Binary Logistic Regression Using Survival Analysis

9 Pages Posted: 6 Sep 2010

Date Written: September 6, 2010

Abstract

Survival analysis problems have elsewhere been recast as problems in logistic regression, after the event times were grouped into intervals. Here we discuss the opposite connection: how binary logistic regression can be viewed fruitfully as a special case of accelerated failure time models in survival analysis. In the corresponding survival analysis setting, all data is either left- or right-censored, and all observation times are the same (taken as t=1). Using this connection, researchers modeling binary outcome data can go beyond the routinely used logit and probit models, to conveniently include the distributions routinely available within survival analysis programs, such as gaussian and exponential. Large data sets may be fruitfully analyzed using this approach with a view to choosing the best available distribution. Our ideas are not new, and our aim is simply to be accessible to a broad community of nonspecialist nonstatisticians. We demonstrate our ideas with numerical examples in R.

Keywords: Logistic Regression, Discrete Choice, Binary Response Variable, Survival Analysis, Left-Censored, Right-Censored

JEL Classification: C10, C25, C41

Suggested Citation

Chatterjee, Devlina and Chatterjee, Anindya, Binary Logistic Regression Using Survival Analysis (September 6, 2010). Available at SSRN: https://ssrn.com/abstract=1672759 or http://dx.doi.org/10.2139/ssrn.1672759

Devlina Chatterjee (Contact Author)

IIT Kanpur ( email )

Room No. 211
IME Department
Kanpur, UT Uttar Pradesh 208016
India
91-512-2596960 (Phone)
91-512-2597553 (Fax)

HOME PAGE: http://www.iitk.ac.in

Anindya Chatterjee

Indian Institute of Technology ( email )

Kharagpur
IIT Khragpur
Kharagpur, IN 721302
India

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