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Title: Causal inference for recurrent event data using pseudo-observations Authors:  Chien-Lin Su - McGill University (Canada) [presenting]
Robert Platt - McGill University (Canada)
Jean-Francois Plante - HEC Montreal (Canada)
Abstract: Recurrent event data are commonly encountered in observational studies where each subject may experience a particular event repeatedly over time. We aim to compare cumulative rate functions of two groups when treatment assignment may depend on the unbalanced distribution of confounders. Based on pseudo-observations, several estimators including inverse probability of treatment weighting estimator, regression model-based estimators and doubly robust estimators are proposed to adjust for the confounding effects. The proposed marginal regression estimator based on pseudo-observations is 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 group differences of cumulative rate functions. 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.