Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 260 - Statistical Methods for Handling Imperfect Data Subject to Missing and/or Mismeasurement
Type: Invited
Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
Sponsor: SSC (Statistical Society of Canada)
Abstract #314487
Title: Analysis of Length-Biased Survival Data with Misclassified Covariate
Author(s): Hua Shen*
Companies: University of Calgary
Keywords: Survival Data; Misclassification

Studies of chronic disease often sample individuals subject to conditions on an event time of interest requiring subjects to have survived to the point of recruitment. Such condition results in length-biased samples. Misclassification in categorical covariate often arise in such settings while validation data is absent and only surrogates are available. In such studies designed to evaluate the covariate effect on event time, we also need to deal with the fact that the distribution of the latent categorical covariate is affected by the sampling mechanism. We use a latent variable model in such setting and conduct the parameter estimation via an expectation-maximization algorithm. The performance of the proposed method is demonstrated in simulation studies. An application is given to the stimulating study on breast cancer.

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

Back to the full JSM 2021 program