Jump to ContentJump to Main Navigation
Applied Longitudinal Data AnalysisModeling Change and Event Occurrence$
Users without a subscription are not able to see the full content.

Judith D. Singer and John B. Willett

Print publication date: 2003

Print ISBN-13: 9780195152968

Published to Oxford Scholarship Online: September 2009

DOI: 10.1093/acprof:oso/9780195152968.001.0001

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2019. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in OSO for personal use (for details see www.oxfordscholarship.com/page/privacy-policy).date: 20 February 2019

Extending the Discrete-Time Hazard Model

Extending the Discrete-Time Hazard Model

Chapter:
(p.407) 12 Extending the Discrete-Time Hazard Model
Source:
Applied Longitudinal Data Analysis
Author(s):

Judith D. Singer

John B. Willett

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195152968.003.0012

The basic discrete-time hazard model invokes assumptions about the population that may, or may not, hold in practice. This chapter examines its assumptions, demonstrating how to evaluate their tenability and relax their constraints when appropriate. Section 12.1 revisits the original specification for the main effect of TIME in the discrete-time hazard model—which, in the previous chapter, was specified using a system of time indicators—and compares it with other specifications that constrain the shape of the baseline hazard function in different ways. Section 12.2 re-examines the logit link that used to relate hazard to predictors in the previous chapter and compare it to an alternative—the complementary log-log link—which yields an important correspondence with the continuous time hazard models that we will describe subsequently. Section 12.3 deals with time-varying predictors, showing how to include them in the discrete-time model and discussing inferential difficulties that their inclusion raises. Sections 12.4 through 12.6, examines three important assumptions embedded in the discrete-time hazard model—the linear additivity assumption (“all predictors operate only as main effects”); the proportionality assumption (“the effects of each predictor are constant over time”); and the no unobserved heterogeneity assumption (“population hazard depends only on predictor values”). Section 12.7 concludes by describing analytic strategies for “residual” analysis to accompany model fitting.

Keywords:   time, discrete-time hazard model, linear additivity, residual analysis

Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .