Causal inference for recurrent event data using pseudo-observations
| Autor: | Chien-Lin Su, Jean-François Plante, Robert W Platt |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
Models Statistical Confounding Estimator Regression analysis Sample (statistics) General Medicine 01 natural sciences Causality Data set 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Causal inference Statistics Humans Computer Simulation 030212 general & internal medicine 0101 mathematics Statistics Probability and Uncertainty CRFS Probability Event (probability theory) Mathematics |
| Zdroj: | Biostatistics. 23:189-206 |
| ISSN: | 1468-4357 1465-4644 |
| DOI: | 10.1093/biostatistics/kxaa020 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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