort studies evaluating switchability between anti-TNF in terms of effectiveness differed for the choice of comparator
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group; similar variability was observed in the case of switchability between statins using adherence as outcome. In addition, different study designs were adopted within the same clinical setting, such as the area of epilepsy, where cohort, case-control, and self-controlled design were used to assess switchability of antiepileptics in terms of clinical outcomes (seizure- or epilepsy-related
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hospitalization). This pattern has been also observed in a recent study focused on epilepsy.77 In general, several open issues exist for observational studies assessing switchability.
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Comparator groups and previous drug use
Two kinds of comparisons are possible: switcher versus non-switcher or switcher versus other
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switchers. In the case of non-switcher comparator group, a further complexity pertains to the selection of the index date for non-switchers. Our review highlighted two possible solutions: i) selecting one date among all prescription dates, usually randomly; ii) selecting the date on the basis of time to switch observed in the switcher group. Using another switching category as a comparator group has been observed only for cohort studies. The choice of comparator directly affected the validity of study results and must be carefully evaluated, taking into account the research question, informational needs of the stakeholder and 11
ACCEPTED MANUSCRIPT strategy to minimize bias.18 In several cases, to compare population with similar characteristics, the choice of another group of switchers is preferable (eg, switch from DMARD to biologic1 or biologic2). Moreover, in the case of comparison respect “no-switcher”, sensitivity analyses on
are under reported in the studies included in this review.
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consistence of result varying index date are suggested; however, this concern and its implications
Previous drug use is a key element to balance groups and it can be disentangled by a restriction, ie,
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including only compliant patients, matching or testing a specific variable (eg, number/type of prescriptions, time in treatment, adherence) as confounders. Given the complexity of medication-
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taking behavior, most of the studies considered more than one variable to define drug user profile. Switching reasons
Another important element to be taken into account in comparative switchability studies is the nonrandom occurrence of switching. As described by Braun and Kudrin, the switch can be related to
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different clinical reasons (ie, lack of effectiveness, side effect, disease worsening), compliance, or organizational aspects (ie, costs and formulary considerations).78 These factors are likely to vary
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across types of switches (ie, same or different molecular entities, small molecules or biosimilars), clinical contexts, and countries (ie, regulatory decision on automatic substitution) as well as how
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their impact on confounding. In our review, about 30% of the cohort studies did not report any information that allows the reasons for switching to be identified. In most of the cases, the authors highlighted the impossibility to retrieve this information from administrative data. Indeed, as shown in our review, this concern can be overcome using indirect variables that trace switching connected with patient health status (lack of effectiveness, side effects, or disease worsening). Obviously, the validity and completeness of these variables remain an open issue and the use of further data coming from innovative sources, such as social media and blogs, could be an option to identify
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ACCEPTED MANUSCRIPT switching reasons to complement claim data.79 In fact, the estimates obtained from these source could be used in sensitivity analysis on residual confounding.80-82In case-control and case-crossover studies focused on clinical outcome (10 out of 11), the restriction to patients without previous
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outcomes led to exclusion of patients with a history of lack of effectiveness or side effects; as a result, only switching not linked to these aspects was considered. However, switching reasons other than for lack of effectiveness or side effects which are connected with patient health status could
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affect risk estimation. In fact, in 42.9% of the case-control studies, a variable to identify disease worsening pre-switch was evaluated as a confounder. In case-crossover studies, while the authors
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stated that confounding for constant characteristics in intra-individuals was adjusted by design, the concern of within-person variability (eg, disease worsening or incident comorbidities) was not addressed.18,83 Multiple switching
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A further specific issue relative to drug switchability study regards multiple switching. In the cohort studies, the analytical approach more often used was as-treated analysis, so multiple switching was censored; instead, when intention-to-treat was applied (33%), no evaluation of subsequent switches
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was performed. As suggested in prescribability studies, both approaches should be applied in order
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to test the consistency of results. In case-control studies, the switching nearest to the date of outcome was considered without taking into account the number of prior switching; thus, multiple switching remained unresolved. On the contrary, in the case-crossover studies, multiple switching occurring in the two different periods considered (ie, case and control periods) was controlled by design, as periods with the same switching status do not contribute to the analysis.18 Multiple switching occurring intra-period was not considered in the included studies and remain an open methodological issue.Risk windows
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ACCEPTED MANUSCRIPT The studies considered in our review have shown different risk windows depending on the designs adopted: short window in case-crossover (0–3 months); intermediate window in the case-control (3– 6 months); and long window in cohort studies (mean follow-up: 12 months). The length of risk
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window may have an impact on the possibility of long-term outcome exploration In the study planning is important to remember that when long-term exposures are analyzed, within-person study designs may have lower precision and greater susceptibility to bias.84 Moreover, although
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regression models where time is taken into consideration, such as Cox, are advisable for outcome evaluation, we observed an improper overuse of logistic models.
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Carry over-effect
Another issue to be considered in planning switchability studies deals with the carry-over effect. This bias mainly affects the case-crossover design where the analysis is performed intra-individual and often remains disregarded. Several strategies could be attempted to control for the carry-over
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effect. For example, sensitivity analyses with different definitions of wash-out period should be conducted to evaluate possible distortions in the outcome estimation, which are associated with this
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phenomenon. In our review all case-crossover studied performed this sensitivity analysis. Strengths and limitations
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We believe that key elements highlighted from this review could be useful to evaluate quality of conduct and reporting of observational switchability studies. They should be considered to answer the questions proposed in ROBINS-I tool, focused on evaluation of the risk of bias in observational context.85 Our study focused on methodological approaches used to reduce bias and confounding specifically affecting switchability studies. Thus, other aspects linked to distortion or comparability between groups which are proper of observational studies (eg, residual confounding) are beyond the scope of our analysis.
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ACCEPTED MANUSCRIPT Our review is based on the assumption that the reporting in examined published articles reflects what was actually performed by authors. It is possible that some authors appropriately dealt with methodological items, but failed to report it. This study did not evaluate the impact of different
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options to study the switchability, which requires further validation.
