Use of two-segmented logistic regression to estimate change-points in epidemiologic studies

Roberto Pastor, Eliseo Guallar

Research output: Contribution to journalArticle

Abstract

In many epidemiologic data, the dose-response relation between a continuous exposure and the risk of disease abruptly changes when the exposure variable reaches an unknown threshold level, the so-called change- point. Although several methods are available for dose-response assessment with dichotomous outcomes, none of them provide inferential procedures to estimate change-points. In this paper, we describe a two-segmented logistic regression model, in which the linear term associated with a continuous exposure in standard logistic regression is replaced by a two-segmented polynomial function with unknown change-point, which is also estimated. A modified, iteratively reweighted least squares algorithm is presented to obtain parameter estimates and confidence intervals, and the performance of this model is explored through simulation. Finally, a two-segmented logistic regression model is applied to a case-control study of the association of alcohol intake with the risk of myocardial infarction and compared with alternative analyses. The ability of two-segmented logistic regression to estimate and provide inferences for the location of change-points and for the magnitude of other parameters of effect will make this model a useful complement to other methods of dose-response analysis in epidemiologic studies.

Original languageEnglish (US)
Pages (from-to)631-642
Number of pages12
JournalAmerican Journal of Epidemiology
Volume148
Issue number7
StatePublished - Oct 1 1998
Externally publishedYes

Fingerprint

Epidemiologic Studies
Logistic Models
Least-Squares Analysis
Case-Control Studies
Myocardial Infarction
Alcohols
Confidence Intervals

Keywords

  • Case-control studies
  • Epidemiologic methods
  • Logistic models
  • Risk assessment

ASJC Scopus subject areas

  • Epidemiology

Cite this

Use of two-segmented logistic regression to estimate change-points in epidemiologic studies. / Pastor, Roberto; Guallar, Eliseo.

In: American Journal of Epidemiology, Vol. 148, No. 7, 01.10.1998, p. 631-642.

Research output: Contribution to journalArticle

@article{21113314e43f40de8afeab5fda76ec49,
title = "Use of two-segmented logistic regression to estimate change-points in epidemiologic studies",
abstract = "In many epidemiologic data, the dose-response relation between a continuous exposure and the risk of disease abruptly changes when the exposure variable reaches an unknown threshold level, the so-called change- point. Although several methods are available for dose-response assessment with dichotomous outcomes, none of them provide inferential procedures to estimate change-points. In this paper, we describe a two-segmented logistic regression model, in which the linear term associated with a continuous exposure in standard logistic regression is replaced by a two-segmented polynomial function with unknown change-point, which is also estimated. A modified, iteratively reweighted least squares algorithm is presented to obtain parameter estimates and confidence intervals, and the performance of this model is explored through simulation. Finally, a two-segmented logistic regression model is applied to a case-control study of the association of alcohol intake with the risk of myocardial infarction and compared with alternative analyses. The ability of two-segmented logistic regression to estimate and provide inferences for the location of change-points and for the magnitude of other parameters of effect will make this model a useful complement to other methods of dose-response analysis in epidemiologic studies.",
keywords = "Case-control studies, Epidemiologic methods, Logistic models, Risk assessment",
author = "Roberto Pastor and Eliseo Guallar",
year = "1998",
month = "10",
day = "1",
language = "English (US)",
volume = "148",
pages = "631--642",
journal = "American Journal of Epidemiology",
issn = "0002-9262",
publisher = "Oxford University Press",
number = "7",

}

TY - JOUR

T1 - Use of two-segmented logistic regression to estimate change-points in epidemiologic studies

AU - Pastor, Roberto

AU - Guallar, Eliseo

PY - 1998/10/1

Y1 - 1998/10/1

N2 - In many epidemiologic data, the dose-response relation between a continuous exposure and the risk of disease abruptly changes when the exposure variable reaches an unknown threshold level, the so-called change- point. Although several methods are available for dose-response assessment with dichotomous outcomes, none of them provide inferential procedures to estimate change-points. In this paper, we describe a two-segmented logistic regression model, in which the linear term associated with a continuous exposure in standard logistic regression is replaced by a two-segmented polynomial function with unknown change-point, which is also estimated. A modified, iteratively reweighted least squares algorithm is presented to obtain parameter estimates and confidence intervals, and the performance of this model is explored through simulation. Finally, a two-segmented logistic regression model is applied to a case-control study of the association of alcohol intake with the risk of myocardial infarction and compared with alternative analyses. The ability of two-segmented logistic regression to estimate and provide inferences for the location of change-points and for the magnitude of other parameters of effect will make this model a useful complement to other methods of dose-response analysis in epidemiologic studies.

AB - In many epidemiologic data, the dose-response relation between a continuous exposure and the risk of disease abruptly changes when the exposure variable reaches an unknown threshold level, the so-called change- point. Although several methods are available for dose-response assessment with dichotomous outcomes, none of them provide inferential procedures to estimate change-points. In this paper, we describe a two-segmented logistic regression model, in which the linear term associated with a continuous exposure in standard logistic regression is replaced by a two-segmented polynomial function with unknown change-point, which is also estimated. A modified, iteratively reweighted least squares algorithm is presented to obtain parameter estimates and confidence intervals, and the performance of this model is explored through simulation. Finally, a two-segmented logistic regression model is applied to a case-control study of the association of alcohol intake with the risk of myocardial infarction and compared with alternative analyses. The ability of two-segmented logistic regression to estimate and provide inferences for the location of change-points and for the magnitude of other parameters of effect will make this model a useful complement to other methods of dose-response analysis in epidemiologic studies.

KW - Case-control studies

KW - Epidemiologic methods

KW - Logistic models

KW - Risk assessment

UR - http://www.scopus.com/inward/record.url?scp=0032191938&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032191938&partnerID=8YFLogxK

M3 - Article

VL - 148

SP - 631

EP - 642

JO - American Journal of Epidemiology

JF - American Journal of Epidemiology

SN - 0002-9262

IS - 7

ER -