Joint regression analysis of mixed-type outcome data via efficient scores

Scott Marchese and Guoqing Diao

Computational Statistics & Data Analysis, 2018, vol. 125, issue C, 156-170

Abstract: Joint analysis of multivariate outcomes composed of mixed data types (continuous, count, binary, survival, etc.) induces special complexity in model specification and analysis. When the scientific question of interest involves a joint effect of covariate(s) of interest on the set of outcome variables, specifying a full probability model may be infeasible, undesirably complex, or computationally intractable. A flexible method to estimate and conduct inference on such joint effects is presented which accounts for correlation among the outcomes without needing to explicitly specify their joint distribution. Simulation studies and an analysis of health care data illustrate the approach and its operating characteristics vis-à-vis other methods.

Keywords: Bonferroni correction; Efficient score; Generalized estimating equations; Mixed-type data; Multiplier bootstrap (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:125:y:2018:i:c:p:156-170

DOI: 10.1016/j.csda.2018.02.008

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