Longitudinal analysis of healthcare costs: a case study of patients with major depressive disorder treated with duloxetine

Abstract

Objective:

To develop and apply a longitudinal model that adjusts for pre-treatment covariates to examine the trajectory of healthcare costs in duloxetine patients with major depressive disorder (MDD).

Methods:

Retrospective healthcare cost data from Thomson Reuters Marketscan® Database included 10,987 patients with MDD, aged 18–64, receiving duloxetine at low (<60?mg/day), standard (60?mg/day), or high (>60?mg/day) initial doses. A linear mixed-effects model for repeated measures used dose, month, and dose*month as fixed effects and patient (dose) as a random effect, and adjusted for demographics, comorbidities, body system disorders, and prior medication history. Model goodness-of-fit was evaluated with R². Rates of change (slopes) were estimated from the fitted model and differences in the cost trajectory among dosing cohorts were tested using the F-test. Bootstrapping and propensity score (PS) stratification were conducted to provide sensitivity analyses.

Results:

Main effects and covariates were all significant (p?R^2?=?0.43). The model revealed a significant increase in healthcare costs in the 6 months preceding and a significant decrease in the 6 months following duloxetine initiation for each initial dose cohort and the overall cohort (p?p?Limitations:

The analyses performed here were based on non-randomized, observational data, and thus subject to potential biases due to unmeasured confounding.

Conclusions:

Longitudinal models, compared with conventional mean-based methods, provide better opportunities to assess changes in cost trajectory patterns around the time of changes in medical treatment. In insured patients with MDD started on duloxetine, healthcare costs increased before duloxetine initiation, perhaps signaling a clinical deterioration that led to a change in treatment strategy. Healthcare costs then decreased following duloxetine initiation.