Statistics and Its Interface

Volume 4 (2011)

Number 4

An estimation method of marginal treatment effects on correlated longitudinal and survival outcomes

Pages: 499 – 509



Qing Pan (Department of Statistics, George Washington University, Washington, D.C., U.S.A.)

Grace Y. Yi (Department of Statistics & Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada)


This paper concerns treatment effects on correlated longitudinal and time to event processes. The marginal mean of the longitudinal outcome in the presence of event occurrence is often of interest from clinical and epidemiological perspectives. When the probability of the event is treatmentdependent, differences between treatment-specific longitudinal outcome means are usually not constant over time. In this paper, we propose a measure to quantify treatment effects using time-varying differences in longitudinal outcome means, which accounts for the constantly changing population composition due to event occurrences. Generalized linear mixed models (GLMM) and proportional hazards (PH) models are employed to construct the proposed measure. The proposed method is applied to analyze the motivating data arising from the study of weight loss in the Diabetes Prevention Program where weights after diabetes occurrence are systematically different from diabetes-free weights.


correlated longitudinal and survival processes, generalized linear mixed model, marginal treatment effects, piecewise constant hazard, proportional hazards model

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