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随机实验中加性风险模型的辅助协变量调整AI 解读

We consider additive hazard models (Aalen, 1989) for the effect of a randomized treatment on a survival outcome, adjusting for auxiliary baseline covariates. We demonstrate that the Aalen least squares estimator of the treatment effect parameter is asymptotically unbiased, even when the hazard's dependence on time or on the auxiliary covariates is misspecified, and even away from the null hypothesis of no treatment effect. We moreover show that adjustment for auxiliary…

利用逻辑斯蒂结构直接效应模型估计二元结局的受控直接效应AI 解读

We consider the problem of assessing whether an exposure affects a dichotomous outcome other than by modifying a given mediator. The standard approach, logistic regression adjusting for both exposure and the mediator, is known to be biased in the presence of confounders for the mediator-outcome relationship. Because additional regression adjustment for such confounders is only justified when they are not affected by the exposure, inverse probability weighting has been…

不可忽略非单调无应答下重复结果均值回归的估计AI 解读

We propose a new class of models for making inference about the mean of a vector of repeated outcomes when the outcome vector is incompletely observed in some study units and missingness is nonmonotone. Each model in our class is indexed by a set of unidentified selection-bias functions which quantify the residual association of the outcome at each occasion t and the probability that this outcome is missing after adjusting for variables observed prior to time t and for the…

使用广义结构均值模型进行因果推断AI 解读

Summary We estimate cause–effect relationships in empirical research where exposures are not completely controlled, as in observational studies or with patient non-compliance and self-selected treatment switches in randomized clinical trials. Additive and multiplicative structural mean models have proved useful for this but suffer from the classical limitations of linear and log-linear models when accommodating binary data. We propose the generalized structural mean model to…