Causal inference for recurrent event data using pseudo-observations. (20th May 2020)

• Record Type: Journal Article
• Authors: Su, Chien-Lin; Platt, Robert W; Plante, Jean-François
• Is Part Of: Biostatistics. Volume 23:Number 1(2022)
• Page Start: 189
• Page End: 206
• Publication Date: 2020-05-20
• Subjects: Cumulative rate function -- Doubly robust estimator -- Inverse probability of treatment weighting -- Pseudo-observations -- Recurrent event data -- Two-sample pseudo-score tests; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxaa020
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: Recurrent event data are commonly encountered in observational studies where each subject may experience a particular event repeatedly over time. In this article, we aim to compare cumulative rate functions (CRFs) of two groups when treatment assignment may depend on the unbalanced distribution of confounders. Several estimators based on pseudo-observations are proposed to adjust for the confounding effects, namely inverse probability of treatment weighting estimator, regression model-based estimators, and doubly robust estimators. The proposed marginal regression estimator and doubly robust estimators based on pseudo-observations are shown to be consistent and asymptotically normal. A bootstrap approach is proposed for the variance estimation of the proposed estimators. Model diagnostic plots of residuals are presented to assess the goodness-of-fit for the proposed regression models. A family of adjusted two-sample pseudo-score tests is proposed to compare two CRFs. Simulation studies are conducted to assess finite sample performance of the proposed method. The proposed technique is demonstrated through an application to a hospital readmission data set.