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Doubly robust estimation and causal inference for recurrent event data.

Chien-Lin Su^1,2,3, Russell Steele¹, Ian Shrier³

¹Department of Mathematics and Statistics, McGill University, Montréal, Canada.

Statistics in Medicine

|April 30, 2020

Summary

This summary is machine-generated.

This study introduces a novel doubly robust method to estimate causal effects for recurrent event data, accounting for confounders. The new approach offers consistent estimation and includes diagnostics for model adequacy.

Keywords:

Nelson-Aalen estimator average causal effect confounder multiplicative rate model recurrent events

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Area of Science:

Biostatistics
Epidemiology
Longitudinal Data Analysis

Background:

Longitudinal databases frequently track recurrent events over time.
Estimating causal effects in such data requires robust statistical methods.
Confounding factors can bias estimates of treatment effects.

Purpose of the Study:

To propose a new doubly robust semiparametric method for estimating the average causal effect of a binary treatment.
To address recurrent event data analysis in the presence of confounders.
To provide a model diagnostic tool for assessing semiparametric rate models.

Main Methods:

A doubly robust semiparametric estimator combining weighted Nelson-Aalen and conditional regression estimators.
Utilizing a semiparametric multiplicative rate model for recurrent events.
Developing a residual-based diagnostic plot for model adequacy.

Main Results:

The proposed doubly robust estimator is proven to be consistent and asymptotically normal.
Simulation studies demonstrate the finite sample performance of the estimators.
The methodology is illustrated using a real-world dataset of circus artist injuries.

Conclusions:

The new doubly robust method provides a reliable approach for causal effect estimation with recurrent event data.
The diagnostic plot aids in validating the underlying semiparametric model assumptions.
This method enhances the analysis of longitudinal data with recurrent events and confounders.