Drug Evaluation Committee Is the Population Adjustment Method Useful in Indirect Comparisons? Matching Adjusted Indirect Comparison and Simulated Treatment Comparison
Data Science Subcommittee
January 2022
The National Institute for Health and Care Excellence (NICE) in the UK, an early adopter of cost-effectiveness evaluation, has published 21 Technical Support Documents (TSD) on statistical methods for health economic evaluation. Document (TSD) on statistical methods for conducting health economic evaluation. These TSDs are very helpful documents for Japan, which is formally introducing cost-effectiveness evaluation from 2019. The Data Science Subcommittee 2021 Continuing Task Force 3 is working to organize the subjects dealt with in NICE's TSDs and provide information that may be useful for practicing cost-effectiveness evaluation in Japan. This report focuses on TSD 18, Methods for population-adjusted indirect comparisons in submissions to NICE, and addresses the situation that companies commonly face (i.e., their own trials have individual patient data and other companies' trials have population Matching Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC)] to adjust for differences in the distribution of effect modifiers across trials in situations commonly faced by companies (where their own trials have individual patient data and other trials only have population-level or group-level data available). Explanation of the method [Matching Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC)] for conducting indirect comparisons by adjusting for differences in the distribution of effect modifiers across trials. Explanation of the method and its theoretical background is provided, as well as performance evaluation of the method based on simulation studies, recent application examples, and an example implementation in R. Although this method has been applied to an increasing number of cases in recent years and has been recognized after its full-scale introduction in Japan, caution should be exercised when applying this method because it requires very strong assumptions. We hope that this report will assist you in selecting an appropriate method and conducting an analysis when conducting indirect comparisons.
Japan Pharmaceutical Manufacturers Association Drug Evaluation Committee
Data Science Subcommittee 2021 Ongoing Task Force 3