Empirical comparison of four baseline covariate adjustment methods in analysis of continuous outcomes in randomized controlled trials
Empirical comparison of four baseline covariate adjustment methods in analysis of continuous outcomes in randomized controlled trials
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随机对照试验中连续结果分析中四种基线协变量调整方法的实证比较
作者：
Shiyuan Zhang;J. Paul;Manyat Nantha;N. Buckley;Uswa Shahzad;Ji Cheng;J. Debeer;M. Winemaker;D. Wismer;Dinshaw Punthakee;V. Avram;L. Thabane
背景：虽然看起来很简单，但在一项随机对照试验中，对一个连续变量进行统计学比较，对试验者来说，这是一个有趣的挑战。我们在此介绍了四种统计方法的经验应用（方差分析的治疗后评分、协方差分析、评分变化和评分变化百分比），使用的数据来自全关节置换术后患者术后疼痛的随机对照试验（关节置换患者的吗啡消耗，加和不加巴喷丁治疗，一项随机对照研究[移动的]试验）。方法协方差分析（ANCOVA）用于调整基线测量，并提供膝关节置换术患者术后1年膝关节屈曲评分平均组间差异的无偏估计。通过比较ANCOVA与三种比较方法进行稳健性检验：治疗后评分、评分变化和较基线的百分比变化。结果4种方法的作用方向相似，但协方差分析（-3.9; 95%置信区间[CI]：-9.5，1.6; P=0.15）和治疗后评分（-4.3; 95% CI：-9.8，1.2; P=0.12）方法提供了与变化评分相比的最高估计精度（−3.0; 95% CI：−9.9，3.8; P=0.38）和百分比变化（−0.019; 95% CI：−0.087，0.050; P=0.58）。结论ANCOVA通过模拟和实证研究，为分析需要协变量调整的连续结果提供了最佳的统计估计。我们的实证研究结果支持使用ANCOVA作为设计和分析具有连续主要结局的试验的最佳方法。
Background Although seemingly straightforward, the statistical comparison of a continuous variable in a randomized controlled trial that has both a pre- and posttreatment score presents an interesting challenge for trialists. We present here empirical application of four statistical methods (posttreatment scores with analysis of variance, analysis of covariance, change in scores, and percent change in scores), using data from a randomized controlled trial of postoperative pain in patients following total joint arthroplasty (the Morphine COnsumption in Joint Replacement Patients, With and Without GaBapentin Treatment, a RandomIzed ControlLEd Study [MOBILE] trials). Methods Analysis of covariance (ANCOVA) was used to adjust for baseline measures and to provide an unbiased estimate of the mean group difference of the 1-year postoperative knee flexion scores in knee arthroplasty patients. Robustness tests were done by comparing ANCOVA with three comparative methods: the posttreatment scores, change in scores, and percentage change from baseline. Results All four methods showed similar direction of effect; however, ANCOVA (−3.9; 95% confidence interval [CI]: −9.5, 1.6; P=0.15) and the posttreatment score (−4.3; 95% CI: −9.8, 1.2; P=0.12) method provided the highest precision of estimate compared with the change score (−3.0; 95% CI: −9.9, 3.8; P=0.38) and percent change (−0.019; 95% CI: −0.087, 0.050; P=0.58). Conclusion ANCOVA, through both simulation and empirical studies, provides the best statistical estimation for analyzing continuous outcomes requiring covariate adjustment. Our empirical findings support the use of ANCOVA as an optimal method in both design and analysis of trials with a continuous primary outcome.