Journal article
Fitting stratified proportional odds models by amalgamating conditional likelihoods
Statistics in medicine, v 27(24), pp 4950-4971
30 Oct 2008
PMID: 18618428
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Classical methods for fitting a varying intercept logistic regression model to stratified data are based on the conditional likelihood principle to eliminate the stratum-specific nuisance parameters. When the outcome variable has multiple ordered categories, a natural choice for the outcome model is a stratified proportional odds or cumulative logit model. However, classical conditioning techniques do not apply to the general
K
-category cumulative logit model (
K
> 2) with varying stratum-specific intercepts as there is no reduction due to suffciency; the nuisance parameters remain in the conditional likelihood. We propose a methodology to fit stratified proportional odds model by amalgamating conditional likelihoods obtained from all possible binary collapsing of the ordinal scale. The method allows for categorical and continuous covariates in a general regression framework. We provide a robust sandwich estimate of the variance of the proposed estimator. For binary exposures, we show equivalence of our approach to the estimators already proposed in the literature. The proposed recipe can be implemented very easily in standard software. We illustrate the methods via three real data examples related to biomedical research. Simulation results comparing the proposed method with a random effects model on the stratification parameters are also furnished.
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Details
- Title
- Fitting stratified proportional odds models by amalgamating conditional likelihoods
- Creators
- Bhramar Mukherjee - Department of Biostatistics, University of Michigan, Ann Arbor, MI 48103Jaeil Ahn - Department of Biostatistics, University of Michigan, Ann Arbor, MI 48103Ivy Liu - School of Mathematics, Statistics, and Computer Science, Victoria University of Wellington, Wellington, New ZealandPaul J Rathouz - Department of Health Studies, University of Chicago, Chicago, Illinois 60637Brisa N Sánchez - Department of Biostatistics, University of Michigan, Ann Arbor, MI 48103
- Publication Details
- Statistics in medicine, v 27(24), pp 4950-4971
- Publisher
- Wiley
- Grant note
- R03 CA130045-01 || CA / National Cancer Institute : NCI
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000260061700007
- Scopus ID
- 2-s2.0-59849113280
- Other Identifier
- 991014878088504721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Mathematical & Computational Biology
- Medical Informatics
- Medicine, Research & Experimental
- Public, Environmental & Occupational Health
- Statistics & Probability