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Targeted maximum likelihood based causal inference: Part II.

Mark J van der Laan¹

¹University of California - Berkeley, CA, USA.

The International Journal of Biostatistics

|July 7, 2011

Summary

This summary is machine-generated.

This article presents a practical template for the targeted maximum likelihood estimator (TMLE) to analyze causal effects from multiple time point interventions. It demonstrates TMLE applications in individualized treatment rules and baseline treatment effects on censored survival data.

Keywords:

causal effect causal graph censored data collaborative double robust cross-validation double robust dynamic treatment regimens efficient influence curve estimating function estimator selection locally efficient loss function marginal structural models for dynamic treatments maximum likelihood estimation model selection path-wise derivative randomized controlled trials sieve super-learning targeted maximum likelihood estimation

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Area of Science:

Causal inference
Statistical methodology
Biostatistics

Background:

Part I of this series introduced the targeted maximum likelihood estimator (TMLE) for analyzing causal effects of multiple time point interventions.
Practical implementation guidelines are crucial for applying advanced statistical methods in real-world research.

Purpose of the Study:

To provide a practical implementation template for the TMLE methodology.
To demonstrate the application of this template in estimating causal effects for complex scenarios.
To facilitate the analysis of individualized treatment rules and baseline treatment effects in clinical trials.

Main Methods:

Development of a practical template for targeted maximum likelihood estimation (TMLE).
Application of the TMLE template to estimate the effect of individualized treatment rules using marginal structural models.
Application of the TMLE template to estimate the effect of a baseline treatment on time-to-event data with right censoring in a randomized clinical trial.

Main Results:

A clear template for implementing TMLE for multiple time point interventions is provided.
Demonstrated successful application of the TMLE template in two distinct, complex estimation problems.
The methodology allows for robust causal effect estimation in the presence of time-varying treatments and censoring.

Conclusions:

The proposed TMLE implementation template offers a practical approach for analyzing complex causal inference problems.
This methodology enhances the ability to estimate treatment effects from observational and trial data, particularly for individualized treatment strategies and survival outcomes.
The template serves as a valuable tool for researchers seeking to apply advanced causal inference techniques.