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Estimating marginal and incremental effects in the analysis of medical expenditure panel data using marginalized two-part random-effects generalized Gamma models: Evidence from China healthcare cost data

TitleEstimating marginal and incremental effects in the analysis of medical expenditure panel data using marginalized two-part random-effects generalized Gamma models: Evidence from China healthcare cost data
Publication TypeJournal Article
Year of Publication2017
AuthorsZhang, B, Liu, W, Hu, Y
JournalStat Methods Med Res
Pagination962280217690770
Date PublishedJan 01
ISBN Number0962-2802
Accession Number28139170
Keywordshealthcare expenditures, marginal effects, marginal models, panel data, random-effects models, two-part models
Abstract

Conditional two-part random-effects models have been proposed for the analysis of healthcare cost panel data that contain both zero costs from the non-users of healthcare facilities and positive costs from the users. These models have been extended to accommodate more flexible data structures when using the generalized Gamma distribution to model the positive healthcare expenditures. However, a major drawback with the extended model, which is inherited from the conditional models, is that it is fairly difficult to make direct marginal inference with respect to overall healthcare costs that includes both zeros and non-zeros, or even on positive healthcare costs. In this article, we first propose two types of marginalized two-part random-effects generalized Gamma models (m2RGGMs): Type I m2RGGMs for the inference on positive healthcare costs and Type II m2RGGMs for the inference on overall healthcare costs. Then, the concepts of marginal effect and incremental effect of a covariate on overall and positive healthcare costs are introduced, and estimation of these effects is carefully discussed. Especially, we derive the variance estimates of these effects by following the delta methods and Taylor series approximations for the purpose of making marginal inference. Parameter estimates of Type I and Type II m2RGGMs are obtained through maximum likelihood estimation. An empirical analysis of longitudinal healthcare costs collected in the China Health and Nutrition Survey is conducted using the proposed methodologies.