Estimating medical costs from a transition model

TL;DR

This paper introduces a flexible, regression-based framework for estimating the net present value of medical costs over time, accounting for censoring, heteroscedasticity, and covariates in longitudinal health data.

Contribution

It presents a novel, general approach to estimate healthcare costs from incomplete longitudinal data, integrating covariates and addressing data complexities.

Findings

The proposed estimator accommodates censoring, heteroscedasticity, and skewness.

It effectively incorporates patient covariates into cost estimation.

The method offers a flexible alternative to existing cost estimation techniques.

Abstract

Nonparametric estimators of the mean total cost have been proposed in a variety of settings. In clinical trials it is generally impractical to follow up patients until all have responded, and therefore censoring of patient outcomes and total cost will occur in practice. We describe a general longitudinal framework in which costs emanate from two streams, during sojourn in health states and in transition from one health state to another. We consider estimation of net present value for expenditures incurred over a finite time horizon from medical cost data that might be incompletely ascertained in some patients. Because patient specific demographic and clinical characteristics would influence total cost, we use a regression model to incorporate covariates. We discuss similarities and differences between our net present value estimator and other widely used estimators of total medical costs. Our model can accommodate heteroscedasticity, skewness and censoring in cost data and provides a flexible approach to analyses of health care cost.