| Literature DB >> 21731531 |
Abstract
In this article, we provide a template for the practical implementation of the targeted maximum likelihood estimator for analyzing causal effects of multiple time point interventions, for which the methodology was developed and presented in Part I. In addition, the application of this template is demonstrated in two important estimation problems: estimation of the effect of individualized treatment rules based on marginal structural models for treatment rules, and the effect of a baseline treatment on survival in a randomized clinical trial in which the time till event is subject to right censoring.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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Year: 2010 PMID： 21731531 PMCID： PMC3126672 DOI： 10.2202/1557-4679.1241
Source DB: PubMed Journal: Int J Biostat ISSN： 1557-4679 Impact factor: 0.968