One difficulty in regression analysis for longitudinal data is that the outcomes are often
missing in a non-ignorable way (Little & Rubin, 1987). Likelihood based approaches to
deal with non-ignorable missing outcomes can be divided into selection models and pattern
mixture models based on the way the joint distribution of the outcome and the missing-data
indicators is partitioned. One new approach from each of these two classes of models is
proposed. In the first approach, a normal copula-based selection model is constructed to
combine the distribution of the outcome of interest and that of the missing-data indicators
given the covariates. Parameters in the model are estimated by a pseudo maximum likelihood
method (Gong & Samaniego, 1981). In the second approach, a pseudo maximum likelihood
method introduced by Gourieroux et al. (1984) is used to estimate the identifiable parameters
in a pattern mixture model. This procedure provides consistent estimators when the mean
structure is correctly specified for each pattern, with further information on the variance
structure giving an efficient estimator. A Hausman type test (Hausman, 1978) of model
misspecification is also developed for model simplification to improve efficiency. Separate
simulations are carried out to assess the performance of the two approaches, followed by
applications to real data sets from an epidemiological cohort study investigating dementia,
including Alzheimer's disease.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-04162004-232213 |
| Date | 21 April 2004 |
| Creators | Shen, Changyu |
| Contributors | Lisa A. Weissfeld, Howard E. Rockette, Sati Mazumdar, Gong Tang, Mary Ganguli, Hiroko H. Dodge |
| 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-04162004-232213/ |
| 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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