EPID 766: Analysis of Longitudinal Data from ... - NCSU Statistics

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CHAPTER 1 Epid 766, D. Zhang 1.7 Methods for analyzing longitudinal data 1. Two-stage: summarize each subject’s outcome and regress the summary statistics on one-time covariates. Especially useful for continuous longitudinal data. However, this method is getting out-dated since mixed model approach can do the same even better. 2. Mixed (effects) model approach: model fixed effects and random effects; use random effect to model correlation. 3. Generalized estimating equation (GEE) approach: model the dependence of marginal mean on covariates. Correlation is not a main interest. Particularly good for discrete data. 4. transition models: use history as covariates. Good for prediction of future response using history. Graduate Summer Session in Epidemiology Slide 30
CHAPTER 1 Epid 766, D. Zhang 1.8 Two-stage method for analyzing longitudinal data • Outcome (continuous): yi1, ..., yini measured at ti1, ..., tini, one-time covariates: xi1, ..., xip. • Two-stage analysis is conducted as follows: 1. Stage 1: Get summary statistics from subject i’s data: yi1, ..., yini. For example, use mean ¯yi = (yi1 + · · · + yini)/ni or fit a linear regression for each subject: yij = bi0 + bi1tij + ǫij, and get estimates � bi0, � bi1 of bi0 and bi1. Here we assume that subject i’s true response at time tij is given by bi0 + bi1tij, a straight line. Suppose t = 0 is the baseline, then bi0 is subject i’s true response at baseline and bi1 is subject i’s change rate of Graduate Summer Session in Epidemiology Slide 31
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