Targeted maximum likelihood estimation in safety analysis

7Citations
Citations of this article
33Readers
Mendeley users who have this article in their library.

Abstract

Objectives To compare the performance of a targeted maximum likelihood estimator (TMLE) and a collaborative TMLE (CTMLE) to other estimators in a drug safety analysis, including a regression-based estimator, propensity score (PS)-based estimators, and an alternate doubly robust (DR) estimator in a real example and simulations. Study Design and Setting The real data set is a subset of observational data from Kaiser Permanente Northern California formatted for use in active drug safety surveillance. Both the real and simulated data sets include potential confounders, a treatment variable indicating use of one of two antidiabetic treatments and an outcome variable indicating occurrence of an acute myocardial infarction (AMI). Results In the real data example, there is no difference in AMI rates between treatments. In simulations, the double robustness property is demonstrated: DR estimators are consistent if either the initial outcome regression or PS estimator is consistent, whereas other estimators are inconsistent if the initial estimator is not consistent. In simulations with near-positivity violations, CTMLE performs well relative to other estimators by adaptively estimating the PS. Conclusion Each of the DR estimators was consistent, and TMLE and CTMLE had the smallest mean squared error in simulations. © 2013 Elsevier Inc. All rights reserved.

Cite

CITATION STYLE

APA

Lendle, S. D., Fireman, B., & Van Der Laan, M. J. (2013). Targeted maximum likelihood estimation in safety analysis. Journal of Clinical Epidemiology, 66(8 SUPPL.8). https://doi.org/10.1016/j.jclinepi.2013.02.017

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free