Conclusion
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Our scoping review points out the relevant contribution of observational research in assessing drug
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switchability through claim data. This study also gives a picture on all clinical areas and drugs already interested by observational effectiveness research on switchability. The main strengths concern the availability of a large sample size, possibility to evaluate rare exposures and to consider multiple outcomes. On the other hand, the heterogeneity in terms of design and methodology adopted in the studies included in the review highlights the lack of a reference design that could
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address all clinical questions associated with the drug switching. Confounding associated with previous drug use and switching reasons as well as bias related to multiple switching remains the
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key factors to be considered in planning these observational studies. This scoping review allows specific strategies to be identified that might mitigate confounding and bias in observational studies
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on drug switchability. This study represents a starting point for researchers and administrators who are approaching the investigation and assessment of issues related to interchangeability of drugs.
Acknowledgments: Only public employees of the regional health authorities were involved in the conception, planning, and conduct of the study. No funding has been received for conducting the study.
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2009;25(7):1793–1805 39.
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inhibitor treatment in patients with acute coronary syndrome undergoing percutaneous coronary intervention: prevalence, predictors and short-term outcome. American heart journal. 2014;167(1):68-76.e2. 46.
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Febuxostat in a Health Plan Database. J Clin Rheumatol 2015;21: 411–418. Bagai A, Wang Y, Wang TY, et al. In-hospital switching between clopidogrel and prasugrel among patients with acute myocardial infarction treated with percutaneous coronary
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intervention: insights into contemporary practice from the national cardiovascular data registry. Circ Cardiovasc Interv 2014;7(4):585-93. Bonafede JR, Donna C, McMorrow Puneet, et al. Treatment effectiveness and treatment
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Devine ST WE, Barron J, Behm A. Acute epilepsy exacerbations in patients switched between A-rated anti-epileptic drugs. Curr Med Res Opin 2010;26(2):455-63.
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Erickson SC, Le L, Ramsey SD, et al. Clinical and pharmacy utilization outcomes with brand to generic antiepileptic switches in patients with epilepsy. Epilepsia 2011;52(7):1365-71.
52.
Degli Esposti L, Sangiorgi D, Buda S, et al. Therapy discontinuation or substitution in patients
substance:
a ‘real-world’ retrospective cohort
study.
doi:10.1136/bmjopen-2016-012003
BMJ
Open
2016;6:e012003.
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with cardiovascular disease, switching among different products of the same off-patent active
seizure-related events. Clinical pharmacology and therapeutics. 2010;88(3):347-53. Glintborg B, Ostergaard M, Krogh NS, et al. Clinical response, drug survival, and predictors
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thereof among 548 patients with psoriatic arthritis who switched tumor necrosis factor alpha inhibitor therapy: results from the Danish Nationwide DANBIO Registry. Arthritis Rheum 2013;65(5):1213-23.
Hansen RN, Campbell JD, Sullivan SD. Association between antiepileptic drug switching and
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epilepsy-related events. Epilepsy Behav 2009;15(4):481-5. 56.
Hansen RN, Nguyen HP, Sullivan SD. Bioequivalent antiepileptic drug switching and the risk
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of seizure-related events. Epilepsy Res 2013;106(1-2):237-43. Hartung DM, Middleton L, Leanne Svoboda L, et al. McGregor JC. Generic Substitution of
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Lamotrigine Among Medicaid Patients with Diverse Indications. CNS Drugs 2012; 26 (8): 707-716. 58.
Hay CRM, Palmer BP, Chalmers EA, et al. PW on behalf of UKHCDO. The incidence of factor VIII inhibitors in severe haemophilia A following a major switch from full-length to Bdomain-deleted factor VIII: a prospective cohort comparison. Haemophilia 2015;21:219–226
59.
Hellfritzsch M, Rathe J, Bjerregaard Stage T, et al. Generic switching of warfarin and risk of excessive anticoagulation: a Danish nationwide cohort study. Pharmacoepidemiol Drug Saf
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Jacobson TA, Wertz DA, Kuznik A, et al. Cardiovascular event rates in atorvastatin patients versus patients switching from atorvastatin to simvastatin. Curr Med Res Opin 2013;29(7):773-
61.
Johnson P, Becker L, Halpern R, et al. Real-world treatment of post-herpetic neuralgia with gabapentin or pregabalin. Clin Drug Investig 2013;33(1):35-44.
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62.
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81.
drugs and the risk of nonadherence. JAMA Intern Med 2013;173(3):202-8. Kesselheim AS, Bykov K, Gagne JJ, et al. Switching generic antiepileptic drug manufacturer
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63.
not linked to seizures. Neurology 2016;87:1–6. 64.
Levin P, Wei W, Miao R, et al. Therapeutically interchangeable? A study of real-world outcomes associated with switching basal insulin analogues among US patients with type 2
65.
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diabetes mellitus using electronic medical records data. Diabetes Obes Metab 2015;17:245–253 Nguyen H. Antiepileptic Drug Switching and Epilepsy-related Events in Subjects with Epilepsy: A Case-Control Analysis of Health Insurance Claims Data. [Thesis] University of
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Washington, 2013.
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rheumatoid arthritis patients switching anti-tumor necrosis factor-alpha drugs. Semin Arthritis Rheum 2012;42(2):119-26. 67.
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events
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Pharmacotherapy
2009;29(7):769-74.
doi:
10.1592/phco.29.7.769.
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Romanelli RJ, Jukes T, Segal JB. Compliance after switching from branded to generic statins. Pharmacoepidemiol Drug Saf 2014;23(10):1093-100.
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Rublee DA, Burke JP. LDL-C goal attainment in patients who remain on atorvastatin or switch
clinical practice. Postgrad Med 2010;122(2):16-24.
Schneeweiss S, Glynn RJ, Avorn J, et al. NSAID switching and short-term gastrointestinal outcome
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rofecoxib.
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injectables to oral agents for multiple sclerosis. Eur J Neurol 2016;23(4):729-36. 73.
Wei W, Zhou S, Miao R, et al. Much ado about nothing? A real-world study of patients with type 2 diabetes switching Basal insulin analogs. Advances in therapy. 2014;31(5):539-60. Xie L, Zhou S, Wei W, et al. Does pen help? A real-world outcomes study of switching from
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vial to disposable pen among insulin glargine-treated patients with type 2 diabetes mellitus. Diabetes Technol Ther 2013;15(3):230-6.
