Work

Simulation of massive public health data by power polynomials

Year: 2012

Type: article

Abstract: Situations in which multiple outcomes and predictors of different distributional types are collected are becoming increasingly common in public health practice, and joint modeling of mixed types has been gaining popularity in recent years. Evaluation of various statistical techniques that have been developed for mixed data in simulated environments necessarily requires joint generation of multiple variables. Most massive public health data sets include different types of variables. For instance, in clustered or longitudinal designs, often multiple variables are measured or observed for each individual or at each occasion. This work is motivated by a need to jointly generate binary and possibly non-normal continuous variables. We illustrate the use of power polynomials to simulate multivariate mixed data on the basis of a real adolescent smoking study. We believe that our proposed technique for simulating such intensive data has the potential to be a handy methodological addition to public health researchers' toolkit.

Source: Statistics in Medicine

DOI: 10.1002/sim.5362

Authors: Hakan Demirtas, Donald Hedeker, Robin J. Mermelstein

Institution: University of Illinois Chicago

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute