regression - Forecasting model for longitudinal data with multiple explanatory variables -

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i new stats, forgive me if didn't phrase question right.

subject 1: y ~ x1  x2  x3  x4  x5 @ t=1 subject 1: y ~ x1  x2  x3  x4  x5 @ t=2 subject 1: y ~ x1  x2  x3  x4  x5 @ t=3 subject 1: y ~ x1  x2  x3  x4  x5 @ t=4  subject 2: y ~ x1  x2  x3  x4  x5 @ t=1 subject 2: y ~ x1  x2  x3  x4  x5 @ t=2 subject 2: y ~ x1  x2  x3  x4  x5 @ t=3 subject 2: y ~ x1  x2  x3  x4  x5 @ t=4

i have longitudinal data set (repeated measures each subject taken 4 time periods) dichotomous response variable. wish build predictive model helps predict y @ t=5.

basically, need regression+time series forecasting model tells me variables significant in explaining variability in response, while accounting within subject correlation/dependence structure on time, , forecasts y or probability of y taking on 1 of 2 binary outcomes, @ time t=5, regressing on predictors until t=4.

i have feeler has mixed models/transition models. but, not sure how structure problem in tool r/sas , package/procedure use.

i believe requires incorporation of markov state transition models, accounting other explanatory variables.

any appreciated.


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