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Lifetime Data Analysis

Sensitivity Analysis for Observational Studies with Recurrent Events

  • 12-08-2023
Published in:
Authors
Jeffrey Zhang
Dylan S. Small
Published in
Lifetime Data Analysis | Issue 1/2024

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Abstract

The article introduces a sensitivity analysis method for observational studies with recurrent events, using the sickle-cell trait and malaria fevers as a case study. It addresses the challenge of unmeasured confounding by developing a model-based approach within the Andersen-Gill and Prentice-Williams-Peterson Gap-Time models. The method is validated through simulations and applied to real-world data, demonstrating its effectiveness in handling complex recurrent event structures. The authors also discuss the calibration of the analysis to observed covariates and the importance of the unmeasured confounder, providing a comprehensive framework for robust causal inference in observational studies.
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previous article Causal survival analysis under competing risks using longitudinal modified treatment policies
next article Assessing model prediction performance for the expected cumulative number of recurrent events
Title
Sensitivity Analysis for Observational Studies with Recurrent Events
Authors
Jeffrey Zhang
Dylan S. Small
Publication date
12-08-2023
Publisher
Springer US
Published in
Lifetime Data Analysis / Issue 1/2024
Print ISSN: 1380-7870
Electronic ISSN: 1572-9249
DOI
https://doi.org/10.1007/s10985-023-09607-6
This content is only visible if you are logged in and have the appropriate permissions.
This content is only visible if you are logged in and have the appropriate permissions.

Other articles of this Issue 1/2024

Assessing model prediction performance for the expected cumulative number of recurrent events

Causal survival analysis under competing risks using longitudinal modified treatment policies

Targeted maximum likelihood estimation for causal inference in survival and competing risks analysis

Causal inference with recurrent and competing events

  • Open Access

Model-based hypothesis tests for the causal mediation of semi-competing risks

RKHS-based covariate balancing for survival causal effect estimation

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