Hello, I’m in the early stages of developing a model to identify patient, provider, and encounter-level factors significantly associated with issuance of a specific type of prescription. Here’s some background: The outcome is binary (patient received prescription within 2 days of encounter/did not receive prescription). This is a longitudinal dataset with roughly 14000 observations (distinct encounters) that occurred over a 4 year period among ~4350 patients. The outcome is positive (patient received a prescription) in about 15% of encounters. For patients with multiple encounters, time between encounters is not evenly spaced. I anticipate developing a GLMM model using PROC GLIMMIX to account for repeated measures among patients. ~40% of patients had only 1 encounter in the study period, ~20% had 2 encounters, ~10% had 3 encounters, ~7% had 4 encounters, and the remaining patients had between 5 and 60 encounters (patient counts get into single digits at 17 encounters and above). It is this last point (the distribution of repeat encounters) that I’m wondering about. That is, I’m wondering if the high proportion of patients with only a single encounter will be an issue in a model designed for repeated observations. I’m also wondering if the skewed distribution in repeat observations might cause problems. To date, I’ve been unable to find any examples/commentary on modelling data with such characteristics. This may be a non-issue. However, I thought I’d ask before I started building the model in earnest, so I can avoid any pitfalls at the outset. Any thoughts/advice/comments are appreciated. Thanks, Stuart
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