One‐step targeted maximum likelihood estimation for time‐to‐event outcomes

• Published in: Biometrics Vol. 76; no. 3; pp. 722 - 733
• Main Authors: Cai, Weixin, Laan, Mark J.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.09.2020
• Subjects: biometry; causal inference; censored data; Estimation; Failure times; machine learning; Maximum likelihood estimation; Maximum likelihood estimators; Monoclonal gammopathy; objectives; observational studies; Random variables; sampling; Statistical inference; Survival; Survival analysis; survival curve; targeted maximum likelihood estimation; survival curve; censored data; survival analysis; targeted maximum likelihood estimation; causal inference; machine learning
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.13172

Researchers in observational survival analysis are interested in not only estimating survival curve nonparametrically but also having statistical inference for the parameter. We consider right‐censored failure time data where we observe n independent and identically distributed observations of a vector random variable consisting of baseline covariates, a binary treatment at baseline, a survival time subject to right censoring, and the censoring indicator. We assume the baseline covariates are allowed to affect the treatment and censoring so that an estimator that ignores covariate information would be inconsistent. The goal is to use these data to estimate the counterfactual average survival curve of the population if all subjects are assigned the same treatment at baseline. Existing observational survival analysis methods do not result in monotone survival curve estimators, which is undesirable and may lose efficiency by not constraining the shape of the estimator using the prior knowledge of the estimand. In this paper, we present a one‐step Targeted Maximum Likelihood Estimator (TMLE) for estimating the counterfactual average survival curve. We show that this new TMLE can be executed via recursion in small local updates. We demonstrate the finite sample performance of this one‐step TMLE in simulations and an application to a monoclonal gammopathy data.