TY - JOUR

T1 - Robustness of the latent variable model for correlated binary data

AU - Tan, Ming

AU - Qu, Yinsheng

AU - Sunil Rao, J.

PY - 1999/3

Y1 - 1999/3

N2 - The marginal regression model offers a useful alternative to conditional approaches to analyzing binary data (Liang, Zeger, and Qaqish, 1992, Journal of the Royal Statistical Society, Series B 54, 3-40). Instead of modelling the binary data directly as do Liang and Zeger (1986, Biometrika 73, 13-22), the parametric marginal regression model developed by Qu et al. (1992, Biometrics 48, 1095-1102) assumes that there is an underlying multivariate normal vector that gives rise to the observed correlated binary outcomes. Although this parametric approach provides a flexible way to model different within-cluster correlation structures and does not restrict the parameter space, it is of interest to know how robust the parameter estimates are with respect to choices of the latent distribution. We first extend the latent modelling to include multivariate t-distributed latent vectors and assess the robustness in this class of distributions. Then we show through a simulation that the parameter estimates are robust with respect to the latent distribution even if latent distribution is skewed. In addition to this empirical evidence for robustness, we show through the iterative algorithm that the robustness of the regression coefficients with respect to misspecifications of covariance structure in Liang and Zeger's model in fact indicates robustness with respect to underlying distributional assumptions of the latent vector in the latent variable model.

AB - The marginal regression model offers a useful alternative to conditional approaches to analyzing binary data (Liang, Zeger, and Qaqish, 1992, Journal of the Royal Statistical Society, Series B 54, 3-40). Instead of modelling the binary data directly as do Liang and Zeger (1986, Biometrika 73, 13-22), the parametric marginal regression model developed by Qu et al. (1992, Biometrics 48, 1095-1102) assumes that there is an underlying multivariate normal vector that gives rise to the observed correlated binary outcomes. Although this parametric approach provides a flexible way to model different within-cluster correlation structures and does not restrict the parameter space, it is of interest to know how robust the parameter estimates are with respect to choices of the latent distribution. We first extend the latent modelling to include multivariate t-distributed latent vectors and assess the robustness in this class of distributions. Then we show through a simulation that the parameter estimates are robust with respect to the latent distribution even if latent distribution is skewed. In addition to this empirical evidence for robustness, we show through the iterative algorithm that the robustness of the regression coefficients with respect to misspecifications of covariance structure in Liang and Zeger's model in fact indicates robustness with respect to underlying distributional assumptions of the latent vector in the latent variable model.

KW - GEE

KW - Latent variable model

KW - Sensitivity

KW - Simulation

UR - http://www.scopus.com/inward/record.url?scp=0032929197&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032929197&partnerID=8YFLogxK

U2 - 10.1111/j.0006-341X.1999.00258.x

DO - 10.1111/j.0006-341X.1999.00258.x

M3 - Article

C2 - 11318164

AN - SCOPUS:0032929197

SN - 0006-341X

VL - 55

SP - 258

EP - 263

JO - Biometrics

JF - Biometrics

IS - 1

ER -