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Epilepsia 2009;50(3):493–500.
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Kwan P, Palmini A. Association between switching antiepileptic drug products and healthcare utilization: A systematic review. Epilepsy Behav 73 (2017) 166–172.
78.
Braun J, Kudrin A. Switching to biosimilar infliximab (CT-P13): Evidence of clinical safety,
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and
impact
on
public
health.
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2016;44(4):257-266.
doi:
10.1016/j.biologicals.2016.03.006. 79.
Risson V, Saini D, Bonzani I, et al. Patterns of Treatment Switching in Multiple Sclerosis
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Therapies in US Patients Active on Social Media: Application of Social Media Content Analysis to Health Outcomes Research. J Med Internet Res 2016;18(3):e62. doi: 10.2196/jmir.5409.
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treatment observational studies are reviewed. J Clin Epidemiol. 2004 Dec; 57(12):1223-31. Sebastian S, Jerry A. A review of uses of health care utilization database for epidemiologic
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81.
research on therapeutics. J Clin Epidemiol. 2005 Apr;58(4):323-37. 82.
Schneeweiss S. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Saf 2006;15(5):291-
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303
Greenland S. Confounding and exposure trends in case-crossover and case-time-control designs. Epidemiology 1996;7(3):231-9.
Nicholas JM1, Grieve AP, Gulliford MC. Within-person study designs had lower precision and
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greater susceptibility to bias because of trends in exposure than cohort and nested caseecontrol
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designs. J Clin Epidemiol. 2012 Apr;65(4):384-93. doi: 10.1016/j.jclinepi.2011.09.004. Epub 2011 Dec 23. 85.
Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in nonrandomised
studies
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BMJ
2016;355:i4919.
http://dx.doi.org/10.1136/bmj.i4919.
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Figure 1. Peculiar issues related to confounding to be considered in planning switchability study.
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Possible solutions to control the confounding both at the design and analytic stage are reported in the box right down.
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13 additional record identified through references
5500 records after duplicates removed
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7303 records identified through database searching
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Figure 2. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Flow Diagram
5444 records discarded:
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- 4863 review - 32 editorial
- 200 experimental study - 349 aim is not switchability
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24 full-text articles excluded:
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56 full-text articles assessed for eligibility
32 studies included in qualitative synthesis
- 3 cross sectional - 2 survey - 2 no control group - 3 sample size<500 - 4 outcome not included in selection criteria - 1 cost-effectiveness study - 7 aim is not switchability
- 2 intervention not included in selection criteria
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Table 1. List and characteristics of studies included in the scoping review (n=32) Author
Year Country Study design
Data FUP sources* months
Patients Drug
Outcome**
Comparator
Type of switch°
Alexopoulos45 Altan46 Bagai47 Bonafede48 Chapman49 Erickson51 Degli Esposti52 Glinborg54 Hay58 Hellfritzsch59 Jacobson60 Johnston61 Levin64 Nguyen-Khoa66 Romanelli69 Rublee70 Schneeweiss71 Spelman72 Wei73 Xie74 Zhou76 Hansen55 Hansen56 Nguyen65 Rascati68 Zachry75 Devine50 Kesselheim62 Gagne53 Kesselheim63 Polard67 Hartung57
2014 2015 2014 2016 2009 2011 2016 2013 2015 2016 2013 2013 2015 2012 2014 2010 2009 2016 2014 2013 2016 2009 2013 2013 2009 2009 2010 2013 2010 2016 2015 2012
MR C MR C C C C MR MR MR C C C C C C C MR C C C C C C C C C C C C C C
836 5,543 8,715 6,945 43,972 1,490 38,183 1,422 1,198 105,751 81,356 24,062 10,657 16,045 8,470 2,096 33,045 792 3,204 3,893 6,762 3,028 9,110 15,686 3,964 1,664 11,796 61,522 1,762 64,544 8,379 616
Clinical (E) Clinical (E) Clinical (E/S) Clinical (E) Adherence Adherence/Clinical (E) Discontinuation Clinical (E) Clinical (E/S) Clinical (S) Clinical (E) Clinical (S) Adherence/Clinical (E/S) Clinical (S) Adherence Clinical (E) Clinical (S) Clinical (E) Adherence/Clinical (E/S) Adherence/Clinical (E/S) Clinical (E) Clinical (E) Clinical (E) Clinical (E) Clinical (E) Clinical (E) Clinical (E) Persistence Clinical (E) Clinical (E) Clinical (E) Clinical (E/S)
no switcher no switcher no switcher other switcher other switcher no switcher no switcher no switcher no switcher no switcher no switcher other switcher no switcher no switcher no switcher no switcher other switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher period no switcher period no switcher period no switcher
diff. mol. entities diff. mol. entities diff. mol. entities diff. mol. entities (b) same/diff. mol. entities same mol. entities same mol. entities diff. mol. entities (b) same mol. entities (b) same mol. entities diff. mol. entities same/diff. mol. entities (b) diff. mol. entities (b) diff. mol. entities (b) same/diff. mol. entities diff. mol. entities diff. mol. entities diff. mol. entities (b/p) diff. mol. entities (b/p) same mol. entities (b/p) diff. mol. entities (p) same mol. entities same mol. entities same mol. entities diff. mol. entities same mol. entities diff. mol. entities same mol. entities same mol. entities same mol. entities same mol. entities same mol. entities
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Antiplatelet Allopurinol and febuxostat Adenosine Diphosphate Receptor Tumor Necrosis Factor Statin Phenytoin,Divalproex, Lamotrigine Simvastatin, Ramipril, Amlodipine Tumor Necrosis factor Full lenght FVIII Warfarin Atorvastatin Tumor Necrosis Factor Insulin Glargine, Insulin Detemir Tumor Necrosis Factor Statins Atorvastatin Cox-2 inhibitors IFNbeta,Glatiramer Acetate Insulin Glargine, Insulin Detemir Insulin glargine Glatiramer Acetate Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Lamotrigine
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1 3-42 6 6,12 3,6,9 3 24 24 12 22 (median) 3-36 72 12 3 6,12,>12 2-20 3,6 6 12 12 4 6 3 3 6 6 3 not specify§ <1 (21 day) <3 (85 day) 3 2
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Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Case-control Case-control Case-control Case-control Case-control Nested case-control Nested case-control Case-crossover Case-crossover Case-crossover Cohort-crossover
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Greece USA USA USA USA USA Italy Denmark UK USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA Canada USA France USA
* Data Sources: C=Claims; MR= Medical Records; ** Outcome: E=Effectiveness; S=safety; E/S= Effectiveness and Safety;§ 2 prescriptions ° diff= different; mol=molecular; b=biologic; p=peptide
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Figure 2 Switching categories according to the Anatomical Therapeutic Chemical (ATC) classification system.
