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Article
Nonparametric Multi-State Representations of Survival and Longitudinal Data with Measurement Error
Statistics in Medicine (2012)
  • Bo Hu, Cleveland Clinic Lerner Research Institute
  • Liang Li, Cleveland Clinic Lerner Research Institute
  • Xiao-Feng Wang, Cleveland Clinic Lerner Research Institute
  • Tom Greeene, University of Utah School of Medicine
Abstract
This paper proposes a nonparametric procedure to describe the progression of longitudinal cohorts over time from a population averaged perspective, leading to multi-state probability curves with the states defined jointly by survival and longitudinal outcomes measured with error. To account for the challenges of informative dropout and nonlinear shapes of the longitudinal trajectories, a bias corrected penalized spline regression is applied to estimate the unobserved longitudinal trajectory for each subject. The multi-state probability curves are then estimated based on the survival data and the estimated longitudinal trajectories. Simulation Extrapolation (SIMEX) is further used to reduce the estimation bias caused by the randomness of the estimated trajectories. A bootstrap test is developed to compare multi-state probability curves between groups. We present theoretical justification of the estimation procedure along with a simulation study to demonstrate finite sample performance. The procedure is illustrated by data from the African American Study of Kidney Disease and Hypertension, and it can be widely applied in longitudinal studies.
Keywords
  • Multi-state representations; penalized spline; SIMEX
Disciplines
Publication Date
February, 2012
Citation Information
Bo Hu, Liang Li, Xiao-Feng Wang and Tom Greeene. "Nonparametric Multi-State Representations of Survival and Longitudinal Data with Measurement Error" Statistics in Medicine (2012)
Available at: http://works.bepress.com/wang/17/