Power and sample assessments for tests of hypotheses on cost-effectiveness ratios
研究了成本效果比假设检验的统计功效和样本量计算方法，推导了单侧和双侧检验的公式，并考虑了成本与效果的相关性，发现忽略相关性会低估所需样本量。
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.