المؤلفون: Frédérique Thomas, Tarik Benmarhnia, Basile Chaix, Ruben Brondeel, Antoine Lewin

المصدر: Epidemiology. 29:87-95

مصطلحات موضوعية: Adult, Male, Patient Dropouts, Epidemiology, Computer science, media_common.quotation_subject, Inference, 01 natural sciences, Body Mass Index, 010104 statistics & probability, 03 medical and health sciences, Sex Factors, 0302 clinical medicine, Bias, Outcome Assessment, Health Care, Statistics, medicine, Humans, Computer Simulation, Attrition, Longitudinal Studies, Obesity, 030212 general & internal medicine, 0101 mathematics, Dropout (neural networks), Aged, media_common, Selection bias, Inverse probability weighting, Age Factors, Middle Aged, Missing data, medicine.disease, Outcome (probability), Educational Status, Female, France, Cohort study

الوصف: Background Most longitudinal studies do not address potential selection biases due to selective attrition. Using empirical data and simulating additional attrition, we investigated the effectiveness of common approaches to handle missing outcome data from attrition in the association between individual education level and change in body mass index (BMI). Methods Using data from the two waves of the French RECORD Cohort Study (N = 7,172), we first examined how inverse probability weighting (IPW) and multiple imputation handled missing outcome data from attrition in the observed data (stage 1). Second, simulating additional missing data in BMI at follow-up under various missing-at-random scenarios, we quantified the impact of attrition and assessed how multiple imputation performed compared to complete case analysis and to a perfectly specified IPW model as a gold standard (stage 2). Results With the observed data in stage 1, we found an inverse association between individual education and change in BMI, with complete case analysis, as well as with IPW and multiple imputation. When we simulated additional attrition under a missing-at-random pattern (stage 2), the bias increased with the magnitude of selective attrition, and multiple imputation was useless to address it. Conclusions Our simulations revealed that selective attrition in the outcome heavily biased the association of interest. The present article contributes to raising awareness that for missing outcome data, multiple imputation does not do better than complete case analysis. More effort is thus needed during the design phase to understand attrition mechanisms by collecting information on the reasons for dropout.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c987ef9e6683a6b06de1c562e39bb3c8Test
https://doi.org/10.1097/ede.0000000000000755Test

المؤلفون: Menglan Pang, Tibor Schuster, Maria Eberg, Robert W. Platt, Kristian B. Filion

المصدر: Epidemiology (Cambridge, Mass.)

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.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df9b7f862bb4ef4476ae73af7a620de8Test
https://doi.org/10.1097/ede.0000000000000487Test