Mohamed Khadeeja, Embleton Andrew, Cuffe Robert L
GlaxoSmithKline, Uxbridge, UK.
Pharm Stat. 2011 Sep-Oct;10(5):461-6. doi: 10.1002/pst.520.
Adjusting for covariates makes efficient use of data and can improve the precision of study results or even reduce sample sizes. There is no easy way to adjust for covariates in a non-inferiority study for which the margin is defined as a risk difference. Adjustment is straightforward on the logit scale, but reviews of clinical studies suggest that the analysis is more often conducted on the more interpretable risk-difference scale. We examined four methods that allow for adjustment on the risk-difference scale: stratified analysis with Cochran-Mantel-Haenszel (CMH) weights, binomial regression with an identity link, the use of a Taylor approximation to convert results from the logit to the risk-difference scale and converting the risk-difference margin to the odds-ratio scale. These methods were compared using simulated data based on trials in HIV. We found that the CMH had the best trade-off between increased efficiency in the presence of predictive covariates and problems in analysis at extreme response rates. These results were shared with regulatory agencies in Europe and the USA, and the advice received is described.
对协变量进行调整能够有效利用数据，并可提高研究结果的精度，甚至能减少样本量。在非劣效性研究中，若界值定义为风险差值，对协变量进行调整并非易事。在对数单位尺度上进行调整很直接，但对临床研究的综述表明，分析更常是在更易于解释的风险差值尺度上进行。我们研究了四种可在风险差值尺度上进行调整的方法：采用 Cochr an - Mantel - Haenszel（CMH）权重的分层分析、具有恒等连接函数的二项回归、使用泰勒近似将对数单位尺度的结果转换为风险差值尺度以及将风险差值界值转换为比值比尺度。基于艾滋病病毒试验的模拟数据对这些方法进行了比较。我们发现，CMH在存在预测性协变量时提高效率与极端反应率下分析问题之间达到了最佳平衡。这些结果已分享给欧洲和美国的监管机构，并阐述了所收到的建议。
