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Causal inference for recurrent event data using pseudo-observations.

Chien-Lin Su¹, Robert W Platt¹, Jean-François Plante²

¹Department of Epidemiology, Biostatistics and Occupational Health, McGill University and Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montréal, Québec, Canada.

Biostatistics (Oxford, England)

|May 21, 2020

Summary

This summary is machine-generated.

This study introduces new methods to compare recurrent event data, adjusting for confounders in treatment assignments. The proposed estimators are consistent and reliable for analyzing complex observational studies.

Keywords:

Cumulative rate function Doubly robust estimator Inverse probability of treatment weighting Pseudo-observations Recurrent event data Two-sample pseudo-score tests

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Area of Science:

Biostatistics
Epidemiology
Health Services Research

Background:

Recurrent event data are common in observational studies, posing challenges due to potential confounding.
Comparing cumulative rate functions (CRFs) between groups requires methods that account for treatment assignment influenced by confounders.

Purpose of the Study:

To develop and evaluate statistical methods for comparing CRFs in the presence of treatment-assignment confounding.
To propose robust estimators and hypothesis tests for recurrent event data analysis.

Main Methods:

Development of inverse probability of treatment weighting, regression-based, and doubly robust estimators using pseudo-observations.
Introduction of a bootstrap approach for variance estimation and residual plots for model diagnostics.
Proposal of adjusted two-sample pseudo-score tests for comparing CRFs.

Main Results:

The proposed marginal regression and doubly robust estimators demonstrate consistency and asymptotic normality.
Simulation studies confirm the finite sample performance of the developed methods.
The technique is validated using hospital readmission data.

Conclusions:

The novel pseudo-observation-based methods effectively adjust for confounding in recurrent event data analysis.
The proposed estimators and tests provide reliable tools for comparing CRFs in observational studies.
This work offers practical solutions for analyzing complex health-related recurrent event data.