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Figure 3A-D. Methodological approaches used in cohort studies (n=21) to control for bias and confounding
§:Only lack of efficacy, side effects, and disease worsening were considered. * Indirect variable: proxy variable which trace drug switching connected with patient health status. (Eg in epilepsy setting, the reporting of convulsion events prior switching is a proxy of drug switching connected with lack of efficacy.)
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Appendix
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Figure 1. Search strategy applied to PubMed database
switch [tiab] OR “switching” [tiab] OR “substitution [tiab] OR switcher* [tiab] OR change [tiab] OR changing [tiab]
2.
Drug Substitution [mesh]
3. 4.
#1 OR #2 outcome* [tiab] OR effect* [tiab] OR effectiveness [tiab] OR safety [tiab] OR “adverse effects”[tiab] OR side effect[tiab] OR compliance [tiab] OR adherence[tiab] or clinical [tiab] OR persistence [tiab] #3 AND #4 ((randomized controlled trial [pt] OR controlled clinical trial [pt] OR randomized [tiab] OR placebo [tiab] OR clinical trials as topic [mesh: noexp] OR randomly [tiab] OR trial [ti]) #5 NOT #6 (animals [mh] NOT humans [mh]) #7 NOT #8
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7. 8. 9.
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1.
ACCEPTED MANUSCRIPT What is new? •
No standard methods have been established to evaluate switchability in observational research.
•
The most frequent study design identified in this scoping review was cohort (65.6%) followed by case-control (21.9%) and self-controlled (12.5%). Case-control and case-crossover studies showed homogeneous methodological strategies to
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•
deal with bias and confounding, while heterogeneity in methodological approaches was observed for cohort studies.
Specific factors should be considered when planning switchability studies to mitigate
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•
reasons; iv) multiple switching.
This study represents a starting point for researchers and administrators who are approaching
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the investigation and assessment of issues related to interchangeability of drugs.
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•
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confounding and bias: i) the choice of comparator group; ii) previous drug use; iii) switching
S0895-4356(18)30174-4
DOI:
10.1016/j.jclinepi.2018.05.003
Reference:
JCE 9650
To appear in:
Journal of Clinical Epidemiology
Received Date: 26 February 2018 Revised Date:
18 April 2018
Accepted Date: 9 May 2018
Please cite this article as: Belleudi V, Trotta F, Vecchi S, Amato L, Addis A, Davoli M, Title: Studies on drug switchability showed heterogeneity in methodological approaches: a scoping review, Journal of Clinical Epidemiology (2018), doi: 10.1016/j.jclinepi.2018.05.003. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Title: Studies on drug switchability showed heterogeneity in methodological approaches: a
Running title: Review on observational designs on switchability Authors:
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Belleudi Valeria1 Trotta Francesco1
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Vecchi Simona1 Amato Laura1 Addis Antonio1
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Davoli Marina1 Affiliation:
Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
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1
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scoping review
Corresponding author: Valeria Belleudi
Department of Epidemiology, Lazio Regional Health Service Via Cristoforo Colombo, 112 00147 Rome, Italy Phone: +39 06 99722133 – Fax: +39 06 99722111 1
ACCEPTED MANUSCRIPT Email: [email protected] Abstract Background: Several drugs share the same therapeutic indication, including those undergoing
the evaluation of switchability through observational research.
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patent expiration. Concerns on the interchangeability are frequent in clinical practice, challenging
Aim: To conduct a scoping review of observational studies on drug switchability to identify
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methodological strategies adopted to deal with bias and confounding.
Methods: We searched PubMed, EMBASE, and Web of Science (updated 1/31/2017) to identify
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studies evaluating switchability in terms of effectiveness/safety outcomes or compliance. Three reviewers independently screened studies extracting all characteristics. Strategies to address confounding, particularly, previous drug use and switching reasons were considered. All findings were summarized in descriptive analyses.
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Results: Thirty-two studies, published in the last 10 years, met the inclusion criteria. Epilepsy, cardiovascular and rheumatology were the most frequently represented clinical areas. 75% of the
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studies reported data on effectiveness/safety outcomes. The most frequent study design was cohort (65.6%) followed by case-control (21.9%) and self-controlled (12.5%). Case-control and case-
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crossover studies showed homogeneous methodological strategies to deal with bias and confounding. Among cohort studies, the confounding associated with previous drug use was addressed introducing variables in multivariate model (47.3%) or selecting only adherent patients (14.3%). Around 30% of cohort studies did not report reasons for switching. In the remaining 70%, clinical parameters or previous occurrence of outcomes were measured to identify switching connected with lack of effectiveness or adverse events.
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ACCEPTED MANUSCRIPT Conclusion: This study represents a starting point for researchers and administrators who are approaching the investigation and assessment of issues related to interchangeability of drugs.
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Keywords: drug switchability, comparator group, previous drug use, switching reasons, multiple
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switching; scoping review
What is new?
No standard methods have been established to evaluate switchability in observational research.
•
The most frequent study design identified in this scoping review was cohort (65.6%) followed
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•
by case-control (21.9%) and self-controlled (12.5%). •
Case-control and case-crossover studies showed homogeneous methodological strategies to
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deal with bias and confounding, while heterogeneity in methodological approaches was observed for cohort studies. •
Specific factors should be considered when planning switchability studies to mitigate
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confounding and bias: i) the choice of comparator group; ii) previous drug use; iii) switching reasons; iv) multiple switching.
This study represents a starting point for researchers and administrators who are approaching
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•
the investigation and assessment of issues related to interchangeability of drugs.
Statement on prior posting/presentations: An abstract of this study has been submitted at the “IX Congress of the Italian Society for Medical Statistics and Clinical Epidemiology” to be held on 1416 September 2017 and it has been accepted as oral communication.
