Examining effect modification within propensity score methods
department or school: Medicine, Faculty of
department or school: Population and Public Health (SPPH), School of
data provider: DSpace
degree campus: UBCV
creator: Kafi, Mohammad Abdullah Heel
issued: 2021-05-04T18:09:42Z
issued: 2021
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: In epidemiological research, the association between treatment and outcome may vary across values of a third variable, termed an “effect modifier”. Studying effect modifiers can help to identify subgroups for which treatment is beneficial or harmful. Standard approaches to assessing the impact of an effect modifier include subgroup analysis or adding an interaction term between treatment and the effect modifier in the regression analysis. Various propensity score (PS) based methods, also have been proposed, but it is unclear whether the PS should be derived based on the full dataset or within subgroups, or how these PS’s should then be used (matching, weights, stratification, covariate adjustment). We explored the performance of various PS-based approaches using simulation to compare how well the approach works under various scenarios. We performed comprehensive Monte Carlo simulation studies considering various scenarios, plasmode simulation, and empirical data analysis to evaluate the performance of all possible combinations of effect modifier approaches within all propensity score methods for various scenarios. We found that, for various simulation scenarios, there were comparable differences among various effect modifier approaches. For example, under simulation scenarios such as the model with additivity, mild non-linearity, moderate non-additivity, the subgroup-specific PS approach produced higher bias than the overall PS approach. PS matching approaches performed better than PS weighting methods in the model’s scenarios with mild non-linearity, mild non-additivity. In Monte Carlo simulation with interaction, the useful interaction approach had more downward bias, lower mean squared error than the overall PS, and subgroup-specific approach, in various scenarios such as additive, mild-linearity, moderate non-linearity. Our findings demonstrate that, under different scenarios, different effect modifiers approaches can be performed better than others. Therefore, …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/78089
note: EXAMINING EFFECTMODIFICATION WITHINPROPENSITY SCORE METHODSbyMohammad Abdullah Heel KafiBSc, University of Dhaka, 2010MSc, University of Dhaka, 2012A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Population and Public Health)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)April 2021© Mohammad Abdullah Heel Kafi 2021The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:Examining Effect Modification within Propensity Score Meth-odssubmitted by Mohammad Abdullah Heel Kafi in partial fulfillmentof the requirements for the degree of Master of Science in Populationand Public Health.Examining Committee:Mohammad Ehsanul Karim, Assistant Professor, School of Populationand Public Health, UBCSupervisorHubert Wong, Associate Professor, School of Population and PublicHealth, UBCSupervisory Committee MemberWei Zhang, Assistant Professor, School of Population and Public Health,UBCSupervisory Committee MemberAbstractIn epidemiological research, the association between treatment and outcomemay vary across values of a third variable, termed an “effect modifier”.Studying effect modifiers can help to identify subgroups for which treat-ment is beneficial or harmful. Standard approaches to assessing the impactof an effect modifier include subgroup analysis or adding an interaction termbetween treatment and the effect modifier in the regression analysis. Var-ious propensity score (PS) based methods, also have been proposed, butit is unclear whether the PS should be derived based on the full datasetor within subgroups, or how these PS’s should then be used (matching,weights, stratification, covariate adjustment). We explored the performanceof various PS-based approaches using simulation to compare how well theapproach work under various scenarios. We performed comprehensive MonteCarlo simulation s…
has type: Thesis/Dissertation
date issued: 2021-11
is shown at: 10.14288/1.0397224
language: eng
degree discipline: Population and Public Health
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Karim, Mohammad Ehsanul
type: Text
identifier URI: http://hdl.handle.net/2429/78089