MARGINAL MODELS FOR BINARY LONGITUDINAL DATA WITH DROPOUTS

Authors

  • SALEHIN K. CHOWDHURY
  • SANJOY K. SINHA

Keywords:

Generalized estimating equation, Inverse probability weight, Longitudinal data, Marginal model, Missing response.

Abstract

In this paper, we propose and explore a set of weighted generalized estimating equations for
fitting regression models to longitudinal binary responses when there are dropouts. Under
a given missing data mechanism, the proposed method provides unbiased estimators of
the regression parameters and the association parameters. Simulations were carried out to
study the robustness properties of the proposed method under both correctly specified and
misspecified correlation structures. The method is also illustrated in an analysis of some
actual incomplete longitudinal data on cigarette smoking trends, which were used to study
coronary artery development in young adults.

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Issue

Vol. 47 No. 1 (2013)

Section

Articles