Misuse of Regression Adjustment for Additional Confounders Following Insufficient Propensity Score Balancing is a research paper published in Epidemiology (2019). On theSindex it has a DataRank of 0.698. It has been cited 14 times, with 10 citing works in its 1-hop citation network.
After propensity score (PS) matching, inverse probability weighting, and stratification or regression adjustment for PS, one may compare different exposure groups with or without further covariate adjustment. In the former case, although a typical application uses the same set of covariates in the PS and the stratification post-PS balancing, several studies adjust for additional confounders in the stratification while ignoring the covariates that have been balanced by the PS. We show the bias arising from such partial adjustments for distinct sets of confounders by PS and regression or stratification. Namely, the stratification or regression after PS balancing causes imbalance in the confounders that have been balanced by the PS if PS-balanced confounders are ignored. We empirically illustrate the bias in the Rotterdam Tumor Bank, in which strong confounders distort the association between chemotherapy and recurrence-free survival. If additional covariates are adjusted for after PS balancing, the covariate sets conditioned in PS should be again adjusted for, or PS should be reestimated by including the additional covariates to avoid bias owing to covariate imbalance.
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Base Score Contribution
0.406
From this paper's citation signal
Citation Network Contribution
0.292
From 6 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 58% comes from its base citations and 42% from the citation network (6 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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