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Declarations of interest: none
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Word count (main text): 3279
Introduction
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Scientific progress has positively affected drug discovery and development leading to approval of different therapeutic alternatives in the same clinical setting. Furthermore, in recent years many
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blockbuster or innovative drugs have undergone patent expiration with consequent marketing authorization of generic or biosimilar versions with the same therapeutic indications.1-3 In this context, the availability of several drugs on the market sharing the same therapeutic indication raises concerns on the interchangeability, ie, the medical practice of changing one
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medicine for another that is expected to achieve the same clinical effect in a given clinical setting. More specifically, two different aspects should be considered when evaluating the drug interchangeability: drug prescribability and drug switchability.4 In the first scenario, new users are
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considered, and the prescription of one or another drug lead to the same clinical result. In the
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second scenario, only prevalent (experienced) users are considered, and this implies a change in therapy: a drug is substituted with another for various reasons, maintaining comparable efficacy and safety.
At the time of approval, for new drugs, only the prescribability should be demonstrated (whether it is a new chemical entity or a generic/biosimilar agent), while the switchability is not a requirement for granting marketing authorization and hence, a matter for pharmacovigilance and post-marketing studies. In fact, a recent document on biosimilars issued by the European Medicines Agency (EMA)
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ACCEPTED MANUSCRIPT emphasized that the switchability is not regulated by the Agency, but rather falls within the remit of the EU Member States.5 However, the lack of evidence on switchability is usually recognized by prescribers and health economists as a key limiting factor for the take up of generics/biosimilars in
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the market, reducing also the competition within therapeutic classes. Therefore, the demand for studies aimed at evaluating switchability between different drugs sharing the same indications as well as between different manufacturers of the same substance (whether they are originators or off-
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patent) is very high. Specifically, in the last case, the narrow therapeutic index of some category of drugs (eg, antiepileptic drugs), potential allergies to excipients, different shapes or colors, and a
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more or less comfortable drug intakes could be source of concern which may impact on outcomes. In the experimental context, individual bioequivalence model is needed to assess switchability, leading to develop new statistical approaches/models that take into account both the type of switch (transition, single, or multiple) and the drugs involved (generic versus branded, biosimilar versus
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originator, new generation drug versus old generation).6-15
Despite crossover trials being identified as the gold standard to evaluate switchability, the following
•
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methodological issues are still present:16
heterogeneity in variabilities between the test and reference product (within individual
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effects);6-9 •
different strategies to evaluate multiple alternation/switching;10
•
difficulties in defining independent risk windows to evaluate the association between drug and outcome (carry-over effect).14
The evaluation of switchability through experimental design brings together classical concerns related to the generalizability of findings, the impossibility to study long-term clinical outcomes, and limited follow-up period. Therefore, there is a high demand for observational studies based on
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ACCEPTED MANUSCRIPT routinely collected data. However, in the observational context, the methodological approaches to evaluate prescribability (in naïve patients) are well established,17-19 while little is known about designs for assessing drug switchability in real practice. As the major concerns in observational
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context are related to confounding, when approaching evaluation of switchability, peculiar issues should be considered in planning the phase of the study such as the choice of comparator group, reasons for switching, and time in treatment before switching. Moreover, possible distortions due to
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multiple switching and carry-over effect should be adequately addressed.
The aim of this study was to conduct a scoping review on drug switchability to synthesize research
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evidence available in the scientific literature describing the use and reporting of the adopted methodological strategies to deal with bias and confounding.
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Methods
The review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.20
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Eligibility criteria and data sources
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All observational studies based on electronic medical records or those that claimed data assessing switchability of drugs with a comparator group and evaluated effectiveness/safety or compliance outcomes were eligible. Only studies conducted on human subjects published in English after the year 2000 were included. We excluded studies on device switching or those conducted in an ophthalmology setting, and studies with sample size ≤500 patients. Reviews, case reports, commentaries, cross-sectional studies, letters to the editor, and short communications were also excluded.
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ACCEPTED MANUSCRIPT We performed a systematic search using electronic databases including MEDLINE (via PubMed) (from January 1, 2000 to January 31, 2017); EMBASE (Elsevier, EMBASE.com) (from January 1, 2000 to January 31, 2017); and Web of Science (Thomson Reuters) (from January 1, 2006 to
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January 31, 2017). For each database, we developed a specific search strategy combining a specific subject heading (such as MeSH terms) and free terms (see Appendix 1 Figure 1 for details). We also
bibliography. Selection of the studies and data extraction
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conducted additional searches screening review articles and relevant editorials to identify useful
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Three reviewers independently screened the titles and abstracts obtained through the search strategy. All potentially eligible studies were obtained as full articles and were evaluated in-depth. In doubtful or controversial cases, all identified discrepancies were discussed until a consensus was reached on all items.
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A number of variables related to study characteristics and switching were extracted using a predefined template: publication year, country of data source, study design, data source, characteristics
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of the population (condition, sample size), investigated drugs, comparator group, outcomes, risk window/follow up, statistical methods/approaches, covariates for risk adjustment, and results. More
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specifically, the previous drug use was retrieved as: i) number of prescriptions; ii) duration of treatment; and iii) adherence/persistence. Switching reasons were extracted as clinical or nonclinical.
In particular, efforts to deal with previous drug use, switching reasons, and multiple switching were also collected (for further details see Figure 1). As the aim of the study focused on methodological issues (and not pooling of data), we did not conduct a risk of bias evaluation of each study included in this scoping review.
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ACCEPTED MANUSCRIPT We extracted and tabulated the study information and used this to perform a descriptive analysis.
