I was asked by a colleague to examine a data set that looked at pre-operative frailty as a predictor of post-operative quality of life, but I am struggling to identify a suitable model due to the nature of the input variables.
The outcome is a RAND 36-item health survey questionnaire which collapses down to eight categories. For example; one category, “physical functioning”, is the mean of 10 individual questions, set against a three point scale; “pain”, is the mean of two questions, set against a five point scale. All said and done, you are left with a score for each category that can take defined values between 0 and 100.
Predictors of these values are frailty (measured as a the proportion of 11 defined co-morbidities that a patient presents with), sex and age. Patients are classified as frail when they have three or more conditions (Frailty index = 3/11, i.e. > 0.27)
The publications I’ve seen using similar data appear not to address the underlying data or justify the model selected. My own path has arrived at using a proportional odds ordinal logistic model (rms::orm) where I treat the outcome as continuous and frailty as ordinal.
fit <- orm(physical functioning ~ scored(frailty) + rcs(age, 4) + sex, data)
My question is, am I approaching this correctly? Any thoughts, suggestions or direction when facing this type of data would be greatly welcomed.