Gardiner J C, Huebner M, Jetton J, Bradley C J
Department of Epidemiology, College of Human Medicine, Michigan State University, USA.
Health Econ. 2000 Apr;9(3):227-34. doi: 10.1002/(sici)1099-1050(200004)9:3<227::aid-hec509>3.0.co;2-z.
We address the issue of statistical power and sample size for cost-effectiveness studies. Tests of hypotheses on the cost-effectiveness ratio (CER) are constructed from the net cost and incremental effectiveness measures. When the difference in effectiveness is known, we derive formulae for statistical power and sample size assessments for one- and two-sided tests of hypotheses of the CER. We also construct a test of the joint hypothesis of cost-effectiveness and effectiveness and derive an expression connecting power and sample size. Our methods account for the correlation between cost and effectiveness and lead to smaller sample size requirements than comparative methods that ignore the correlation. The implications of our formulae for cost-effectiveness studies are illustrated through numerical examples. When compared with trials designed to demonstrate effectiveness alone, our results indicate that a trial appropriately powered to demonstrate cost-effectiveness might require sample sizes many times greater.
我们探讨了成本效益研究中的统计功效和样本量问题。关于成本效益比（CER）的假设检验是根据净成本和增量效果指标构建的。当效果差异已知时，我们推导了用于CER单侧和双侧假设检验的统计功效和样本量评估公式。我们还构建了成本效益和效果联合假设的检验，并推导了一个将功效与样本量联系起来的表达式。我们的方法考虑了成本与效果之间的相关性，与忽略相关性的比较方法相比，所需样本量更小。通过数值示例说明了我们的公式对成本效益研究的影响。与仅旨在证明效果的试验相比，我们的结果表明，一个有足够功效来证明成本效益的试验可能需要大很多倍的样本量。
