Recommendations for the Calculation of Transition Probabilities in Markov Cohort Models: A Targeted Literature Review

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and, counterintuitively, modellers can create ‘false’ confidence in PSA results by including more parameters. PRM88 Modelling the Cost-Effectiveness of Treatment Strategies in Chronic Lymphocytic Leukaemia, Follicular Lymphoma and Multiple Myeloma: A Review and Critical Appraisal of the Published Literature Kankeu Tchewonpi H1, Manca A2 1University of York, York, UK, 2University of York, Heslingon, York, UK

Objectives: To review existing cost-effectiveness analysis (CEA) models published in chronic lymphocytic leukaemia (CLL), follicular lymphoma (FL) and multiple myeloma (MM), with a view to critically appraise their methodological quality and highlight areas for improvement.  Methods: Peer-reviewed and grey literature identified in MEDLINE, EMBASE and Google Scholar (cut-off date March 2016), published in English. No restrictions on the treatment options included or the patients’ characteristics. When possible, studies were assessed against the ISPOR-SMDM modelling good research practice guideline recommendations.  Results: We identified 33 full-length CEAs reports (10 CLL, 8 FL, 15 MM). Most analyses used cohort models (27% CLL, 21% FL and 40% MM) - typically informed by aggregated data from the literature (67% CLL, 71% FL and 62% MM) - to represent disease prognoses and treatment pathways. Four CEAs (1 CLL, 1 FL and 2 MM) used discrete event simulation (DES) models, accounting for the effects of upstream/downstream decisions on the costs and outcomes of the primary object of the evaluation. Very few analyses (21%) used real-world data (alone or in combination with RCTs) to populate their models. The quality of these CEAs was heterogeneous when assessed against the ISPOR-SMDM modelling good research practice recommendations. Generally, the decision problem, target population, interventions/comparators and health outcomes were well defined, but some important features like the perspective of the analysis (12%), the time horizon (18%) and the conceptual representation of the decision problem (52%) were not systematically reported or provided.  Conclusions: Treatment pathways in CLL, FL and MM are often individualised and difficult to model, given the heterogeneous events/outcomes profiles observed throughout the patients prognoses. CEA studies reporting quality needs to improve. Real-world long-term individual patient-level data is fundamental for modelling the nuanced sequential nature of treatment and therapy switch decisions in CLL, FL and MM and the outcomes that follow. PRM89 Recommendations for the Calculation of Transition Probabilities in Markov Cohort Models: A Targeted Literature Review Olariu E1, Cadwell K1, Fox D1, Hancock E1, Trueman D1, Ratcliffe M1, Grieve R2, ChevrouSeverac H3 1PHMR Ltd, London, UK, 2London School of Hygiene and Tropical Medicine, London, UK, 3Takeda Pharmaceuticals International AG, Glattpark-Opfikon, Switzerland

Objectives: Although Markov cohort models represent one of the most common forms of decision-analytic models used in health technology assessment (HTA), correct implementation of such models requires reliable estimation of transition probabilities, often in the context of incomplete datasets (e.g. limited follow-up or not containing all relevant comparators). This study sought to identify formal recommendations, consensus statements or guidelines which detailed how such transition probability matrices should be estimated.  Methods: A literature review was performed to identify relevant publications in the following databases: Medline, Embase, The Cochrane Library and Pubmed. Electronic searches were supplemented by hand-searches of HTA websites in Australia, Belgium, Canada, France, Germany, Ireland, Norway, Portugal, Sweden and the UK. One reviewer assessed studies for eligibility.  Results: Of the 1,931 citations identified, no studies met the inclusion criteria for full-text review. The review of the electronic searches found no guidelines on transition probabilities in Markov models. Hand-searching the websites of HTA agencies identified ten guidelines on economic evaluations (Australia, Belgium, Canada, France, Germany, Ireland, Portugal, Sweden, UK). All provided general guidance on how to develop economic models. Regarding transition probabilities, it was stated that the preferred method of presentation is in a matrix, that information should be given on the type of distribution they follow, as well as whether they are constant or vary over time or according to age. French and UK guidelines state that parameters for models should be obtained from a systematic search process. ISPOR taskforce guidance discusses the use of rates and probabilities, and provides best practice guidance on reporting.  Conclusions: There is limited formal guidance available on the estimation of transition probabilities. Given the increasing importance of cost-effectiveness analysis in the decision-making process, additional guidance may inform a more consistent approach to decision-analytic modelling. PRM90 To What Extent Does the Published Economic Analyses of Treatments for Opioid Use Disorder Capture its Chronic, Relapsing Nature and its Impact on Society? Cranmer H1, Ronquest NA2, Barnes A1, Nadipelli VR2, Akehurst R1 1BresMed Health Solutions, Sheffield, UK, 2Indivior Inc., Richmond, VA, USA

Objectives: Opioid use disorder (OUD) is a global problem with enormous economic, personal and public health consequences. Current estimates suggest that there are approximately 32 million illicit opioid users globally, the majority of whom misuse diamorphine (heroin) and non-prescription opioid drugs. We conducted a systematic literature review to identify evidence of the economic impact of OUD treatments. The aim was to assess to what extent the current evidence captures the chronic, relapsing nature of opioid dependence and its impact on society.  Methods: We searched global electronic databases, key international health technology assessment websites, conference proceedings and reference lists from 1995-2015. Comparisons of costs and/or cost-effectiveness between two or more pharmacological maintenance interventions for OUD were included. Studies were

