Determining And Identifying Costs For Economic Evaluations: Guidance From Canada

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Matching adjusted indirect comparisons (MAIC) and simulated treatment comparisons (STC) produce comparisons between treatments in different trials after adjusting for imbalances between their populations. These methods overcome issues like disjointed evidence networks and heterogeneity in network meta-analyses, and can produce comparative evidence where it may otherwise be impossible. While MAIC and STC are conceptually similar, they differ in the way they adjust for population differences. The former uses weights to match on all available baseline characteristics and the latter predicts outcomes in the comparator’s population using equations. Matching on all baseline characteristics is intuitively appealing, but when there are large differences between populations or numerous, possibly correlated, variables to match, the derived weights can have an unbalanced distribution, giving a relatively small subset of patients the majority of the weight. This can lead to spurious findings and loss of precision. In STC, predictive equations can be very informative as they identify determinants of the outcome, which represent the variables that that may confound comparisons. The equations may also be useful beyond the STC for use in economic modelling. Prediction of the adjusted results for non-linear outcomes (like time-to-event endpoints) requires additional analytical steps, however. A hybrid approach that leverages the strengths of the two approaches can overcome these challenges. A predictive equation is developed as in STC to identify potential confounders among the all available variables. Balancing weights are then derived to match on these confounding variables, and used to derive adjusted outcomes via reweighting instead of prediction. The robustness of results to omitted baseline characteristics can be assessed via sensitivity analysis. Despite the reduced matching list, there could remain substantial loss of effective sample size. In such cases, tone can explore whether some precision can be gained via prediction by reverting to an STC approach. PRM197 A Role For Pro-Based Patient Treatment Simulators In Observing Physician Treatment Of Pain And Discomfort Hu G, Koganov M KMK Consulting Inc., Morristown, NJ, USA

There has been much research about the use of patient-reported outcomes (PROs) in clinical practice. However, it has been difficult to quantify the impact of PROs on clinical practice due to methodological concerns as well as heterogeneity of study designs on the subject (Valderas 2008). This paper demonstrates the value of developing a patient simulator to better observe the impact of PROs- and in particular, HRQoL measures- on real-world treatment decisions. Although questionnaires can accurately capture physician behaviour in clinical practice (Rethans 1987), they are limited in the amount of information they can provide about a patient without risking respondent fatigue. Simulation combines many of the benefits of a questionnaire with those of observing a physician in clinical practice, providing a robust sample size of both doctors and simulated patients, rich information, and an engaging task for respondents. In 2015, we began creating a patient simulator designed to mimic the richness and detail characteristic of a typical doctor’s visit. The simulation is designed to study treatment patterns of several conditions- in this publication, we focus on osteoarthritis, an area with significant unmet need (Sofat 2014) and with well-established PRO measures (most notably the WOMAC pain score) (Davies 1999). Within the simulation, doctors are presented with patient profiles containing a basic profile and photo with demographic information, medical and treatment history, radiographic scans, and patient complaints based on items contained in the WOMAC evaluation. Beyond osteoarthritis, this design helps researchers understand how management of conditions involving pain and discomfort is influenced by use of PRO measures. By observing how doctors treat various patient cases presented in the simulation, we can determine how specific measures impact treatment decisions and goals. These decisions can then be used to evaluate the impact of PROs on compliance with best practices, as well as patient outcomes. PRM198 Improving The Quality Of Care In Paediatric Hospital Environment By Optimizing Budget Allocation Between Investment In Vaccination And Future Institutional Development Dort T1, Schecroun

N2, Standaert

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PRM199 Applications Of The Fda Guidance On Common Issues In Drug Development For Rare Diseases Pan YI UBC: An Express Scripts Company, Dorval, QC, Canada

In August 2015, the US Food and Drug Administration (FDA) released a draft document to address some challenges in developing drugs for rare diseases. The following practical suggestions can be drawn from the FDA guidance. First, to adequately describe and understand the rare disease’s natural history, a disease registry should be planned strategically as part of the drug development process. One main objective of the registry would be gaining comprehensive knowledge on the disease over its clinical course and identifying the target population and important subgroups. In addition, a disease registry provides an unique opportunity to longitudinally document patient-reported outcomes such as health-related quality of life and health care utilization, which are gaining importance in post-marketing evaluations. Next, the FDA encourages the study of the disease’s pathophysiology as well as the mechanism of action of the investigative compound as fully as possible. Thorough and concrete knowledge on both will ensure effective identification of critical biomarkers that are either disease-related or associated with the compound’s pharmacologic response(s). If not already well-established, assays for those biomarkers should be developed and approved of use, ideally before the initiation of clinical trials to enhance both program efficiency and scientific validity. Drug developers should be prepared to discuss these nonclinical preparatory issues as early as possible during pre-investigational new drug application (pre-IND) meetings with the FDA. Finally, selecting appropriate efficacy endpoints for trials as the research progresses and presenting a strong case of effectiveness and safety in the end are critical to the eventual success of product approval. PRM200 Mitigating Treatment Unblinding In Electronic Systems With Patient-Reported Outcome Results Ross J, Holzbaur E, Rothrock T Almac Clinical Technologies, Souderton, PA, USA

