Targeted maximum likelihood estimation for pharmacoepidemiological research.

Targeted maximum likelihood estimation for pharmacoepidemiological research.

Overview

What is the issue?
  • Targeted maximum likelihood estimation has been proposed for estimating marginal causal effects, and is robust to misspecification of either the treatment or outcome model. However, due perhaps to its novelty, targeted maximum likelihood estimation has not been widely used in pharmacoepidemiology.

What is the aim of the study?
  • The objective of this study was to demonstrate targeted maximum likelihood estimation in a pharmacoepidemiological study with a high-dimensional covariate space, to incorporate the use of high-dimensional propensity scores into this method, and to compare the results to those of inverse probability weighting.

How was the study conducted?
  • We implemented the targeted maximum likelihood estimation procedure in a single-point exposure study of the use of statins and the 1-year risk of all-cause mortality postmyocardial infarction using data from the UK Clinical Practice Research Datalink.

  • A range of known potential confounders were considered, and empirical covariates were selected using the high-dimensional propensity scores algorithm.
  • We estimated odds ratios using targeted maximum likelihood estimation and inverse probability weighting with a variety of covariate selection strategies.
What did the study find?
  • Through a real example, we demonstrated the double robustness of targeted maximum likelihood estimation. We showed that results with this method and inverse probability weighting differed when a large number of covariates were included in the treatment model.

Implications
  • Targeted maximum likelihood can be used in highdimensional covariate settings.

  • In high-dimensional covariate settings, differences in results between targeted maximum likelihood and inverse probability weighted estimation are likely due to sensitivity to (near) positivity violations. Further investigations are needed to gain better understanding of the advantages and limitations of this method in pharmacoepidemiological studies.

Manuscripts

Pang M, Schuster T, Filion K, Eberg M, Platt RW. Targeted maximum likelihood estimation for pharmacoepidemiological research. Epidemiology. 2016 Jul;27(4):570-7.

Presentations

Project Team

Corresponding Author
Robert W. Platt PhD
CPRD
Collaborator
Maria Eberg
CPRD
Collaborator
Kristian Filion PhD
CPRD
Collaborator
Menglan Pang MSc
CPRD