To evaluate whether pharmacist consultation has a favorable impact on drug therapy that improves patient survival, we consider a model for survival analysis that include multiple time-dependent indicator variables to predict the effect of pharmacy intervention on mortality rate. However, in the PC intervention sites, many patients switched within the model after initial randomization. We needed a strategy which takes account of intervention actually received. The number of new prescriptions filled at PC intervention sites was introduced into Cox Models as intervention variables in order to measure the extent to which the patient was exposed to each model of pharmaceutical care. The baseline of comparison is the Control Group, which was not exposed to any formal PC intervention.
Selection bias is then a potentially serious problem.
In such settings, one can use the Cox model for inference about the relative risk in such cases by using multiple time-dependent covariate structure to specify a continuous functional form for the relative hazard as a function of time since switch. We conclude that the time dependent model predicts better than our previous time-fixed model. The interventions performed by specially trained pharmacists included targeted patient education, systematic patient monitoring and feedback, behavior modification, and regular communication with patients' physicians to allow early intervention for drug related problems. Prescription drug costs and total medical costs were analyzed by comparing claims data from 188 patients enrolled in the intervention program at the three participating pharmacies to data from 400 matched control patients at five non- participating pharmacies. All of the pharmacies were part of the same retail chain, and all patients used the same health care insurer.