I'm modeling longitudinal patient level health records data which will have the following structure:

\begin{equation*} \text{Prob}(Y_{it}=1 \text{ at time } t \text{ and member } i)=\beta_{i0} +\beta_{i1}x_{i,t-1} + \beta_{i2}x_{i,t-2} + ... + \beta_{ik}x_{i,t-k} + \text{additional covariates for patient age, diagnoses, etc.} \end{equation*}

where Y is an inpatient hospitalization indicator variable for time period t and patient i, and the x's measure frequency of home nursing visits during prior time periods.

The predictor variables are significantly correlated at the patient level. I'm considering a logistic regression, however the strong correlation between the x's makes things a bit more complicated.

Is a time series model appropriate here, or would a multilevel repeated measures model be better (say using proc mixed in SAS with a random intercept for each member)?

  • It seems like the random effects model is more practicable here and is probably a more appropriate choice. Whether or not the collinearity presents a problem depends on what your goals are with the analysis. – Macro Feb 22 '13 at 18:19
  • Thank you, I've decided on a random effects model as well. I reasoned frequency of visits are similar more due to member (or possibly provider) level characteristics rather than freq. of visits on day t actually being influenced by day t-1. – RobertF Feb 25 '13 at 15:45

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