Development of a Physicians’ Choice Model Using Mixed Logit With Random Prices for Drugs Case Study on Diabetes Type II

Abstract

This paper presents a first experiment with random generator of drug prices and a first simulation on physicians’ treatment choices (case on pharmacotherapies) for diabetes type II care. It also aims to compare the effects of the price variables according to public versus private health plans on physicians’ choices (Medicare versus commercial Health Plans). The base line model used is a Mixed Logit model with Random Price variables. A series of experiments with random parameters generations is designed with various sequences and number of draws. The model is tested on a real analytical dataset, extracted from the CDC physician survey (National Ambulatory Care Survey, NAMCS), for patients with diabetes type II without complications, for previous predictive econometrics with ENDEPUSresearch, Inc. The model uses a first drug choice set with three alternatives: oral agents only, combined therapies, no drug. The choice models introduce qualitative dependent variables and complement the series of cumulative logistic models per disease. The matlab code for the new specification test on the Independence of Irrelevant Alternatives at individual level is modified to fit this type of medical applications; first runs compare main parameters of a full choice set versus reduced choice sets of alternatives. It is planned to design more experiments for extended choice sets and widespread applications, in order to lead to user friendly tools for medical systems. The collaboration with Professor Jerry Hausman on the US market will help with use of results and new ways to adjust the reliability on the selection of alternatives; it may provide additional guidance to the algorithms used by professionals and for health policies.