I have some questions concerning a retrospective study on the effectiveness of Paxlovid in high risk patients:
Briefly:
Included patients: 180,351
Patients who received Paxlovid ( a five days treatment course): 4747
Outcome: composite of severe COVID-19 or COVID-19 specific mortality
Number of events: 942
Number of variables in the multivariable cox PH model:14 + Paxlovid that was modelled was a time-dependent variable.
Patients were followed for 28 days
Paxlovid group: 39 events in the 4737 patients
Non-paxlovid group: 903 events in the 175614 patients
HR for treatment with Paxlovid from the multivariable cox PH model 0.54 ( 95% CI , 0.39-0.75)
-
Can sparse data bias be an issue in this study? ( It can still be an issue even when sample size is large but with adjustment for many variables)
-
Is it justified to model Paxlovid treatment as time-dependent variable? the authors state that " Paxlovid was modeled as time-dependent variable, allowing subjects to transfer from one exposure group to another during follow-up" , but patients who received first dose of Paxlovid more than 5 days since positive sars-cov-2 test were excluded, so basically a patient is “allowed to transfer” between exposure groups only in the first five days since positive test . ( I suppose that a product term time*Paxlovid was added to the model but I am not sure of this and the authors did not specify how exactly this was done )