Abstract
Abstract We consider the problem of interpreting categorical regression models, such as the polytomous logistic model, the continuation‐ratio model, the stereotype model, and the cumulative‐odds model. We present a method to convert categorical regression coefficients into estimates of standardized fitted probabilities, probability differences and probability ratios. We use a delta‐method approach to estimate standard errors. We then present a small simulation study to compare different transforms for setting confidence limits, and provide an illustration of our approach in an observational study of drug therapy of polymyositis.
Keywords
Categorical variableRegressionStatisticsRegression analysisComputer scienceEconometricsMathematics
MeSH Terms
AlgorithmsAnti-Inflammatory AgentsAutoantibodiesAzathioprineConfidence IntervalsConfounding FactorsEpidemiologicData InterpretationStatisticalHumansImmunosuppressive AgentsModelsStatisticalOdds RatioPolymyositisPrednisoneRegression AnalysisStatisticsNonparametricTreatment Outcome
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Publication Info
- Year
- 1995
- Type
- article
- Volume
- 14
- Issue
- 19
- Pages
- 2131-2141
- Citations
- 48
- Access
- Closed
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Cite This
Marshall M. Joffe,
Sander Greenland
(1995).
Standardized estimates from categorical regression models.
Statistics in Medicine
, 14
(19)
, 2131-2141.
https://doi.org/10.1002/sim.4780141907
Identifiers
- DOI
- 10.1002/sim.4780141907
- PMID
- 8552892