TY - JOUR
T1 - Association models for clustered data with binary and continuous responses
AU - Lin, Lanjia
AU - Bandyopadhyay, Dipankar
AU - Lipsitz, Stuart R.
AU - Sinha, Debajyoti
PY - 2010/3
Y1 - 2010/3
N2 - 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.
AB - 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.
KW - Bivariate binary and continuous responses
KW - Bridge distribution
KW - Logit link
KW - Probability integral transformation
UR - http://www.scopus.com/inward/record.url?scp=77949750822&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77949750822&partnerID=8YFLogxK
U2 - 10.1111/j.1541-0420.2008.01232.x
DO - 10.1111/j.1541-0420.2008.01232.x
M3 - Article
C2 - 19432772
AN - SCOPUS:77949750822
SN - 0006-341X
VL - 66
SP - 287
EP - 293
JO - Biometrics
JF - Biometrics
IS - 1
ER -