المؤلفون: Issa J Dahabreh, Sarah E. Robertson, Miguel A. Hernán

المصدر: Epidemiology. 30:807-812

مصطلحات موضوعية: Epidemiology, Inverse probability weighting, Statistics as Topic, Nonparametric statistics, Estimator, 01 natural sciences, Outcome (probability), Weighting, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Inverse probability, Statistics, Parametric model, Humans, 030212 general & internal medicine, 0101 mathematics, Probability, Randomized Controlled Trials as Topic, Curse of dimensionality, Mathematics

الوصف: When generalizing inferences from a randomized trial to a target population, two classes of estimators are used: g-formula estimators that depend on modeling the conditional outcome mean among trial participants and inverse probability (IP) weighting estimators that depend on modeling the probability of participation in the trial. In this article, we take a closer look at the relation between these two classes of estimators. We propose IP weighting estimators that combine models for the probability of trial participation and the probability of treatment among trial participants. We show that, when all models are estimated using nonparametric frequency methods, these estimators are finite-sample equivalent to the g-formula estimator. We argue for the use of augmented IP weighting (doubly robust) generalizability estimators when nonparametric estimation is infeasible due to the curse of dimensionality, and examine the finite-sample behavior of different estimators using parametric models in a simulation study.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e4ca208e266838097cb72764016f021Test
https://doi.org/10.1097/ede.0000000000001097Test

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: Duncan C. Thomas

المصدر: Epidemiology. 28:470-478

مصطلحات موضوعية: Oncology, medicine.medical_specialty, education.field_of_study, Epidemiology, business.industry, Colorectal cancer, Inverse probability weighting, Confounding, Population, Cancer, medicine.disease, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, Internal medicine, Statistics, Covariate, Medicine, 030212 general & internal medicine, Family history, Risk assessment, business, education

الوصف: Screening behavior depends on previous screening history and family members' behaviors, which can act as both confounders and intermediate variables on a causal pathway from screening to disease risk. Conventional analyses that adjust for these variables can lead to incorrect inferences about the causal effect of screening if high-risk individuals are more likely to be screened. Analyzing the data in a manner that treats screening as randomized conditional on covariates allows causal parameters to be estimated; inverse probability weighting based on propensity of exposure scores is one such method considered here. I simulated family data under plausible models for the underlying disease process and for screening behavior to assess the performance of alternative methods of analysis and whether a targeted screening approach based on individuals' risk factors would lead to a greater reduction in cancer incidence in the population than a uniform screening policy. Simulation results indicate that there can be a substantial underestimation of the effect of screening on subsequent cancer risk when using conventional analysis approaches, which is avoided by using inverse probability weighting. A large case-control study of colonoscopy and colorectal cancer from Germany shows a strong protective effect of screening, but inverse probability weighting makes this effect even stronger. Targeted screening approaches based on either fixed risk factors or family history yield somewhat greater reductions in cancer incidence with fewer screens needed to prevent one cancer than population-wide approaches, but the differences may not be large enough to justify the additional effort required. See video abstract at, http://links.lww.com/EDE/B207Test.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::cc0652b488599971bccb52ca5a1454cbTest
https://doi.org/10.1097/ede.0000000000000668Test

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

المؤلفون: Schmidt, Amand F, Klungel, Olaf H, Groenwold, Rolf H H, Sub Pharmacotherapy, Theoretical, Pharmacoepidemiology and Clinical Pharmacology

المصدر: Epidemiology, 27(1), 133. Lippincott Williams and Wilkins
Epidemiology, 27(1), 133. NLM (Medline)

مصطلحات موضوعية: Epidemiology, Marginal structural model, Logistic regression, Research Support, 01 natural sciences, Risk Assessment, EVENTS, 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Statistics, Outcome Assessment, Health Care, Journal Article, INSTRUMENTAL VARIABLES, Humans, Comparative Study, Computer Simulation, CORONARY-HEART-DISEASE, 030212 general & internal medicine, 0101 mathematics, Non-U.S. Gov't, OBSERVATIONAL DATA, MARGINAL STRUCTURAL MODELS, Mathematics, Probability, Models, Statistical, Inverse probability weighting, Research Support, Non-U.S. Gov't, Confounding, RISK SCORES, Confounding Factors, Epidemiologic, Confidence interval, Regression, Nominal level, Observational Studies as Topic, PROPENSITY SCORE, Logistic Models, TRIAL GENERALIZABILITY, Research Design, Data Interpretation, Statistical, CAUSAL INFERENCE, Propensity score matching, SIMULATION

الوصف: BACKGROUND: Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. METHODS: We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. RESULTS: At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. CONCLUSION: In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

وصف الملف: image/pdf; application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29649a042cf77eca156d0bca277f5214Test
https://hdl.handle.net/1874/329750Test