test Browse by Author Names Browse by Titles of Works Browse by Subjects of Works Browse by Issue Dates of Works
       

Advanced Search
Home   
 
Browse   
Communities
& Collections
  
Issue Date   
Author   
Title   
Subject   
 
Sign on to:   
Receive email
updates
  
My Account
authorized users
  
Edit Profile   
 
Help   
About T-Space   

T-Space at The University of Toronto Libraries >
School of Graduate Studies - Theses >
Doctoral >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/32855

Title: Variable Selection and Adjustment in Relation to Propensity Scores and Prognostic Scores: From Single-level to Multilevel Data
Authors: Yu, Bing
Advisor: Hong, Guanglei
Department: Human Development and Applied Psychology
Keywords: education
psychology
statistics
Issue Date: 31-Aug-2012
Abstract: Through three sets of simulations, this dissertation evaluates the effectiveness of alternative approaches to causal inference that make use of propensity scores. In the setting of single-level data, the first study examines the relative performance of (a) three variable selection methods for propensity score models (i.e., including all the treatment predictors, including all the outcome predictors, or including confounders), and (b) three adjustment methods in outcome models (i.e., adjusting for the propensity score only, adjusting for the propensity score in combination with the prognostic score, and adjusting for the propensity score in combination with strong outcome-predictive covariates). The second study tests the robustness of the alternative approaches under a range of model misspecifications, including omitted covariates, omitted nonlinear terms, and omitted interaction terms in a propensity score model, a prognostic score model, or an outcome model. The third study extends the evaluation to multilevel data by additionally examining another dimension unique to multilevel data. The study compares random intercept and slopes models, random intercept models, and single-level models for the propensity score and prognostic score estimations. The impact of omitting cluster-level covariates is also examined under each type of model specification. Evaluation criteria include bias, precision, mean squared error, remaining sample size after stratification, and confidence interval coverage percentage. The main findings are: (1) in general, adjustment methods in outcome models have more important consequences than variable selection for propensity score models for bias reduction, precision, and MSE; (2) the robustness against model misspecifications under alternative approaches depends on the type of misspecifications; (3) multilevel propensity score models show advantages over their single-level counterparts especially when combined with prognostic score adjustment; (4) omitting cluster-level information is not highly consequential once the multilevel structure has been accounted for by using multilevel outcome models.
URI: http://hdl.handle.net/1807/32855
Appears in Collections:Doctoral

Files in This Item:

File Description SizeFormat
Yu_Bing_201206_PhD_thesis.pdf4.36 MBAdobe PDF
View/Open

Items in T-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

uoft