Survey Methodology
Firth’s penalized likelihood for proportional hazards regressions for complex surveys

by Pushpal K. MukhopadhyayNote 1

  • Release date: December 15, 2020
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Abstract

This article proposes a weight scaling method for Firth’s penalized likelihood for proportional hazards regression models. The method derives a relationship between the penalized likelihood that uses scaled weights and the penalized likelihood that uses unscaled weights, and it shows that the penalized likelihood that uses scaled weights have some desirable properties. A simulation study indicates that the penalized likelihood using scaled weights produces smaller biases in point estimates and standard errors than the biases produced by the penalized likelihood using unscaled weights. The weighted penalized likelihood is applied to estimate hazard rates for heart attacks by using a public-use data set from the National Health and Epidemiology Followup Study (NHEFS). SAS® statements to estimate hazard rates using data from complex surveys are given in the appendix.

Key Words:      Monotone likelihood; Delete-one jackknife; Weight scaling.

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How to cite

Mukhopadhyay, P.K. (2020). Firth’s penalized likelihood for proportional hazards regressions for complex surveys. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 46, No. 2. Paper available at https://www150.statcan.gc.ca/n1/pub/12-001-x/2020002/article/00004-eng.htm

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ISSN : 1492-0921

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Published by authority of the Minister responsible for Statistics Canada.

© Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2020

Use of this publication is governed by the Statistics Canada Open Licence Agreement.

Catalogue No. 12-001-X

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Table of contents

Abstract - Article main page
Section 1. Introduction
Section 2. Weight scaling
Section 3. Applications in complex surveys
Section 4. Summary
Acknowledgements
Appendix 1
Appendix 2
References

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