A note on marginalization of regression parameters from mixed models of binary outcomes

Donald Hedeker, Stephen H. C. du Toit, Hakan Demirtas and Robert D. Gibbons

Biometrics, 2018, vol. 74, issue 1, 354-361

Abstract: This article discusses marginalization of the regression parameters in mixed models for correlated binary outcomes. As is well known, the regression parameters in such models have the “subject†specific†(SS) or conditional interpretation, in contrast to the “population†averaged†(PA) or marginal estimates that represent the unconditional covariate effects. We describe an approach using numerical quadrature to obtain PA estimates from their SS counterparts in models with multiple random effects. Standard errors for the PA estimates are derived using the delta method. We illustrate our proposed method using data from a smoking cessation study in which a binary outcome (smoking, Y/N) was measured longitudinally. We compare our estimates to those obtained using GEE and marginalized multilevel models, and present results from a simulation study.

Date: 2018
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