Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Health Econ. 2012 Oct;21(10):1183-95. doi: 10.1002/hec.1781. Epub 2011 Sep 1.
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.
先前应用信息价值方法确定随机临床试验最优样本量的应用都假设在增量净收益均值方面没有研究间的差异。通过采用分层模型，我们提供了一种在放宽这一假设的情况下确定最优样本量的解决方案。该解决方案通过来自文献的两个实例进行了说明。预期净收益随着研究间差异的增加而增加，这反映了增量净收益的不确定性增加，以及从以往证据中借鉴数据的程度降低。因此，在假设没有研究间差异的情况下，如果当前证据充足，试验可以成为最佳选择。然而，尽管预期净收益增加，所说明的实例中的最优样本量对研究间差异的数量相对不敏感。即使选择反映研究间差异非常大的样本量，预期净收益的百分比损失也很小。
