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Activity Number: 454
Type: Contributed
Date/Time: Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #312407 View Presentation
Title: Semiparametric Accelerate Failure Time Modeling for Clustered Failure Times from Stratified Sampling
Author(s): Sy Han Chiou*+ and Sangwook Kang and Jun Yan
Companies: University of Minnesota, Duluth and Yonsei University and University of Connecticut
Keywords: case cohort study ; efficient estimator ; induced smoothing ; sandwich variance estimator ; weighted estimating equations ; stratified sampling
Abstract:

Clustered failure time data arising from stratified sampling are often encountered in studies where it is desired to reduce both cost and sampling error. In such settings, semiparametric accelerated failure time (AFT) models have not been used as frequently as Cox relative risk models in practice due to lack of efficient and reliable computing routines for statistical inferences, with challenge rooted in non-smooth rank-based estimating functions. The recently proposed induced smoothing approach, which provides fast and accurate inferences for AFT models, is generalized to incorporate weights that can be applied to accommodate stratified sampling design. The estimators resulting from the induced smoothing weighted estimating equations are consistent and asymptotically normal with the same distribution as the estimators from the non-smooth estimating equations. The variance is estimated by computationally efficient sandwich estimators.


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