Estimation of heterogeneous average treatment effect: Panel data correlated random coefficients model with polychotomous endogenous treatments

We estimated the treatment effects of biologic disease modifying anti-rheumatoid drugs (DMARDs) on the quarterly total health-care expenditure, while controlling non-random assignment to treatment (endogeneity) and allowing heterogeneity in treatment effects. The structural parameters, heterogeneous (ATE), and homogeneous (ATE1) average treatment effects were defined as the impact of treatment on quarterly total health-care expenditure, if patients are randomly assigned to biologic DMARDs.;A retrospective cohort was selected from California Medicaid paid claims between 01/01/1999 and 12/31/2005. Non-overlapping quarters were created from pharmacy claims for biologic (adalimumab and etanercept) and standard (leflunomide, hydroxychloroquine and sulfasalazine) DMARDs. Final sample included 23,297 observations on 5,239 individual patients.;A fixed-effects panel data correlated random coefficients (CRC) model allowed for heterogeneity in treatment effects. Endogeneity was controlled by adding a generalized residual function constructed based on Lee's (1983) approach. Selection choice model was varied from the multinomial, nested, and mixed logit.;Controlling endogeneity significantly increased ATE1 for both biologic DMARDs, as compared to naive fixed-effects (baseline standard DMARD). Nested-logit based ATE1 was higher as compared to the multinomial-logit ATE1. Allowing for unobserved heterogeneity resulted in the ATE of adalimumab to decrease under the multinomial-logit corrected model, while an increase was observed in the nested-logit corrected model. In case of ATE for etanercept, an increase was observed under both the above mentioned models as compared to ATE1.;The results point out the need to control for time-varying endogeneity in panel data models. When treatment effects are heterogeneous and especially when treatment selection is a discrete choice set, the specification of latent index model matters. Sorting on gains is an important source of bias in medical outcomes and in this study, it manifested in terms of large differences in the magnitude of ATE1 and ATE parameters, which questions homogeneity assumption.;The methodological issues addressed in this study impact our understanding of the cost effects of drug treatment. Models need to be realistic to mimic real life clinical decisions to inform important drug coverage decisions. Panel data CRC model with endogeneity correction is one such tool to assess comparative effectiveness using an observational study design for expenditure outcomes.