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The trend odds model for ordinal data.
Stat Med 2013 Jun; 32(13):2250-2261
Ordinal data appear in a wide variety of scientific fields. These data are often analyzed using ordinal logistic regression models that assume proportional odds. When this assumption is not met, it may be possible to capture the lack of proportionality using a constrained structural relationship between the odds and the cut-points of the ordinal values. We consider a trend odds version of this constrained model, wherein the odds parameter increases or decreases in a monotonic manner across the cut-points. We demonstrate algebraically and graphically how this model is related to latent logistic, normal, and exponential distributions. In particular, we find that scale changes in these potential latent distributions are consistent with the trend odds assumption, with the logistic and exponential distributions having odds that increase in a linear or nearly linear fashion. We show how to fit this model using SAS Proc NLMIXED and perform simulations under proportional odds and trend odds processes. We find that the added complexity of the trend odds model gives improved power over the proportional odds model when there are moderate to severe departures from proportionality. A hypothetical data set is used to illustrate the interpretation of the trend odds model, and we apply this model to a swine influenza example wherein the proportional odds assumption appears to be violated.
Analytical-processes; Models; Statistical-analysis; Mathematical-models; Author Keywords: non-proportional odds; constrained cumulative odds; influenza; latent distributions; logistic distribution
Ana W. Capuano, 600 South Paulina, Suite 1023B, Chicago, IL, 60612
Issue of Publication
Statistics in Medicine
University of Iowa
Page last reviewed: November 8, 2019
Content source: National Institute for Occupational Safety and Health Education and Information Division