Type of Document	Dissertation
Author	Munshi, Mahtab Rohinton
Author's Email Address	mahtab_munshi@hotmail.com
URN	etd-11112005-211710
Title	Impact of Missing Data on Building Prognostic Models and Summarizing Models Across Studies
Degree	Doctor of Philosophy
Department	Statistics, Department of
Advisory Committee	Advisor Name Title Daniel McGee, Sr. Committee Chair Isaac Eberstein Committee Member Myles Hollander Committee Member Somesh Chattopadhyay Committee Member Xufeng Niu Committee Member
Keywords	Coronary Heart Disease Stratified Model Summary Coefficients Maximum Likelihood Estimation Logistic Model Missing Data
Date of Defense	2005-09-09
Availability	unrestricted
Abstract We examine the impact of missing data in two settings, the development of prognostic models and the addition of new risk factors to existing risk functions. Most statistical software presently available perform complete case analysis, wherein only participants with known values for all of the characteristics being analyzed are included in model development. Missing data also impacts the summarization of evidence amongst multiple studies using meta-analytic techniques. As we progress in medical research, new covariates become available for studying various outcomes. While we want to investigate the influence of new factors on the outcome, we also do not want to discard the historical datasets that do not have information about these markers. Our research plan is to investigate dierent methods to estimate parameters for a model when some of the covariates are missing. These methods include likelihood based inference for the study-level coecients and likelihood based inference for the logistic model on the person-level data. We compare the results from our methods to the corresponding results from complete case analysis. We focus our empirical investigation on a historical example, the addition of high density lipoproteins to existing equations for predicting death due to coronary heart disease. We verify our methods through simulation studies on this example.
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