This dissertation addresses regression models with missing covariate data. These methods are shown to be significant to public health research since they enable researchers to use a wider spectrum of data. Unbiased estimating equations are the focus of this dissertation, predominantly semiparametric methods utilized to solve for regression parameters in the presence of missing covariate data. The first aim of this dissertation is to evaluate the properties of an efficient score, an inverse probability weighted estimating equation approach, for logistic regression in a two-phase design. Simulation studies showed that the efficient score is more efficient than two other pseudo-likelihood methods when the correlation between the missing covariate and its surrogate is high.
The second aim of this dissertation is to develop a methodology for left truncated covariate data with a binary outcome. To address this problem, we proposed two methods, a likelihood-based approach and an estimating equation approach, to estimate the coefficients and their standard errors for a regression model with a left truncated covariate. The estimating equation technique is close to completion, and once solved should be the most efficient method. The likelihood-based method is compared to standard methods of filling in the truncated values with the lower threshold value or using only the nontruncated values. Simulation studies demonstrated that the likelihood-based method has the best variance correction and moderate bias correction. The application of this method is illustrated in a sepsis study conducted at the University of Pittsburgh.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-02032005-152546 |
| Date | 15 February 2005 |
| Creators | D'Angelo, Gina Marie |
| Contributors | Bret Goodpaster, Jong-Hyeon Jeong, Gong Tang, H. Samuel Wieand, Lisa A. Weissfeld |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-02032005-152546/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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