Type of Document Dissertation Author Miller, Gregory Charles Author's Email Address firstname.lastname@example.org URN etd-07082011-143105 Title Investigating the Use of Mortality Data as a Surrogate for Morbidity Data Degree Doctor of Philosophy Department Statistics, Department of Advisory Committee
Advisor Name Title Daniel McGee Committee Co-Chair Myles Hollander Committee Co-Chair Jinfeng Zhang Committee Member Wei Wu Committee Member Myra Hurt University Representative Keywords
- Risk Models
- Cox Proportional Hazards
Date of Defense 2011-06-07 Availability unrestricted AbstractWe are interested in differences between risk models based on Coronary Heart Disease (CHD) incidence, or morbidity, compared to risk models based on CHD death. Risk models based on morbidity have been developed based on the Framingham Heart Study, while the European SCORE project developed a risk model for CHD death. Our goal is to determine whether these two developed models differ in treatment decisions concerning patient heart health.
We begin by reviewing recent metrics in surrogate variables and prognostic model performance. We then conduct bootstrap hypotheses tests between two Cox proportional hazards models using Framingham data, one with incidence as a response, and one with death as a response, and find that the coefficients differ for the age covariate, but find no significant differences for the other risk factors.
To understand how surrogacy can be applied to our case, where the surrogate variable is nested within the true variable of interest, we examine models based on a composite event compared to models based on singleton events.
We also conduct a simulation, simulating times to a CHD incidence and time from CHD incidence to CHD death, censoring at 25 years to represent the end of a study. We compare a Cox model with death response with a Cox model based on incidence using bootstrapped confidence intervals, and find that age and systolic blood pressure have differences with their covariates. We continue the simulation by using Net Reclassification Index (NRI) to evaluate the treatment decision performance of the two models, and find that the two models do not perform significantly different in correctly classifying events, if the decisions are based on the risk ranks of the individuals. As long as the relative order of patients' risks is preserved across different risk models, treatment decisions based on classifying an upper specified percent as high risk will not be significantly different.
We conclude the dissertation with statements about future methods for approaching our question.
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