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Review
. 2009 Jul;47(7 Suppl 1):S14-20.
doi: 10.1097/MLR.0b013e3181a31971.

Design and analysis issues for economic analysis alongside clinical trials

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Review

Design and analysis issues for economic analysis alongside clinical trials

Deborah A Marshall et al. Med Care. 2009 Jul.

Abstract

Introduction: Clinical trials can offer a valuable and efficient opportunity to collect the health resource use and outcomes data for economic evaluation. However, economic and clinical studies differ fundamentally in the question they seek to answer.

Objective: The design and analysis of trial-based cost-effectiveness studies require special consideration, which are reviewed in this article.

Summary: Traditional randomized controlled trials, using an experimental design with a controlled protocol, are designed to measure safety and efficacy for product registration. Cost-effectiveness analysis seeks to measure effectiveness in the context of routine clinical practice, and requires collection of health care resources to allow estimation of cost over an equal timeframe for each treatment alternative. In assessing suitability of a trial for economic data collection, the comparator treatment and other protocol factors need to reflect current clinical practice and the trial follow-up must be sufficiently long to capture important costs and effects. The broadest available population and a measure of effectiveness reflecting important benefits for patients are preferred for economic analyses. Special analytical issues include dealing with missing and censored cost data, assessing uncertainty of the incremental cost-effectiveness ratio, and accounting for the underlying heterogeneity in patient subgroups. Careful consideration also needs to be given to data from multinational studies since practice patterns can differ across countries.

Conclusion: Although clinical trials can be an efficient opportunity to collect data for economic evaluation, careful consideration of the suitability of the study design, and appropriate analytical methods must be applied to obtain rigorous results.

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