Assessment of Multiple Ordinal Endpoints

Häberle, Lothar; Pfahlberg, Annette; Gefeller, Olaf

doi:10.1002/bimj.200810502

Cited by 10 publications

(7 citation statements)

References 36 publications

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“…Furthermore Spearman correlations were assessed between the DSB levels normalised to the DLP and the body mass index of the patients and the attenuations of the blood, respectively. In order to analyse the overall correlation of tube current-time product and cranio-caudal range with DSB levels, multivariate Spearman correlations were computed according to the method of Rosenbaum [19,20]. Thereby, tube current-time product and cranio-caudal range were considered as tuple.…”

Section: Lymphocyte Separation and Immunofluorescence Analysismentioning

confidence: 99%

Effect of CT scan protocols on x-ray-induced DNA double-strand breaks in blood lymphocytes of patients undergoing coronary CT angiography

et al. 2010

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Section: Lymphocyte Separation and Immunofluorescence Analysismentioning

confidence: 99%

Effect of CT scan protocols on x-ray-induced DNA double-strand breaks in blood lymphocytes of patients undergoing coronary CT angiography

et al. 2010

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“…As the number of variables increases, information content (the proportion of paired comparisons that can be decided) drops fast, until all µ-scores (u-scores for multivariate data) become NA, especially with a ‘strong’ order[82], as compared to its ‘weak’ counterpart[67]. As pointed out recently[88], averaging univariate u-scores[89] or using the lexicographical order avoid this problem, yet require that the relative importance of the variables be constant and known or that less important variables contribute only by breaking ties.…”

Section: Multivarate Datamentioning

confidence: 99%

Nonparametric Methods for Molecular Biology

Wittkowski

Song

2009

Methods in Molecular Biology

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In 2003, the completion of the Human Genome Project[1] together with advances in computational resources[2] were expected to launch an era where the genetic and genomic contributions to many common diseases would be found. In the years following, however, researchers became increasingly frustrated as most reported ‘findings’ could not be replicated in independent studies[3]. To improve the signal/noise ratio, it was suggested to increase the number of cases to be included to tens of thousands[4], a requirement that would dramatically restrict the scope of personalized medicine. Similarly, there was little success in elucidating the gene–gene interactions involved in complex diseases or even in developing criteria for assessing their phenotypes. As a partial solution to these enigmata, we here introduce a class of statistical methods as the ‘missing link’ between advances in genetics and informatics. As a first step, we provide a unifying view of a plethora of non-parametric tests developed mainly in the 1940s, all of which can be expressed as u-statistics. Then, we will extend this approach to reflect categorical and ordinal relationships between variables, resulting in a flexible and powerful approach to deal with the impact of (1) multi-allelic genetic loci, (2) poly-locus genetic regions, and (3) oligo-genetic and oligo-genomic collaborative interactions on complex phenotypes.

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“…Wittkowski () proposed a test for multivariate ordinal data using U‐statistics based on a product ordering of outcomes, an idea also explored by Rosenbaum in depth (, ). Häberle, Pfahlberg, and Geffeler () defined the ranking methods of many of the above‐referenced tests in terms of different types of partial orders.…”

Section: Introductionmentioning

confidence: 99%

Global Rank Tests for Multiple, Possibly Censored, Outcomes

2016

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Summary Clinical trials often collect multiple outcomes on each patient, as the treatment may be expected to affect the patient on many dimensions. For example, a treatment for a neurological disease such as ALS is intended to impact several dimensions of neurological function as well as survival. The assessment of treatment on the basis of multiple outcomes is challenging, both in terms of selecting a test and interpreting the results. Several global tests have been proposed, and we provide a general approach to selecting and executing a global test. The tests require minimal parametric assumptions, are flexible about weighting of the various outcomes, and are appropriate even when some or all of the outcomes are censored. The test we propose is based on a simple scoring mechanism applied to each pair of subjects for each endpoint. The pairwise scores are then reduced to a summary score, and a rank-sum test is applied to the summary scores. This can be seen as a generalization of previously proposed nonparametric global tests (e.g. O'Brien 1984). We discuss the choice of optimal weighting schemes based on power and relative importance of the outcomes. As the optimal weights are generally unknown in practice, we also propose an adaptive weighting scheme and evaluate its performance in simulations. We apply the methods to analyze the impact of a treatment on neurological function and death in an ALS trial.

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Assessment of Multiple Ordinal Endpoints

Cited by 10 publications

References 36 publications

Effect of CT scan protocols on x-ray-induced DNA double-strand breaks in blood lymphocytes of patients undergoing coronary CT angiography

Effect of CT scan protocols on x-ray-induced DNA double-strand breaks in blood lymphocytes of patients undergoing coronary CT angiography

Nonparametric Methods for Molecular Biology

Global Rank Tests for Multiple, Possibly Censored, Outcomes

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