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Mantel-Haenszel-type inference for odds ratios with ordinal responses

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Published .
Written in English


Book details:

Edition Notes

Statementby I-Ming Liu
The Physical Object
Paginationviii, 170 leaves ;
Number of Pages170
ID Numbers
Open LibraryOL25923266M
OCLC/WorldCa33393688

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This article proposes a Mantel-Haenszel-type estimator of an assumed common cumulative odds ratio in a proportional odds model for an ordinal response with several 2 x c contingency tables. It is Author: Ivy Liu. Logistic Regression Models for Ordinal Response Variables provides applied researchers in the social, educational, and behavioral sciences with an accessible and comprehensive coverage of analyses for ordinal outcomes. The content builds on a review of logistic regression, and extends to details of the cumulative (proportional) odds, continuation ratio, and adjacent category models for ordinal Cited by: Mantel-Haenszel and 2x2 tables Author: Blume, Greevy BIOS Page 5 of 14 The basic idea of is to get a weighted average of the strata-specific odds ratios. This can be done on the log scale using weights that are inversely proportional to the variance of the strata specific estimate or it can be done on the correct scale using other Size: KB. Describing Ordinal Odds Ratios for Stratified r x c Tables. Biometrical Journal, 45, Hartzel, Mantel-Haenszel-Type Inference for Cumulative Odds Ratios with a Stratified Ordinal Response. Biometrics, 52,

  Statistical inference for equivalence trials with ordinal responses: A latent normal distribution approach ), one must specify the superiority margins for the K-1 odds ratios. Unlike the difference and the ratio, no official guideline has been given for the choice of “equivalence” odds ratio Cited by: 3. Mantel-Haenszel-type inference for cumulative odds ratios with a stratified ordinal response. Liu IM, Agresti A Biometrics, 52(4), 01 Dec This article proposes a Mantel-Haenszel-type estimator of an assumed common cumulative odds ratio in a proportional odds model for an ordinal response with several 2 x c contingency tables. Statistical models for ordinal responses such as the proportional odds, the continuation ratio and the adjacent category model have been investigated extensively in the literature (see Agresti, ). However, these methods are not suitable for applications where the association between predictors and the response is of a complex nature Cited by:

The likelihood ratio test (df = 7) of the constancy of OR's gave a P-value , thus suggesting that the odds ratio does not depend on the covariates. Therefore, using the parametric method (4b) with δ = 0 was considered appropriate, which yielded an OR estimate of (95% CI: –).Cited by: 3. Analysis of Ordinal Categorical Data, Second Edition is an excellent book for courses on categorical data analysis at the upper-undergraduate and graduate levels. It is also an invaluable resource for researchers and practitioners who conduct data analysis in the areas of public health, business, medicine, and the social and behavioral sciences/5(4). Still another use of ordinal response methods is the application of rank-based methods to continuous responses so as to obtain robust inferences. For example, the proportional odds model described later allows for a continuous Y and is really a generalization of the Wilcoxon–Mann–Whitney rank by: The Mantel-Haenszel () log-odds estimator for dichotomies and the Liu-Agresti () cumulative log-odds estimator for polytomies are: Liu, I-M, & Agresti, A. (). Mantel-Haenszel-type inference for cumulative odds ratios with a stratified ordinal response. Biometrics, 52,