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Results A total of 7,316 articles were identified from the literature search. After excluding duplicates, a total of 5,500 citations remained. The PRISMA flow diagram (Figure 2) reports the number of citations screened based on titles and abstracts and the reasons for the exclusion, leading to a sample of 56
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articles that were evaluated in full-text form. Then, 24 out of 56 studies were excluded as they did
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not satisfy our exclusion/inclusion criteria.21-44 A final sample of 32 studies evaluating drug switchability were judged eligible for inclusion in our review (see appendix Table 1 for full details). 45-76
Table 1 shows the main characteristics of the studies included in this review. The 32 studies were
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published between 2009 and 2016 (65.6% in the last 5 years), and the majority were carried out in USA (81.3%). The sample size ranged from 616 to 105,751 (median: 7,662) patients and data were mainly gathered from claims database (87.5%). The most frequent study design was cohort (65.6%)
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followed by case-control (21.9%) and self-controlled (12.5%). In cohort studies, two different comparator groups were considered: no switcher (81%) or another switching category (19%). For
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the other designs, only no-switcher comparator was considered. The clinical areas most frequently studied were epilepsy (37.5%), cardiovascular (25.0%), and rheumatology (12.5%). In particular, there were a high number of subjects to be involved in future meta-analyses in these areas. Clinical outcome was evaluated in 75% (28 out of 32) of the studies, (effectiveness, 18/28; safety, 4/28; or both 6/28), while only 12.5% of the studies (8/28) focused on adherence.
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ACCEPTED MANUSCRIPT Different switching categories were identified according to the Anatomical Therapeutic Chemical (ATC) classification system (Figure 2). In 40.6% of the studies, the switching has been analyzed considering the same substance was produced by different manufacturers (eg, originators and
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related off-patented version, ATC V level) and in 34.4%, the switching occurred among different substances belonging to the same therapeutic category (ATC IV level); in the 9% of the cases the switch occurred toward a combination of ATC IV and V level. Indeed, in 12.5% of the cases,
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switching was explored among drugs sharing the same indication but leading to different therapeutic categories. Twenty-two studies evaluating switchability involved small molecules,
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while the remaining considered biologics or/and peptides.
Case-control and case-crossover studies were based on claim data and showed homogeneous methodological strategies to deal with bias and confounding. In both study designs, to control for previous drug use and switching reasons, only adherent/persistent patients without previous
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outcome in two possible risk windows (1–3 or 3–6 months) were selected. The selection of patients without prior outcomes led to the exclusion of patients with a history of lack of effectiveness or side effects; thus, no switching due to these reasons can be included. Moreover, in the case-control
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design, matching and multivariate logistic models were used to control the effect of other potential reasons for switching; for example, in three studies, a variable to identify disease worsening pre-
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switch was evaluated as a confounder. In the case control studies, the switching nearest to the date of outcome was considered without any evaluation of prior switches. On the contrary, in the casecrossover studies, subjects with multiple switching (one in the case period and the other in the control period) did not contribute to the analysis. For cohort studies, heterogeneity in methodological approaches was observed (Figure 3A-D). The most frequently used adjustment technique was multivariate regression (52.4%), followed by propensity score matching (33.3%) (Figure 3A). Median duration of follow-up to evaluate outcomes
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ACCEPTED MANUSCRIPT was 12 months. However, only 57.4% of the studies used a regression model that took into account time post switch (person-time approach) to evaluate the association between switching and outcomes, rather than defining the outcome as a binary variable. The as-treated analysis (multiple
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switches were censored) was adopted in 57.1% of the studies, while in 33.3%, the analysis was intention-to-treat (Figure 3B). The majority of studies considered previous drug use as a key factor for the adjustment (81.0% of the studies quantified exposure to the drug from which patients were
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switching). Confounding associated with previous drugs was addressed in 14.3% of the cases by selecting adherent patients, while 47.6% of the studies considered previous drug use as a
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confounder in terms of the number of prescriptions, duration of treatment, or adherence/persistence (Figure 3C). In 19% of the cases, both inclusion criteria and adjustment were applied to control for previous drug use.
Around 30% of cohort studies did not report any information that allow direct or indirect reasons
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for switching. In the remaining studies, indirect variables (38.1%) or clinical parameters (33.3%) were used to identify switching connected with lack of effectiveness or adverse events (Figure 3D). When no switcher comparator was used, the index date (of no switcher) was defined using the first
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(7%), the last (43%), or a random (43%) date among all dispensing dates available in the enrolment
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period or considering an exposure time similar to the switcher (7%) (data not shown).
Discussion
Our scoping review shows that the evidence on observational studies evaluating switchability of drugs is recent, related to specific clinical areas and focused on effectiveness outcomes. Similar percentages of studies evaluated switching between the same substance (produced by different
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ACCEPTED MANUSCRIPT manufactures) or between different substances leading to the same therapeutic category. The most used study design is the cohort, although high heterogeneity was observed in terms of the methodological approaches adopted. On the contrary, the few studies with case-control and case-
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crossover designs used more homogeneous methodologies. Methodological approaches
Our review shows that different methodological approaches were attempted also when investigating
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similar research questions within the same clinical setting. For example, cohort studies evaluating switchability between anti-TNF in terms of effectiveness differed for the choice of comparator
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group; similar variability was observed in the case of switchability between statins using adherence as outcome. In addition, different study designs were adopted within the same clinical setting, such as the area of epilepsy, where cohort, case-control, and self-controlled design were used to assess switchability of antiepileptics in terms of clinical outcomes (seizure- or epilepsy-related
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hospitalization). This pattern has been also observed in a recent study focused on epilepsy.77 In general, several open issues exist for observational studies assessing switchability.
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Comparator groups and previous drug use
Two kinds of comparisons are possible: switcher versus non-switcher or switcher versus other
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switchers. In the case of non-switcher comparator group, a further complexity pertains to the selection of the index date for non-switchers. Our review highlighted two possible solutions: i) selecting one date among all prescription dates, usually randomly; ii) selecting the date on the basis of time to switch observed in the switcher group. Using another switching category as a comparator group has been observed only for cohort studies. The choice of comparator directly affected the validity of study results and must be carefully evaluated, taking into account the research question, informational needs of the stakeholder and 11
ACCEPTED MANUSCRIPT strategy to minimize bias.18 In several cases, to compare population with similar characteristics, the choice of another group of switchers is preferable (eg, switch from DMARD to biologic1 or biologic2). Moreover, in the case of comparison respect “no-switcher”, sensitivity analyses on
are under reported in the studies included in this review.
