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Prognostic Covariate Adjustment for Logistic Regression in Randomized Controlled Trials
March 1, 2024, 5:45 a.m. | Yunfan Li, Arman Sabbaghi, Jonathan R. Walsh, Charles K. Fisher
stat.ML updates on arXiv.org arxiv.org
Abstract: Randomized controlled trials (RCTs) with binary primary endpoints introduce novel challenges for inferring the causal effects of treatments. The most significant challenge is non-collapsibility, in which the conditional odds ratio estimand under covariate adjustment differs from the unconditional estimand in the logistic regression analysis of RCT data. This issue gives rise to apparent paradoxes, such as the variance of the estimator for the conditional odds ratio from a covariate-adjusted model being greater than the variance …
abstract analysis arxiv binary challenge challenges effects endpoints logistic regression novel regression stat.ap stat.me stat.ml type
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