A common problem in biomedical research is to describe a response (Y) as a function of explanatory variables (X). When observations are independent, generalized linear models (GLM's) including linear, logistic, log-linear and proportional hazards models are well developed for this purpose. When observations are correlated, current techniques are inadequate, particularly for discrete and non-normal outcomes. This study will develop regression methods for correlated data which arise in longitudinal and time series studies. In longitudinal studies, a few observations are made on each of many subjects. Repeated valves for an individual are correlated. In time series studies, inferences about the relationship of Y and X are made from a single longer set of correlated observations. By developing extensions of GLM's, the methods will be useful for a variety of discrete and continuous responses common to biomedical and particularly infectious disease research. For longitudinal studies, I will develop transitional, marginal and random effects (mixed) models. These will be used to analyze longitudinal data from the Multicenter AIDS Cohort Study (MACS), the Haiti HIV Study (HHS) and a study of infectious diseases and Vitamin A deficiency in Indonesian children (ICS). For the time series case, parameter and observation driven models (Cox, 1981) will be studied. These models will be used to estimate trends and make short-term projections of AIDS incidence by risk group and state in the U.S. and of hepatitis incidence by county in the State of Pennsylvania.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
First Independent Research Support & Transition (FIRST) Awards (R29)
Project #
5R29AI025529-05
Application #
3454452
Study Section
Special Emphasis Panel (SSS (G))
Project Start
1988-02-01
Project End
1993-01-31
Budget Start
1992-02-01
Budget End
1993-01-31
Support Year
5
Fiscal Year
1992
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Type
Schools of Public Health
DUNS #
045911138
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Katz, J (1995) Sample-size implications for population-based cluster surveys of nutritional status. Am J Clin Nutr 61:155-60
Zeger, S L; Diggle, P J (1994) Semiparametric models for longitudinal data with application to CD4 cell numbers in HIV seroconverters. Biometrics 50:689-99
Katz, J; Zeger, S; Liang, K Y (1994) Appropriate statistical methods to account for similarities in binary outcomes between fellow eyes. Invest Ophthalmol Vis Sci 35:2461-5
Katz, J; Zeger, S L (1994) Estimation of design effects in cluster surveys. Ann Epidemiol 4:295-301
Katz, J; Carey, V J; Zeger, S L et al. (1993) Estimation of design effects and diarrhea clustering within households and villages. Am J Epidemiol 138:994-1006
Katz, J; Zeger, S L; West Jr, K P et al. (1993) Clustering of xerophthalmia within households and villages. Int J Epidemiol 22:709-15
Wilcox, L S; Chu, S Y; Eaker, E D et al. (1991) Risk factors for regret after tubal sterilization: 5 years of follow-up in a prospective study. Fertil Steril 55:927-33
Sommer, A; Zeger, S L (1991) On estimating efficacy from clinical trials. Stat Med 10:45-52
Schwartz, J; Zeger, S (1990) Passive smoking, air pollution, and acute respiratory symptoms in a diary study of student nurses. Am Rev Respir Dis 141:62-7
Zeger, S L; See, L C; Diggle, P J (1989) Statistical methods for monitoring the AIDS epidemic. Stat Med 8:3-21

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