First - very new to Statistics; about half through a basic biostats book and an R book.
I have a set of data where I"m trying to see if there is a correlation between a medication and weight gain. The dataset:
- ~2200 patients
- A list of their primary diagnoses, weight, date's of visit, and whether they were on medication at that time.
- Each patient may be on or off the medication at various points, though I have limited the data set to patients receiving at least 90 days of medication.
- Different patients are on / off the medication for varying # of days. Also, each patient has recorded visit for a varying amount of total days (though at least 90 based on 90 day min. medication limit from #2).
My main goals are:
- Is this medication correlated with weight gain?
- If so / not - does it depend on a certain threshold length of constant use?
- (Eventually) do either of the above two correlations change based on the patients diagnoses (Some pt's will be grouped together)
My question is - really, where should I begin? What types of analyses should I be doing? I'm willing to put in the reading / work to figure it out; but I'm not quite sure where to look. As stated in intro, I have - some - experience with R, and am expecting to carry out my in depth analysis in that.
So far, I have been looking at Average of change in weight / days on medication; so basically giving each patient a Wt. Change per day of med. Im planning on adding wt. change per day while off medication and all patient variables, but this illuminates what I need something more advanced for --
How do I account for trends occurring independent of medication. A thought - say a patient started a diet, and has been steadily losing 1lb per week prior to medicaiton. If that medication is started, it is possible the overall trend will overshadow the medication effect. In this case - is there a practical way to account for long term trends, without examining each patients data individually?