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International Journal of Epidemiology 2001;30:1379-1382
© International Epidemiological Association 2001

Theory and Methods

Regression models for unconstrained, partially or fully constrained continuation odds ratios

Stephen R Colea and Cande V Ananthb

a Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, USA.
b Section of Epidemiology and Biostatistics, Department of Obstetrics, Gynecology and Reproductive Sciences, UMDNJ—Robert Wood Johnson Medical School, NJ, USA.

Correspondence: Dr Stephen Cole, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St E-7139 Baltimore, MD 21205, USA. E-mail: scole{at}jhsph.edu

Abstract

Epidemiologists frequently encounter studies with ordered responses. Standard ordered response logit models, such as the continuation ratio model, constrain exposure to have a homogenous effect across thresholds of the ordered response. We demonstrate a method for fitting regression models for unconstrained, partially or fully constrained continuation odds ratios using a ‘person-threshold’ data set. For each subject, we create a separate record for each response threshold the subject is ‘at risk’ of passing and then apply standard binary logistic regression to estimate the continuation-ratio model. An example demonstrates the unconstrained, partially and fully constrained continuation-ratio model, while a small simulation study examines some properties of the proposed ‘person-threshold’ approach. Finally, we present a brief discussion of statistical software to implement the method.

Keywords Continuation-ratio model, epidemiological methods, odds ratio, ordered response

Accepted 3 May 2001


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