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Activity Number: 700
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #321425 View Presentation
Title: An Empirical Saddlepoint Approximation--Based Method for Smoothing Survival Functions Under Right Censoring
Author(s): Pratheepa Jeganathan* and Alexandre Trindade and Robert Paige
Companies: Texas Tech University and Texas Tech University and Missouri University of Science and Technology
Keywords: Smoothing survival function ; Kaplan-Meier estimator ; Moment generating function ; lifetime data with right censoring. ; tail-completion

The Kaplan-Meier (KM) estimator is a commonly used non-parametric procedure for estimating survival functions. However, KM only defines the approximate probability of observed failure times, and may not deliver a proper density function if the largest observation is right censored. In addition, existing smoothing methods based on KM also assume that the largest observation is not censored. To alleviate these issues, we devise a method for smoothing KM survival functions based on an empirical saddlepoint approximation. The method inverts the moment generating function (MGF) defined through a Riemann-Stieltjes integral of the empirical cumulative distribution function with KM weights and exponential right-tail completion. The performance of the methodology is examined in simulation studies, which demonstrates that the proposed empirical saddlepoint approximation method is faster and more accurate than existing methods for smoothing survival functions. The R scripts were written to implement the new method, and R is used for all the simulation studies.

Authors who are presenting talks have a * after their name.

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