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consistence of result varying index date are suggested; however, this concern and its implications
Previous drug use is a key element to balance groups and it can be disentangled by a restriction, ie,
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including only compliant patients, matching or testing a specific variable (eg, number/type of prescriptions, time in treatment, adherence) as confounders. Given the complexity of medication-
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taking behavior, most of the studies considered more than one variable to define drug user profile. Switching reasons
Another important element to be taken into account in comparative switchability studies is the nonrandom occurrence of switching. As described by Braun and Kudrin, the switch can be related to
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different clinical reasons (ie, lack of effectiveness, side effect, disease worsening), compliance, or organizational aspects (ie, costs and formulary considerations).78 These factors are likely to vary
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across types of switches (ie, same or different molecular entities, small molecules or biosimilars), clinical contexts, and countries (ie, regulatory decision on automatic substitution) as well as how
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their impact on confounding. In our review, about 30% of the cohort studies did not report any information that allows the reasons for switching to be identified. In most of the cases, the authors highlighted the impossibility to retrieve this information from administrative data. Indeed, as shown in our review, this concern can be overcome using indirect variables that trace switching connected with patient health status (lack of effectiveness, side effects, or disease worsening). Obviously, the validity and completeness of these variables remain an open issue and the use of further data coming from innovative sources, such as social media and blogs, could be an option to identify
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ACCEPTED MANUSCRIPT switching reasons to complement claim data.79 In fact, the estimates obtained from these source could be used in sensitivity analysis on residual confounding.80-82In case-control and case-crossover studies focused on clinical outcome (10 out of 11), the restriction to patients without previous
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outcomes led to exclusion of patients with a history of lack of effectiveness or side effects; as a result, only switching not linked to these aspects was considered. However, switching reasons other than for lack of effectiveness or side effects which are connected with patient health status could
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affect risk estimation. In fact, in 42.9% of the case-control studies, a variable to identify disease worsening pre-switch was evaluated as a confounder. In case-crossover studies, while the authors
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stated that confounding for constant characteristics in intra-individuals was adjusted by design, the concern of within-person variability (eg, disease worsening or incident comorbidities) was not addressed.18,83 Multiple switching
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A further specific issue relative to drug switchability study regards multiple switching. In the cohort studies, the analytical approach more often used was as-treated analysis, so multiple switching was censored; instead, when intention-to-treat was applied (33%), no evaluation of subsequent switches
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was performed. As suggested in prescribability studies, both approaches should be applied in order
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to test the consistency of results. In case-control studies, the switching nearest to the date of outcome was considered without taking into account the number of prior switching; thus, multiple switching remained unresolved. On the contrary, in the case-crossover studies, multiple switching occurring in the two different periods considered (ie, case and control periods) was controlled by design, as periods with the same switching status do not contribute to the analysis.18 Multiple switching occurring intra-period was not considered in the included studies and remain an open methodological issue.Risk windows
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ACCEPTED MANUSCRIPT The studies considered in our review have shown different risk windows depending on the designs adopted: short window in case-crossover (0–3 months); intermediate window in the case-control (3– 6 months); and long window in cohort studies (mean follow-up: 12 months). The length of risk
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window may have an impact on the possibility of long-term outcome exploration In the study planning is important to remember that when long-term exposures are analyzed, within-person study designs may have lower precision and greater susceptibility to bias.84 Moreover, although
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regression models where time is taken into consideration, such as Cox, are advisable for outcome evaluation, we observed an improper overuse of logistic models.
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Carry over-effect
Another issue to be considered in planning switchability studies deals with the carry-over effect. This bias mainly affects the case-crossover design where the analysis is performed intra-individual and often remains disregarded. Several strategies could be attempted to control for the carry-over
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effect. For example, sensitivity analyses with different definitions of wash-out period should be conducted to evaluate possible distortions in the outcome estimation, which are associated with this
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phenomenon. In our review all case-crossover studied performed this sensitivity analysis. Strengths and limitations
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We believe that key elements highlighted from this review could be useful to evaluate quality of conduct and reporting of observational switchability studies. They should be considered to answer the questions proposed in ROBINS-I tool, focused on evaluation of the risk of bias in observational context.85 Our study focused on methodological approaches used to reduce bias and confounding specifically affecting switchability studies. Thus, other aspects linked to distortion or comparability between groups which are proper of observational studies (eg, residual confounding) are beyond the scope of our analysis.
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ACCEPTED MANUSCRIPT Our review is based on the assumption that the reporting in examined published articles reflects what was actually performed by authors. It is possible that some authors appropriately dealt with methodological items, but failed to report it. This study did not evaluate the impact of different
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options to study the switchability, which requires further validation.
Conclusion
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Our scoping review points out the relevant contribution of observational research in assessing drug
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switchability through claim data. This study also gives a picture on all clinical areas and drugs already interested by observational effectiveness research on switchability. The main strengths concern the availability of a large sample size, possibility to evaluate rare exposures and to consider multiple outcomes. On the other hand, the heterogeneity in terms of design and methodology adopted in the studies included in the review highlights the lack of a reference design that could
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address all clinical questions associated with the drug switching. Confounding associated with previous drug use and switching reasons as well as bias related to multiple switching remains the
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key factors to be considered in planning these observational studies. This scoping review allows specific strategies to be identified that might mitigate confounding and bias in observational studies
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on drug switchability. This study represents a starting point for researchers and administrators who are approaching the investigation and assessment of issues related to interchangeability of drugs.
Acknowledgments: Only public employees of the regional health authorities were involved in the conception, planning, and conduct of the study. No funding has been received for conducting the study.
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IMS Institute for Healthcare Informatics. Delivering on the Potential of Biosimilar Medicines.
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1.
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Figure 1. Peculiar issues related to confounding to be considered in planning switchability study.
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Possible solutions to control the confounding both at the design and analytic stage are reported in the box right down.
