Consider a retrospective study to be planned with
- 200 patients with
- 5 risk factors (such as age, disease score = disease severity, hypertension etc.) which may affect (a) outcome as such (older patients may have a higher risk for unfavourable outcome even independent from intervention) and (b) success of an intervention (see next point)
- 2 interventions. One special thing in this retrospective analysis is that risk factors will have influenced the kind of intervention, because people think (albeit not know) that e.g. higher age should favour intervetion A vs B, i.e. there issome interaction between risk factors and intervention.
- 4 outcomes (success vs. treatment failure and adverse events) which, if necessary, can be taken together as one binary outcome (favourable/not favourable)
The aim of the study is to find guidance, which intervention is better, likely depending on an individual patient's risk factors.
I feel this should be a quite common study question, but somehow I am stuck. I'd like to do something beyond simply explorativly analysing each and any combination of the above variables. What way of analysis is appropriate? (as opposed to copy-paste code that technically can be executed)
- GLM: will analyse risk factors and outcome, but is it appropriate to simply add intervention as an additional independent variable to the GLM model (outcome.favourable ~ intervention + risk.factor.1 + risk.factor.2 ...)? Can this be modelled with interactions - if so, how to set them properly and to to avoid too many elements in the modele valuation?
- Cox proportional hazards model: would (as bonus) include follow-up time, but also here I am not sure how to incorporate treatment vs risk factor effets..., at least based on examples I found so far?
- I am happy for better suggestions
I prefer a solution that can be done in R.