Targeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research
| العنوان: | Targeted Maximum Likelihood Estimation for Pharmacoepidemiologic Research |
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| المؤلفون: | Menglan Pang, Tibor Schuster, Maria Eberg, Robert W. Platt, Kristian B. Filion |
| المصدر: | Epidemiology (Cambridge, Mass.) |
| بيانات النشر: | Ovid Technologies (Wolters Kluwer Health), 2016. |
| سنة النشر: | 2016 |
| مصطلحات موضوعية: | Adult, Male, Adolescent, Databases, Factual, Epidemiology, Computer science, Maximum likelihood, Myocardial Infarction, 01 natural sciences, Cohort Studies, Young Adult, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Cause of Death, Statistics, Covariate, Humans, Statistics::Methodology, 030212 general & internal medicine, Mortality, 0101 mathematics, Aged, Retrospective Studies, Likelihood Functions, Pharmacoepidemiology, Inverse probability weighting, Causal effect, Novelty, Middle Aged, United Kingdom, Outcome (probability), 3. Good health, Propensity score matching, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Female, Hydroxymethylglutaryl-CoA Reductase Inhibitors |
| الوصف: | Supplemental Digital Content is available in the text. Background: 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. Methods: 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. Results: 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. Conclusions: 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. |
| تدمد: | 1044-3983 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df9b7f862bb4ef4476ae73af7a620de8Test https://doi.org/10.1097/ede.0000000000000487Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....df9b7f862bb4ef4476ae73af7a620de8 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 10443983 |
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