Association models for clustered data with binary and continuous responses

Lanjia Lin, Dipankar Bandyopadhyay, Stuart R. Lipsitz, Debajyoti Sinha

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

We consider analysis of clustered data with mixed bivariate responses, i.e., where each member of the cluster has a binary and a continuous outcome. We propose a new bivariate random effects model that induces associations among the binary outcomes within a cluster, among the continuous outcomes within a cluster, between a binary outcome and a continuous outcome from different subjects within a cluster, as well as the direct association between the binary and continuous outcomes within the same subject. For the ease of interpretations of the regression effects, the marginal model of the binary response probability integrated over the random effects preserves the logistic form and the marginal expectation of the continuous response preserves the linear form. We implement maximum likelihood estimation of our model parameters using standard software such as PROC NLMIXED of SAS. Our simulation study demonstrates the robustness of our method with respect to the misspecification of the regression model as well as the random effects model. We illustrate our methodology by analyzing a developmental toxicity study of ethylene glycol in mice.

Original languageEnglish (US)
Pages (from-to)287-293
Number of pages7
JournalBiometrics
Volume66
Issue number1
DOIs
StatePublished - Mar 2010

Keywords

  • Bivariate binary and continuous responses
  • Bridge distribution
  • Logit link
  • Probability integral transformation

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