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Activity Number: 322 - Time-To-Event Models in Complex Observational Studies
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #300417 Presentation
Title: Goodness-of-Fit Tests for the Linear Transformation Models with Interval-Censored Data
Author(s): Soutrik Mandal* and Suojin Wang and Samiran Sinha
Companies: National Cancer Institute and Texas A&M University and Texas A&M University
Keywords: Interval-censored; Goodness-of-fit; Transformation models

Interval-censored time-to-event data are widely observed in medical studies. Semiparametric transformation models are often used to analyze this type of data. The linear transformation model contains popular models like the Cox proportional hazards and proportional odds models as special cases. A misspecified model leads to invalid inference. We propose a new class of omnibus supremum tests for the goodness-of-fit of the assumed model. We derive the analytical expression of the test statistics under the null hypothesis and assess it through a Monte Carlo method. We compare the performance of our method with other existing methods through simulation studies and real data example.

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

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