# Logistic Regression with dependent observations

I have a dataset that contains 100 different patients over 5 year’s period. Every patient is examined each month with regard to particular illness and marked as healthy or ill (0 or 1). Every person appears 60 times in my sample (5 * 12 = 60).

Every month patient provides A = Average blood pressure in that month, B = Average daily exercise hours and C = Average number of Cigarettes smoked in that month.

The layout of the dataset is as follows:

ID (Unique Patient Identifier)
Month (1 to 60)
A (Average blood pressure in that month)
B (Average daily exercise hours)
C (Average number of Cigarettes smoked in that month)
Ill (Yes, No)


I was thinking of using Logistic Regression which uses information from last three months and gives a probability for patient to be flagged as Ill in next 2 months.

My problem is that logistic regression assumes that observations are independent whereas in my case they are obviously not.

What should I do? Should I use something like GEE or GLMM or something else?

• – boscovich Dec 4 '12 at 19:35
• Thanks... Any practical advices i.e. what would you do if you were to analyse above data? – user13467 Dec 5 '12 at 17:56