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not excluded by geographical region or language. Data quality was appraised using published checklists.  Results: From 765 records, 39 studies met the inclusion criteria, including cost-effectiveness, cost-utility, cost-benefit and cost-minimisation analyses. Of these, only 10 considered a time horizon beyond 10 years, and only one considered retreatment for patients after relapse, despite the chronic nature of OUD. Twenty studies investigated a societal perspective: specifically, incarceration (n= 12), cost of crime to society and the victim (n= 14), diversion (n= 1), and productivity and workforce (n= 5). The majority of economic studies of OUD published after 2005 captured societal costs.  Conclusions: This review identified an increasing interest towards understanding the societal impact of OUD treatments. However, the evidence to assess the long-term effects of the interventions is scarce. Only a few studies captured the long-term effect of treatment, and only one study was found to capture the relapsing nature of the disease. Further studies to capture long-term social factors are needed to quantify the full economic impact of OUD treatments. PRM91 Review of Health Economic Models for Antibiotics Virhage M1, Polyzoi M1, Geale K1, Geale K2, Corcoran K1, Anell B1 International, Stockholm, Sweden, 2Umeå University, Umeå, Sweden

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Objectives: Critically appraise published health economic models evaluating treatments of bacteria resistant to antibiotics, focusing on methicillin-resistant Staphylococcus aureus (MRSA) and extended spectrum beta-lactamase (ESBL)producing Enterobacteriaceae.  Methods: A structured literature review was conducted in Embase, identifying relevant publications by combining search facets for MRSA or ESBL, economic models, and antibiotics, excluding publications including the terms “animal” or “farm”. All search facets were search in the abstract and title, resulting in 40 publications. Two reviewers, blinded to the other’s initial evaluation, evaluated each of the titles and abstracts and found that 25 were relevant for a full review.  Results: The absolute majority of the identified studies presented costeffectiveness models with a decision-analytic approach and a time horizon ranging from two to four weeks. The site of infection studied was dominated by nosocomial pneumonia as well as skin and soft tissue infections caused by MRSA. Only one model concerned gram-negative pathogens. No model was identified studying ESBL as the cause of infection. The most common antibiotic agents modelled were linezolid, vancomycin, daptomycin or their combination with a median of two treatment lines modelled. One model studies the cost-effectiveness of carbapenems. Sources used for informing antibiotic efficacy data were primarily published literature, clinical trials or clinical expert opinion. The most common outcome measures modelled were direct medical costs and resource utilization as well as efficacy measured by treatment success or antibacterial usage estimates. Four models (16%) included quality-adjusted life years (QALYs) as outcome measure.  Conclusions: The cost-effectiveness of linezolid and vancomycin for treatment of MRSA has been well-studied in various types of infections. There is a need for further costeffectiveness and cost-benefit studies on antibiotic failure in more than two treatment lines, especially in carbapenem treatment of infections caused by ESBL, as these pose a significant resistance threat today. PRM92 Automatic Extraction and Classification of Patients’ Smoking Status from Free Text Using Natural Language Processing Caccamisi A1, JorgensenJörgensen L2, Dalianis H3, Rosenlund M2 1Karolinska Institute, Stockholm, Sweden, 2IMS Health, Stockholm, Sweden, 3Stockholm University, Stockholm, Sweden

Objectives: To develop a machine learning algorithm for automatic classification of smoking status (smoker, ex-smoker, non-smoker and unknown status) in EMRs, and validate the predictive accuracy compared to a rule-based method. Smoking is a leading cause of death worldwide and may introduce confounding in research based on real world data (RWD). Information on smoking is often documented in free text fields in Electronic Medical Records (EMRs), but structured RWD on smoking is sparse.  Methods: 32 predictive models were trained with the Weka machine learning suite, tweaking sentence frequency, classifier type, tokenization and attribute selection using a database of 85,000 classified sentences. The models were evaluated using F-Score and Accuracy based on out-of-sample test data including 8,500 sentences. The error weight matrix was used to select the best model, assigning a weight to each type of misclassification and applying it to the models confusion matrices.  Results: The best performing model was based on the Support Vector Machine (SVM) Sequential Minimal Optimization (SMO) classifier using a polynomial kernel with parameter C equal to 6 and a combination of unigrams and bigrams as tokens. Sentence frequency and attributes selection did not improve model performance. SMO achieved 98.25% accuracy and 0.982 F-Score versus 79.32% and 0.756, respectively, for the rule-based model.  Conclusions: A model using machine learning algorithms to automatically classify patients smoking status was successfully developed. This algorithm would enable automatic assessment of smoking status directly from EMRs, obviating the need to extract complete case notes and manual classification. PRM93 Potential Efficacy of Lomitapide, A MTP (Microsomal Triglyceride Transfer Protein) Inhibitor, on Survival in Homozygous Familial Hypercholesterolaemia (HOFH): Results of an Event Modelling Analysis Leipold R1, Raal F2, Ishak J1, Phillips H3, Deanfield J4 1Evidera, Bethesda, MD, USA, 2University of the Witwatersrand, Johannesburg, South Africa, 3Aegerion Pharmaceuticals Ltd, Berkshire, UK, 4University College London, London, UK

Objectives: Public health burdens of rare diseases are usually unknown due to small patient numbers and limited outcomes data. We aimed to quantify mortality gaps in HoFH, a rare genetic disorder characterised by extremely elevated lowdensity lipoprotein cholesterol (LDL-C) levels and premature mortality from accelerated atherosclerosis. A modelling approach was employed to estimate the expected