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The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.

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1557-4679
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A General Implementation of TMLE for Longitudinal Data Applied to Causal Inference in Survival Analysis

Ori M. Stitelman
  • University of California - Berkeley
/ Victor De Gruttola
  • Harvard School of Public Health
/ Mark J. van der Laan
  • University of California - Berkeley
Published Online: 2012-09-18 | DOI: https://doi.org/10.1515/1557-4679.1334

Abstract

In many randomized controlled trials the outcome of interest is a time to event, and one measures on each subject baseline covariates and time-dependent covariates until the subject either drops-out, the time to event is observed, or the end of study is reached. The goal of such a study is to assess the causal effect of the treatment on the survival curve. We present a targeted maximum likelihood estimator of the causal effect of treatment on survival fully utilizing all the available covariate information, resulting in a double robust locally efficient substitution estimator that will be consistent and asymptotically linear if either the censoring mechanism is consistently estimated, or if the maximum likelihood based estimator is already consistent. In particular, under the independent censoring assumption assumed by current methods, this TMLE is always consistent and asymptotically linear so that it provides valid confidence intervals and tests. Furthermore, we show that when both the censoring mechanism and the initial maximum likelihood based estimator are mis-specified, and thus inconsistent, the TMLE exhibits stability when inverse probability weighted estimators and double robust estimating equation based methods break down The TMLE is used to analyze the Tshepo study, a study designed to evaluate the efficacy, tolerability, and development of drug resistance of six different first-line antiretroviral therapies. Most importantly this paper presents a general algorithm that may be used to create targeted maximum likelihood estimators of a large class of parameters of interest for general longitudinal data structures.

Keywords: survival analysis; causal inference; double robust; targeted maximum likelihood estimation; time-dependent covariates; informative censoring

About the article

Published Online: 2012-09-18



Citation Information: The International Journal of Biostatistics, ISSN (Online) 1557-4679, DOI: https://doi.org/10.1515/1557-4679.1334. Export Citation

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[1]
Romain Neugebauer, Julie A. Schmittdiel, and Mark J. van der Laan
Statistics in Medicine, 2014, Volume 33, Number 14, Page 2480
[2]
Mireille E. Schnitzer, Erica E.M. Moodie, Mark J. van der Laan, Robert W. Platt, and Marina B. Klein
Biometrics, 2014, Volume 70, Number 1, Page 144

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