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I am conducting a study on neurological diseased patients. I am looking into possible factors related to time-to-dementia. Costs associated with neurological testing allow us to assess all patients in the prospective cohort on an annual basis. We have (can afford) 4 fixed follow up visits for all patients - baseline, 1-year, 2-year, 3-year. We assess dementia at each visit. My data look roughly as follows:

  • Baseline: 0/140 patients with dementia.
  • 1-year: 10/120 patients with dementia.
  • 2-year: 20/100 patients with dementia.
  • 3-year: 30/80 patients with dementia.

A lot of censoring is occurring year-to-year. Further, I don't know when patients actually develop/convert to dementia. Just know it occurred between:

  • (0,1]
  • (1,2]
  • (2,3]

A sensible looking reference on "fixed interval" survival data seemed to suggest that I could analyze these data (after manipulating the dataset) using GLM (binomial distribution, and complementary log-log link). Also compared to Cox PH using Efron method to account for ties. Thoughts on this approach?

http://www.ics.uci.edu/~vqnguyen/stat255/Lecture13.pdf

If I want a "regression" approach to investigate time to dementia what might be possible alternatives to the above model(s)?

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