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Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted
Helene C W Rytgaard1, Frank Eriksson1, Mark J van der Laan2
1Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark.
Biometrics
|March 29, 2023
Summary
This study introduces targeted maximum likelihood estimation (TMLE) for analyzing time-to-event data with competing risks. The novel method enhances causal inference using machine learning for accurate treatment effect estimation.
Area of Science:
- Biostatistics
- Causal Inference
- Machine Learning
Background:
- Time-to-event data analysis often involves complexities like right-censoring and competing risks.
- Targeted Maximum Likelihood Estimation (TMLE) is a robust statistical methodology for causal effect estimation.
- Existing TMLE methods require adaptation for continuous-time competing risks scenarios.
Purpose of the Study:
- To extend and specialize TMLE for analyzing treatment effects on absolute risk and survival probabilities in time-to-event data with competing risks.
- To develop and implement a novel targeting algorithm for continuous-time TMLE in competing risks settings.
- To integrate the highly adaptive lasso estimator for flexible modeling of conditional hazards.
Main Methods:
- Specialized continuous-time TMLE for competing risks, employing an iterative targeting algorithm to update cause-specific hazards.
- Implementation of the highly adaptive lasso estimator for continuous-time conditional hazards using L1-penalized Poisson regression.
- Validation through simulations and application to a follicular cell lymphoma dataset with time-varying effects.
Main Results:
- The proposed TMLE procedure effectively estimates causal parameters under mild nonparametric restrictions.
- The highly adaptive lasso successfully captures complex, time-varying effects in the follicular cell lymphoma data.
- Simulations demonstrated competitive performance compared to random survival forests and discrete-time TMLE.
Conclusions:
- The developed TMLE framework offers a powerful, machine-learning-based tool for semiparametric causal inference in continuous-time time-to-event data with competing risks.
- This approach provides a flexible and data-driven method for estimating treatment effects in complex survival data.
- The findings highlight the utility of advanced statistical methods for analyzing real-world clinical data.
Keywords:
causal inferencecompeting risksnonparametric inferencesemiparametric efficiency theorysurvival analysistargeted maximum likelihood estimationMore Related Videos
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