Format

Send to

Choose Destination
See comment in PubMed Commons below
J Am Stat Assoc. 2009 Sep 1;104(487):1192-1202.

Analyzing Length-biased Data with Semiparametric Transformation and Accelerated Failure Time Models.

Author information

1
Department of Biostatistics M. D. Anderson Cancer Center The University of Texas, Houston, TX 77030 yshen@mdanderson.org .

Abstract

Right-censored time-to-event data are often observed from a cohort of prevalent cases that are subject to length-biased sampling. Informative right censoring of data from the prevalent cohort within the population often makes it difficult to model risk factors on the unbiased failure times for the general population, because the observed failure times are length biased. In this paper, we consider two classes of flexible semiparametric models: the transformation models and the accelerated failure time models, to assess covariate effects on the population failure times by modeling the length-biased times. We develop unbiased estimating equation approaches to obtain the consistent estimators of the regression coefficients. Large sample properties for the estimators are derived. The methods are confirmed through simulations and illustrated by application to data from a study of a prevalent cohort of dementia patients.

PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for PubMed Central
    Loading ...
    Support Center