This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #306855
Title: A Consistent Estimator of the Absolute Deviation Between Predicted and Observed Survival Functions
Author(s): Matthias Schmid*+
Companies: University of Erlangen-Nuremberg
Address: Waldstrasse 6, Erlangen, D-91054, Germany
Keywords: survival analysis ; prognostic value ; model misspecification ; prediction error

Distance-based measures of prediction error have become a popular tool to assess and compare the prognostic value of survival models. The talk is focused on the properties of an estimator suggested by Schemper and Henderson (Biometrics 2000, "SH estimator"), which quantifies the time-dependent absolute distance between predicted and observed survival functions of the observations in a data set. It is shown that the SH estimator is not robust w.r.t. misspecification of the working model, i.e., the estimator may be biased if the assumptions needed for a consistent estimation of the survival functions are not met. To correct for the misspecification bias, an alternative estimator of the absolute distance between survival functions is suggested. The new estimator, which is based on inverse-probability-of-censoring weights, proves to be consistent for the true mean absolute prediction error.

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