Improving efficiency of inferences in randomized clinical trials using auxiliary covariates
Improving efficiency of inferences in randomized clinical trials using auxiliary covariates
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DOI：
10.1111/j.1541-0420.2007.00976.x
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发表时间：
2008-09-01
期刊：
影响因子：
1.9
通讯作者：
Davidian, Marie
中科院分区：
文献类型：
--
作者：
Zhang, Min;Tsiatis, Anastasios A.;Davidian, Marie
随机临床试验的主要目标是对两种或多种治疗方法进行比较。例如，在连续反应的双臂试验中，重点可能在于治疗手段的差异；对于两种以上的治疗，比较可以基于成对差异。对于二元结果，可以使用成对优势比或对数优势比。一般来说，比较可以基于相关统计模型中有意义的参数。在这种情况下，估计和测试的标准分析通常仅基于针对反应和治疗分配收集的数据。在许多试验中，辅助基线协变量信息也可能是可用的，利用这些数据来提高推理效率是很有意义的。从半参数理论的角度来看，我们提出了一种广泛适用的方法来调整辅助协变量，以在随机临床试验分析中实现更有效的治疗参数估计和测试。模拟和应用证明了该方法的性能。
The primary goal of a randomized clinical trial is to make comparisons among two or more treatments. For example, in a two-arm trial with continuous response, the focus may be on the difference in treatment means; with more than two treatments, the comparison may be based on pairwise differences. With binary outcomes, pairwise odds ratios or log odds ratios may be used. In general, comparisons may be based on meaningful parameters in a relevant statistical model. Standard analyses for estimation and testing in this context typically are based on the data collected on response and treatment assignment only. In many trials, auxiliary baseline covariate information may also be available, and it is of interest to exploit these data to improve the efficiency of inferences. Taking a semiparametric theory perspective, we propose a broadly applicable approach to adjustment for auxiliary covariates to achieve more efficient estimators and tests for treatment parameters in the analysis of randomized clinical trials. Simulations and applications demonstrate the performance of the methods.