title: Targeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research.
abstract: 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. 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  Targeted maximum likelihood can be used in high-dimensional 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.
authors.affiliation: From the aCentre For Clinical Epidemiology, Lady Davis Research Institute, Jewish General Hospital, Montreal, Quebec, Canada; bDepartment of Epidemiology, Biostatistics and Occupational Health, cDepartment of Pediatrics, dDivision of Clinical Epidemiology, Department of Medicine, McGill University, Montreal, Quebec, Canada; and eThe Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.
authors.forename: Menglan
authors.initials: M
authors.lastname: Pang
authors.forename: Tibor
authors.initials: T
authors.lastname: Schuster
authors.forename: Kristian B
authors.initials: KB
authors.lastname: Filion
authors.forename: Maria
authors.initials: M
authors.lastname: Eberg
authors.forename: Robert W
authors.initials: RW
authors.lastname: Platt
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002423:Cause of Death; D015331:Cohort Studies; D016208:Databases, Factual; D005260:Female; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use*; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D009026:Mortality; D009203:Myocardial Infarction / Q000188:drug therapy*; D017891:Pharmacoepidemiology; D012189:Retrospective Studies; D006113:United Kingdom; D055815:Young Adult
chemical_list: D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors