Rank ANCOVA is a bad idea?
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I am currently working on a project looking at the best way to handle missing data (such as quality of life) due to death. One approach is to use categories/ranks for the outcome, where death is the worst category. One approach that my colleagues wish to consider is rank ANCOVA. However I am pleased to say that they also wish to consider proportional odds models and continuation ratio models.
I found an R help page that says “This [rank ANCOVA] has been shown to yield unreliable analyses. Use the more formal proportional odds ordinal logistic model. This is a generalization of the Wilcoxon-Mann-Whitney-Kruskal-Wallis statistic. This is implemented in the rms package and elsewhere”.
My question is this: Could anyone tell me where this has been shown? In particular the part about rank ancova being unreliable?
regression nonparametric
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I am currently working on a project looking at the best way to handle missing data (such as quality of life) due to death. One approach is to use categories/ranks for the outcome, where death is the worst category. One approach that my colleagues wish to consider is rank ANCOVA. However I am pleased to say that they also wish to consider proportional odds models and continuation ratio models.
I found an R help page that says “This [rank ANCOVA] has been shown to yield unreliable analyses. Use the more formal proportional odds ordinal logistic model. This is a generalization of the Wilcoxon-Mann-Whitney-Kruskal-Wallis statistic. This is implemented in the rms package and elsewhere”.
My question is this: Could anyone tell me where this has been shown? In particular the part about rank ancova being unreliable?
regression nonparametric
New contributor
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add a comment |
$begingroup$
I am currently working on a project looking at the best way to handle missing data (such as quality of life) due to death. One approach is to use categories/ranks for the outcome, where death is the worst category. One approach that my colleagues wish to consider is rank ANCOVA. However I am pleased to say that they also wish to consider proportional odds models and continuation ratio models.
I found an R help page that says “This [rank ANCOVA] has been shown to yield unreliable analyses. Use the more formal proportional odds ordinal logistic model. This is a generalization of the Wilcoxon-Mann-Whitney-Kruskal-Wallis statistic. This is implemented in the rms package and elsewhere”.
My question is this: Could anyone tell me where this has been shown? In particular the part about rank ancova being unreliable?
regression nonparametric
New contributor
$endgroup$
I am currently working on a project looking at the best way to handle missing data (such as quality of life) due to death. One approach is to use categories/ranks for the outcome, where death is the worst category. One approach that my colleagues wish to consider is rank ANCOVA. However I am pleased to say that they also wish to consider proportional odds models and continuation ratio models.
I found an R help page that says “This [rank ANCOVA] has been shown to yield unreliable analyses. Use the more formal proportional odds ordinal logistic model. This is a generalization of the Wilcoxon-Mann-Whitney-Kruskal-Wallis statistic. This is implemented in the rms package and elsewhere”.
My question is this: Could anyone tell me where this has been shown? In particular the part about rank ancova being unreliable?
regression nonparametric
regression nonparametric
New contributor
New contributor
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asked 12 hours ago
Dan JacksonDan Jackson
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161
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This paper may be a good one demonstrating problems with rank transform anova: https://www.tandfonline.com/doi/abs/10.1080/01621459.1995.10476644
https://www.jstor.org/stable/2291530?seq=1#metadata_info_tab_contents
Just transforming data with ranks and using ordinary regression/anova/ancova, in cases other than the k-sample problem, i.e., attempting to use this when adjusting for covariates or assessing interactions, is not fully based on good statistical principles, and results are hard to interpret.
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1 Answer
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1 Answer
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$begingroup$
This paper may be a good one demonstrating problems with rank transform anova: https://www.tandfonline.com/doi/abs/10.1080/01621459.1995.10476644
https://www.jstor.org/stable/2291530?seq=1#metadata_info_tab_contents
Just transforming data with ranks and using ordinary regression/anova/ancova, in cases other than the k-sample problem, i.e., attempting to use this when adjusting for covariates or assessing interactions, is not fully based on good statistical principles, and results are hard to interpret.
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add a comment |
$begingroup$
This paper may be a good one demonstrating problems with rank transform anova: https://www.tandfonline.com/doi/abs/10.1080/01621459.1995.10476644
https://www.jstor.org/stable/2291530?seq=1#metadata_info_tab_contents
Just transforming data with ranks and using ordinary regression/anova/ancova, in cases other than the k-sample problem, i.e., attempting to use this when adjusting for covariates or assessing interactions, is not fully based on good statistical principles, and results are hard to interpret.
$endgroup$
add a comment |
$begingroup$
This paper may be a good one demonstrating problems with rank transform anova: https://www.tandfonline.com/doi/abs/10.1080/01621459.1995.10476644
https://www.jstor.org/stable/2291530?seq=1#metadata_info_tab_contents
Just transforming data with ranks and using ordinary regression/anova/ancova, in cases other than the k-sample problem, i.e., attempting to use this when adjusting for covariates or assessing interactions, is not fully based on good statistical principles, and results are hard to interpret.
$endgroup$
This paper may be a good one demonstrating problems with rank transform anova: https://www.tandfonline.com/doi/abs/10.1080/01621459.1995.10476644
https://www.jstor.org/stable/2291530?seq=1#metadata_info_tab_contents
Just transforming data with ranks and using ordinary regression/anova/ancova, in cases other than the k-sample problem, i.e., attempting to use this when adjusting for covariates or assessing interactions, is not fully based on good statistical principles, and results are hard to interpret.
answered 11 hours ago
Frank HarrellFrank Harrell
55.4k3108244
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Dan Jackson is a new contributor. Be nice, and check out our Code of Conduct.
Dan Jackson is a new contributor. Be nice, and check out our Code of Conduct.
Dan Jackson is a new contributor. Be nice, and check out our Code of Conduct.
Dan Jackson is a new contributor. Be nice, and check out our Code of Conduct.
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