Joffe M M, Colditz G A
Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia 19104-6021, USA.
Stat Med. 1998 Oct 15;17(19):2233-49. doi: 10.1002/(sici)1097-0258(19981015)17:19<2233::aid-sim922>3.0.co;2-0.
The direct effect of a treatment on some outcome is that part of the treatment's effect not referred through a specified covariate intermediate on the pathway between treatment and outcome. Such direct effects are often of primary interest in a data analysis. Unfortunately, standard methods of analysis (for example, stratification or modelling) do not, in general, produce consistent estimates of direct effects whether or not the covariate is 'controlled'. Robins and co-authors have proposed two methods for estimation of direct effects applicable when reliable information is available on the covariate. We propose a third approach for reducing bias: data restriction. By restricting the analysis to strata of the data in which the effect of treatment on the covariate is small, we can (under certain assumptions) reduce bias in estimating treatment's direct effect. We discuss these points with reference to difference and ratio measures of treatment effect. The approach will sometimes be applicable even with an unmeasured or poorly measured covariate. We illustrate these points with data from an observational study of the effect of hormone replacement therapy on breast cancer.
治疗对某些结局的直接效应是指治疗效应中不通过治疗与结局之间路径上指定的协变量中介传递的那部分效应。这种直接效应在数据分析中通常是主要关注的内容。不幸的是，一般来说，无论协变量是否被“控制”，标准的分析方法（例如分层或建模）都不能产生一致的直接效应估计值。罗宾斯及其合著者提出了两种在协变量有可靠信息时适用的直接效应估计方法。我们提出了第三种减少偏差的方法：数据限制。通过将分析限制在数据中治疗对协变量影响较小的分层中，我们可以（在某些假设下）减少估计治疗直接效应时的偏差。我们参照治疗效应的差值和比值度量来讨论这些要点。即使存在未测量或测量不佳的协变量，该方法有时也适用。我们用一项关于激素替代疗法对乳腺癌影响的观察性研究数据来说明这些要点。