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13 additional record identified through references
5500 records after duplicates removed
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7303 records identified through database searching
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Figure 2. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Flow Diagram
5444 records discarded:
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- 4863 review - 32 editorial
- 200 experimental study - 349 aim is not switchability
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24 full-text articles excluded:
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56 full-text articles assessed for eligibility
32 studies included in qualitative synthesis
- 3 cross sectional - 2 survey - 2 no control group - 3 sample size<500 - 4 outcome not included in selection criteria - 1 cost-effectiveness study - 7 aim is not switchability
- 2 intervention not included in selection criteria
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Table 1. List and characteristics of studies included in the scoping review (n=32) Author
Year Country Study design
Data FUP sources* months
Patients Drug
Outcome**
Comparator
Type of switch°
Alexopoulos45 Altan46 Bagai47 Bonafede48 Chapman49 Erickson51 Degli Esposti52 Glinborg54 Hay58 Hellfritzsch59 Jacobson60 Johnston61 Levin64 Nguyen-Khoa66 Romanelli69 Rublee70 Schneeweiss71 Spelman72 Wei73 Xie74 Zhou76 Hansen55 Hansen56 Nguyen65 Rascati68 Zachry75 Devine50 Kesselheim62 Gagne53 Kesselheim63 Polard67 Hartung57
2014 2015 2014 2016 2009 2011 2016 2013 2015 2016 2013 2013 2015 2012 2014 2010 2009 2016 2014 2013 2016 2009 2013 2013 2009 2009 2010 2013 2010 2016 2015 2012
MR C MR C C C C MR MR MR C C C C C C C MR C C C C C C C C C C C C C C
836 5,543 8,715 6,945 43,972 1,490 38,183 1,422 1,198 105,751 81,356 24,062 10,657 16,045 8,470 2,096 33,045 792 3,204 3,893 6,762 3,028 9,110 15,686 3,964 1,664 11,796 61,522 1,762 64,544 8,379 616
Clinical (E) Clinical (E) Clinical (E/S) Clinical (E) Adherence Adherence/Clinical (E) Discontinuation Clinical (E) Clinical (E/S) Clinical (S) Clinical (E) Clinical (S) Adherence/Clinical (E/S) Clinical (S) Adherence Clinical (E) Clinical (S) Clinical (E) Adherence/Clinical (E/S) Adherence/Clinical (E/S) Clinical (E) Clinical (E) Clinical (E) Clinical (E) Clinical (E) Clinical (E) Clinical (E) Persistence Clinical (E) Clinical (E) Clinical (E) Clinical (E/S)
no switcher no switcher no switcher other switcher other switcher no switcher no switcher no switcher no switcher no switcher no switcher other switcher no switcher no switcher no switcher no switcher other switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher no switcher period no switcher period no switcher period no switcher
diff. mol. entities diff. mol. entities diff. mol. entities diff. mol. entities (b) same/diff. mol. entities same mol. entities same mol. entities diff. mol. entities (b) same mol. entities (b) same mol. entities diff. mol. entities same/diff. mol. entities (b) diff. mol. entities (b) diff. mol. entities (b) same/diff. mol. entities diff. mol. entities diff. mol. entities diff. mol. entities (b/p) diff. mol. entities (b/p) same mol. entities (b/p) diff. mol. entities (p) same mol. entities same mol. entities same mol. entities diff. mol. entities same mol. entities diff. mol. entities same mol. entities same mol. entities same mol. entities same mol. entities same mol. entities
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Antiplatelet Allopurinol and febuxostat Adenosine Diphosphate Receptor Tumor Necrosis Factor Statin Phenytoin,Divalproex, Lamotrigine Simvastatin, Ramipril, Amlodipine Tumor Necrosis factor Full lenght FVIII Warfarin Atorvastatin Tumor Necrosis Factor Insulin Glargine, Insulin Detemir Tumor Necrosis Factor Statins Atorvastatin Cox-2 inhibitors IFNbeta,Glatiramer Acetate Insulin Glargine, Insulin Detemir Insulin glargine Glatiramer Acetate Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Antiepileptic Lamotrigine
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1 3-42 6 6,12 3,6,9 3 24 24 12 22 (median) 3-36 72 12 3 6,12,>12 2-20 3,6 6 12 12 4 6 3 3 6 6 3 not specify§ <1 (21 day) <3 (85 day) 3 2
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Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Cohort Case-control Case-control Case-control Case-control Case-control Nested case-control Nested case-control Case-crossover Case-crossover Case-crossover Cohort-crossover
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Greece USA USA USA USA USA Italy Denmark UK USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA Canada USA France USA
* Data Sources: C=Claims; MR= Medical Records; ** Outcome: E=Effectiveness; S=safety; E/S= Effectiveness and Safety;§ 2 prescriptions ° diff= different; mol=molecular; b=biologic; p=peptide
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Figure 2 Switching categories according to the Anatomical Therapeutic Chemical (ATC) classification system.
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Figure 3A-D. Methodological approaches used in cohort studies (n=21) to control for bias and confounding
§:Only lack of efficacy, side effects, and disease worsening were considered. * Indirect variable: proxy variable which trace drug switching connected with patient health status. (Eg in epilepsy setting, the reporting of convulsion events prior switching is a proxy of drug switching connected with lack of efficacy.)
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Appendix
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Figure 1. Search strategy applied to PubMed database
switch [tiab] OR “switching” [tiab] OR “substitution [tiab] OR switcher* [tiab] OR change [tiab] OR changing [tiab]
2.
Drug Substitution [mesh]
3. 4.
#1 OR #2 outcome* [tiab] OR effect* [tiab] OR effectiveness [tiab] OR safety [tiab] OR “adverse effects”[tiab] OR side effect[tiab] OR compliance [tiab] OR adherence[tiab] or clinical [tiab] OR persistence [tiab] #3 AND #4 ((randomized controlled trial [pt] OR controlled clinical trial [pt] OR randomized [tiab] OR placebo [tiab] OR clinical trials as topic [mesh: noexp] OR randomly [tiab] OR trial [ti]) #5 NOT #6 (animals [mh] NOT humans [mh]) #7 NOT #8
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No standard methods have been established to evaluate switchability in observational research.
•
The most frequent study design identified in this scoping review was cohort (65.6%) followed by case-control (21.9%) and self-controlled (12.5%). Case-control and case-crossover studies showed homogeneous methodological strategies to
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deal with bias and confounding, while heterogeneity in methodological approaches was observed for cohort studies.
Specific factors should be considered when planning switchability studies to mitigate
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reasons; iv) multiple switching.
This study represents a starting point for researchers and administrators who are approaching
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the investigation and assessment of issues related to interchangeability of drugs.
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confounding and bias: i) the choice of comparator group; ii) previous drug use; iii) switching