JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 415
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
Sponsor: SSC
Abstract - #304596
Title: Nonparametric Estimation of the Survival Function for Prevalent and Incident Cases Under Stationary Incidence
Author(s): Pierre-Jerome Bergeron*+ and Masoud Asgharian
Companies: University of Ottawa and McGill University
Address: 585 King Edward Avenue, Ottawa, ON, K1N 6N5, Canada
Keywords: nonparametric ; length-biased sampling ; right-censoring ; survival analysis ; EM algorithm

In epidemiological studies, subject who experience disease onset after recruitment are called incident cases, and subjects whose onset occured prior to the study are prevalent cases. The latter tend to survive longer than the former due to sampling bias. The Kaplan-Meier (KM) estimator is the best known estimator of the survival function with right-censoring, and a simple modification allows for estimation with general left-truncation, conditionally adjusting for the bias. However, when dealing with prevalent cases under stationary incidence, the data are length-biased. The nonparametric MLE of the survival function that corrects for length-bias is obtained through an EM algorithm which provides efficient estimation of the survival from onset that accounts for dependent censoring and known truncation distribution. The approach is completely different than that of the KM estimator. In large longitudinal studies, one may observe both incident and prevalent cases, and there is no simple way to combine both estimators to use all the data. We present a product-limit approach, obtain its asymptotic properties and illustrate the method with data from Canadian Study of Health and Aging.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.