I am after some suggestions on what statistical analysis I can perform to show a before-and-after effect in a longitudinal electronic healthcare record (EHR). I have N number of EHRs, of varying sizes/time-spans. Each record has a history of recurrent disease records (for the one disease). To see whether a particular drug has had an effect on the disease outcome (duration before the next relapse), I have used time-gap recurrent cox regression.
However, I would now like to see whether the disease outcome (a series of remissions into relapses, good = long durations in between, bad = short durations in between) is immediately clear from the first prescription of a particular drug. In my head I imagine, taking all of the records (of vary time-span sizes -- very important to remember), and adjusting so every record overlaps when the drug of interest is first prescribed. Y axis is disease prevalence or risk, and x axis is time. From before the initial drug prescription event, disease prevalence/risk should be high, then after crossing the initial prescription time, disease prevalence/risk should drop. This would help demonstrate the efficacy of the drug.
Some points to remember: 1) Each medical record maybe unique in timespan. 2) The first prescription event of a particular drug will happen at different times across the record set. 3) Some records may have no medical events before the drug was prescribed (as all the diseases of interest feel after the drug prescription of interest). 4) The number of medical events either before or after the first prescription of the drug may be sparsely populated (making binning by time very difficult) or richly populated.
Is there a name for this kind of analysis? I am using R. Any suggestions are very welcome.
Have you considered a pre- and post- study design using time series and standardizing the intervention period for everybody as the time each individual starts using the therapy. Since each medical record maybe unique in time-span, I will suggest using person-period (e.g., person-day, person-month, etc.) as the unit of analysis.
Look like the data description fits into an interrupted time-series analysis
- Badges
- Science topic