Assessing practical significance of the proportional odds assumption

Kim, Jihyun

doi:10.1016/j.spl.2003.07.017

Cited by 47 publications

(27 citation statements)

References 6 publications

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“…Here, we consider only a subset of the variables from that study, and we do not aim to give a complete assessment of the factors that influence aftercare placement. Neither do we perform formal testing of the proportional odds assumption —a necessary task in real‐life applications. Hosmer et al .…”

Section: Clinical Example: Determinants Of Aftercare Placementmentioning

confidence: 99%

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A goodness‐of‐fit test for the proportional odds regression model

Fagerland

Hosmer

2012

Statistics in Medicine

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We examine goodness-of-fit tests for the proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. We derive a test statistic based on the Hosmer-Lemeshow test for binary logistic regression. Using a simulation study, we investigate the distribution and power properties of this test and compare these with those of three other goodness-of-fit tests. The new test has lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. Moreover, the test allows for the results to be summarized in a contingency table of observed and estimated frequencies, which is a useful supplementary tool to assess model fit. We illustrate the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents. The test proposed in this paper is similar to a recently developed goodness-of-fit test for multinomial logistic regression. A unified approach for testing goodness of fit is now available for binary, multinomial, and ordinal logistic regression models.

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Section: Clinical Example: Determinants Of Aftercare Placementmentioning

confidence: 99%

“…Web Table 3 presents the 1%, 5%, and 10% rejection rates of the tests. We ran simulations using a single continuous covariate with the same coefficients as in models (11), (12), and (13). The results for the C g tests (Web Tables 4-6) were similar to those of Web Tables 1-3.…”

Section: The Models Used To Investigate the Null Distributionsmentioning

confidence: 99%

A goodness‐of‐fit test for the proportional odds regression model

Fagerland

Hosmer

2012

Statistics in Medicine

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“…While this is lower than the standard of 0.05, we felt than multinomial logistic regression unnecessarily obscured our analyses by comparing nominal categories rather than ordered categories. Simulation models by Kim (2003) show that rejection of the null hypothesis for the proportional odds assumption does not have practical applications for large samples. Moreover, substantively similar results were found using both OLS and multinomial regression (figures available upon request).…”

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confidence: 99%

What Would Jesus Buy: American Consumption of Religious and Spiritual Material Goods

Park

Baker

2007

Scientific Study of Religion

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This article examines an important and understudied part of the sociological study of religion: consumption of religious materials. We use data from the Baylor Religion Survey and present an initial look at which Americans read the Left Behind series, viewed The Passion of the Christ, or purchased religious paraphernalia such as “What Would Jesus Do” bracelets. Drawing on cultural consumption and religious capital research we explore class, demographic, and religious influences on consumption patterns. We find some support for each of these perspectives, which suggests that religious consumption is not a subset of cultural consumption nor of religious practice but is some combination of both.

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“…Some researchers suggested that a significance test for model goodness of fit is not sufficient for comparing the baseline-category logits model with the more parsimonious models, such as proportional odds or adjacent-categories logits models [37,38]. Statistical significance does not necessarily imply clinical or public health significance.…”

Section: Discussionmentioning

confidence: 99%

A Bayesian hierarchical modeling approach for studying the factors affecting the stage at diagnosis of prostate cancer

Zhou

Lawson

Hébert

et al. 2007

Statistics in Medicine

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We extend the baseline-category logits model for categorical response data to accommodate two distinct kinds of clustering. Our extension introduces random effects that have one component exhibiting spatial dependence and a second component that is distributed independently. We use this enhanced categorical logits model for investigating the factors that affect the geographical distribution of the diagnostic stage of prostate cancer (PrCA) in South Carolina (SC). Using incidence data from the SC registry, we fit three types of models: the baseline-category logits model, the proportional odds model, and the adjacent-categories logits model, each incorporating our two-component random effects. The deviance information criterion (DIC) is used for selecting the best-fitting model. The results from the best model are presented and interpreted. The county-specific random effects are mapped to characterize the spatial distribution pattern of diagnostic stage of PrCA in the study region. In terms of spatial distribution of the diagnostic stage of PrCA, an area of excess (unexplained) risk was found in the north-west area, and an area of low excess risk in the north-east area for regional-stage cancer in SC was identified through the analysis of the cancer registry data.

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Assessing practical significance of the proportional odds assumption

Cited by 47 publications

References 6 publications

A goodness‐of‐fit test for the proportional odds regression model

A goodness‐of‐fit test for the proportional odds regression model

What Would Jesus Buy: American Consumption of Religious and Spiritual Material Goods

A Bayesian hierarchical modeling approach for studying the factors affecting the stage at diagnosis of prostate cancer

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