期刊
BIOMETRICS
卷 63, 期 2, 页码 610-617出版社
BLACKWELL PUBLISHING
DOI: 10.1111/j.1541-0420.2006.00722.x
关键词
logistic model; longitudinal data; multivariate discrete data; Poisson model; random effects
资金
- NIDA NIH HHS [DA07903] Funding Source: Medline
- NIMH NIH HHS [P30 MH58107, P30 MH058107, P30 MH058107-119002, 1 R01 MH60213, U10 MH057615, R01 MH060213] Funding Source: Medline
A binomial outcome is a count s of the number of successes out of the total number of independent trials n = s + f, where f is a count of the failures. The n are random variables not fixed by design in many studies. Joint modeling of (s, f) can provide additional insight into the science and into the probability pi of success that cannot be directly incorporated by the logistic regression model. Observations where n = 0 are excluded from the binomial analysis yet may be important to understanding how pi is influenced by covariates. Correlation between s and f may exist and be of direct interest. We propose Bayesian multivariate Poisson models for the bivariate response (s, f), correlated through random effects. We extend our models to the analysis of longitudinal and multivariate longitudinal binomial outcomes. Our methodology was motivated by two disparate examples, one from teratology and one from an HIV tertiary intervention study.
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