Patient Reported Outcomes (PRO) are commonly used for measuring treatment efficacy, safety, and trial endpoints in clinical trials. PROs are a significant contribution to the patient-centeredness of clinical trials as responses come directly from patients. Important, but not frequently thought about/discussed when planning clinical trials, is whether PRO responses contain information that could be (potentially) unblinding. Knowledge of treatment assignment can influence the behavior of sponsor/study personnel/patients, introducing bias into the trial. When implementing PROs in blinded clinical trials, the set-up should be evaluated for unblinding risks as PRO responses from treatment efficacy assessments could provide information that allow the sponsor/study personnel/patients to guess the patient’s treatment assignment, therefore leading to unblinding. For example, if trial medication causes a specific and obvious side effect (e.g. urine discoloration) and the PRO assessment asks the patient questions on their urine color, it would be possible for the sponsor/ study personnel/patient to determine the patient’s assigned treatment group. Also, with the clinical trial world’s move to electronic systems (e.g. electronic Case Report Forms, Patient Reported Outcome, databases, etc.) to capitalize on the associated time and cost efficiencies, better quality data, and reduced burden to sponsor/study personnel/patients, the reports and dashboards available may provide for increased visibility of this unblinding information that although previously available may not have been obvious or apparent. To mitigate these risks, careful consideration should be made when planning for trials to ensure that PRO data is not unblinding or potentially unblinding, that access to unblinding data in reports and alerts is limited to only unblinded/unmasked team members. This conceptual paper will describe the importance of blinding in clinical trials, discussing unblinding risks associated with PROs and how to mitigate these risks through proper implementation and training. Examples will be provided. PRM201 Determining And Identifying Costs For Economic Evaluations: Guidance From Canada

1Navigha S.A. on behalf of GSK Vaccines, Brussels, Belgium, 2Keyrus Biopharma on behalf of GSK Vaccines, Lasnes, Belgium, 3GSK Vaccines, Wavre, Belgium

Budden AJ1, Lee KM1, Jacobs P2 1CADTH, Ottawa, ON, Canada, 2Institute of Health Economics, Edmonton, AB, Canada

Winter seasons show a decrease in hospital quality of care (QoC) due to the surge of paediatric diseases (rotavirus, RSV, influenza and pneumococcal infection) leading to overcrowded paediatric wards. Bed occupancy rates often reach 95%, above the 85% good management threshold, resulting in chaotic conditions. An evaluation study in Belgium reports an increase in QoC after rotavirus vaccine introduction. QoC could be improved by investing in more hospital beds and/or a larger vaccination programme while accounting for budget restrictions. We used the Cobb-Douglas model (1928), that defines the improvement in QoC (% growth) as a function of rotavirus vaccine coverage (%) and extra hospital beds (% of existing beds), to produce iso-QoC curves. The coordinates (vaccine coverage and extra hospital beds) of one specific iso-QoC curve identify the targeted QoC improvement. We determined which combination of these two strategies should be made to reach a given QoC improvement with the minimum budget. Data from Jessa Hospital (Hasselt, Belgium) were used as an example. The annual birth cohort in the catchment area is around 7,000 children, the winter period is 120 days (January – April) and 34 paediatric beds are available. The rotavirus vaccination cost per course is € 114 and the daily cost of a paediatric bed is € 298. We target a 50% increase in QoC and try to find the best budget allocation between vaccine prevention and extra hospital beds. Calibration is made with standard parameter values. The model suggests that to reach a 50% QoC increase the budget-minimizing allocation should be a combination of 95.75% vaccine coverage and 7.12% extra hospital beds (~2 extra beds). This model shows that both strategies need to be implemented but vaccination has clearly a higher potential than extra beds for improving the hospital QoC during winter seasons.

CADTH produced an update to the Guidance Document for the Costing of Health Care Resources in the Canadian Setting: 2nd edition in 2016. The document provides guidance regarding considerations for determining the selection of various costs in economic evaluations as well as identifying publicly available sources of cost information. The following steps are recommended when considering costs in an economic evaluation. 1) Establish the decision problem (including perspective, time horizon, target population, and setting) at the start of the evaluation. 2) Identify relevant costs for inclusion, based on decision problem and clinical pathway. 3) Measure resources using physical units. 4) Value the resources in monetary terms; the principle of opportunity cost may reflect the prices paid to acquire resources. 5) Identify potential sources of variability, uncertainty, and bias, which may be attributable to differences in published information or availability based on geography or having imperfect evidence, determine the validity and the complexity of the data required. 6) Report selection, use and justification of data sources clearly and transparently. The methodology, data sources, and calculations should also be provided. The Guidance Document for the Costing of Health Care Resources in the Canadian Setting: 2nd edition aims to better assist Canadian researchers in appropriately identifying, measuring, and sourcing the types of costs and resources relevant for economic evaluations. PRM202 An Analysis Of The Criteria Used In Existing Or Proposed Mcda Models Piniazhko O1, Nemeth B2