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

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CHAPTER 1 Epid 766, D. Zhang If we have only one observation Yi for each subject from a sample of n patients, then we can’t separate σ2 e and σ2 b . Although we can use data Y1, Y2, ..., Yn to make inference on µ, we can’t make any inference on σ2 b . However, if we have repeated (or longitudinal) measurements Yij of blood pressure for each subjects, then Yij = bi + eij. Now, it is possible to make inference about all quantities µ, σ 2 b and σ 2 e. 5. A longitudinal study provides more evidence for possible causal interpretation. Graduate Summer Session in Epidemiology Slide 24
CHAPTER 1 Epid 766, D. Zhang 1.6 Challenges in analyzing longitudinal data Key assumptions in a classical regression model: There is only one observation of response per subject, =⇒ responses are independent to each other. For example, when y = cholesterol level, yi = β0 + β1age i + β2sexi + ǫi. However, the observations from the same subject in a longitudinal study tend to be more similar to each other than those observations from other subjects, =⇒ responses (from the same subjects) are not independent any more. Although, the observations from different subjects are still independent. What happens if we treat observations as independent (i.e., ignore the correlation)? 1. In general, the estimation of the associations (regression coefficients) of the outcome and covariates is valid. Graduate Summer Session in Epidemiology Slide 25
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