"Nonparametric Multi-state Analysis of Longitudinal and Survival Data "
November 30, 2010 @ 3:30 pm - 4:30 pm
Liang Li, Ph.D., Cleveland Clinic
Location: Blockley Hall - Room 701
TITLE: Nonparametric Multi-state Analysis of Longitudinal and Survival
ABSTRACT: Most methodology for joint modeling of longitudinal and survival data approach the problem from a subject-specific perspective via shared parameter model or its variants. This paper proposes a new nonparametric procedure that produces population averaged inference, leading to multi-state probability curves with the states defined jointly by longitudinal and survival data. To account for informative censoring and flexible nonlinear shapes of the longitudinal profiles, a bias corrected penalized spline regression is applied to estimate the longitudinal profile for each subject. The probability curves are then estimated sequentially based on the observed survival data and the smoothed longitudinal profiles. Simulation Extrapolation (SIMEX) method is employed to correct the deconvolution bias caused by the measurement error of the longitudinal data. A bootstrap test procedure is developed to compare multi-state probability curves between different groups. We present theoretical justification of the procedure along with simulation results. The procedure was applied to data from the African American Studies of Kidney Disease and Hypotension (AASK), a multi-center clinical trial. This is joint work with Bo Hu (Cleveland Clinic) and Tom Greene (University of Utah).