The primary goal of our project is to develop and evaluate statistical methods for neurological studies. The secondary goal is to disseminate these methods by writing computer programs, specifically SAS Macros. The statistical problems to be investigated in this proposal arise from the NIH-funded projects in which the Principal Investigator has been participating. Although these problems have frequently arisen in other fields as well, satisfactory solutions have not, as of yet, been found.
Specific Aim 1 is to develop methods for the Cox proportional hazards model when covariates are partially missing. Specifically, we propose to develop a likelihood-based imputation method with or without specifying functional form of the distribution for missing covariate under various missing mechanisms.
Specific Aim 2 is to develop methods for handling missing data in repeated measures analysis under various missing mechanisms.
Specific Aim 3 is to develop a joint estimating equation method for handling nonignorably missing outcomes in bivariate binary data arising from Northern Manhattan Stroke Study.
| Lee, Hye-Seung; Cho Paik, Myunghee; Lee, Joseph H (2009) Estimating a multivariate familial correlation using joint models for canonical correlations: application to memory score analysis from familial Hispanic Alzheimer's disease study. Biometrics 65:463-9 |
| Paik, Myunghee Cho; Wang, Cuiling (2009) HANDLING MISSING DATA BY DELETING COMPLETELY OBSERVED RECORDS. J Stat Plan Inference 139:2341-2350 |
| Lee, Hye-Seung; Paik, Myunghee Cho; Lee, Joseph H (2008) Genotype-adjusted familial correlation analysis using three generalized estimating equations. Stat Med 27:5471-83 |
| Cho Paik, Myunghee (2004) Nonignorable missingness in matched case-control data analyses. Biometrics 60:306-14 |
| Lin, I F; Paik, M C (2001) Matched case-control data analysis with selection bias. Biometrics 57:1106-12 |
| Paik, M C; Sacco, R; Lin, I F (2000) Bivariate binary data analysis with nonignorably missing outcomes. Biometrics 56:1145-56 |
