ACCOUNTING FOR BETWEEN‐STUDY VARIATION IN INCREMENTAL NET BENEFIT IN VALUE OF INFORMATION METHODOLOGY
提出一种分层模型方法，在确定随机临床试验最优样本量时，允许增量净收益存在研究间变异，并通过两个实例说明预期净收益随变异增加而上升，但最优样本量对变异不敏感。
Previous applications of value of information methods for determining optimal sample size in randomized clinical trials have assumed no between-study variation in mean incremental net benefit. By adopting a hierarchical model, we provide a solution for determining optimal sample size with this assumption relaxed. The solution is illustrated with two examples from the literature. Expected net gain increases with increasing between-study variation, reflecting the increased uncertainty in incremental net benefit and reduced extent to which data are borrowed from previous evidence. Hence, a trial can become optimal where current evidence is sufficient assuming no between-study variation. However, despite the expected net gain increasing, the optimal sample size in the illustrated examples is relatively insensitive to the amount of between-study variation. Further percentage losses in expected net gain were small even when choosing sample sizes that reflected widely different between-study variation.