Generalized pairwise comparisons of prioritized outcomes in the two-sample problem
This paper extends the idea behind the U‐statistic of the Wilcoxon–Mann–Whitney test to perform generalized pairwise comparisons between two groups of observations. The observations are outcomes captured by a single variable, possibly repeatedly measured, or by several variables of any type (e.g. di...
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| Vydané v: | Statistics in medicine Ročník 29; číslo 30; s. 3245 - 3257 |
|---|---|
| Hlavný autor: | |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Chichester, UK
John Wiley & Sons, Ltd
30.12.2010
Wiley Subscription Services, Inc |
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| ISSN: | 0277-6715, 1097-0258, 1097-0258 |
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| Abstract | This paper extends the idea behind the U‐statistic of the Wilcoxon–Mann–Whitney test to perform generalized pairwise comparisons between two groups of observations. The observations are outcomes captured by a single variable, possibly repeatedly measured, or by several variables of any type (e.g. discrete, continuous, time to event). When several outcomes are considered, they must be prioritized. We show that generalized pairwise comparisons extend well‐known non‐parametric tests, and illustrate their interest using data from two randomized clinical trials. We also show that they lead to a general measure of the difference between the groups called the ‘proportion in favor of treatment’, denoted Δ, which is related to traditional measures of treatment effect for a single variable. Copyright © 2010 John Wiley & Sons, Ltd. |
|---|---|
| AbstractList | This paper extends the idea behind the U-statistic of the Wilcoxon-Mann-Whitney test to perform generalized pairwise comparisons between two groups of observations. The observations are outcomes captured by a single variable, possibly repeatedly measured, or by several variables of any type (e.g. discrete, continuous, time to event). When several outcomes are considered, they must be prioritized. We show that generalized pairwise comparisons extend well-known non-parametric tests, and illustrate their interest using data from two randomized clinical trials. We also show that they lead to a general measure of the difference between the groups called the 'proportion in favor of treatment', denoted Δ, which is related to traditional measures of treatment effect for a single variable. This paper extends the idea behind the U‐statistic of the Wilcoxon–Mann–Whitney test to perform generalized pairwise comparisons between two groups of observations. The observations are outcomes captured by a single variable, possibly repeatedly measured, or by several variables of any type (e.g. discrete, continuous, time to event). When several outcomes are considered, they must be prioritized. We show that generalized pairwise comparisons extend well‐known non‐parametric tests, and illustrate their interest using data from two randomized clinical trials. We also show that they lead to a general measure of the difference between the groups called the ‘proportion in favor of treatment’, denoted Δ, which is related to traditional measures of treatment effect for a single variable. Copyright © 2010 John Wiley & Sons, Ltd. This paper extends the idea behind the U-statistic of the Wilcoxon-Mann-Whitney test to perform generalized pairwise comparisons between two groups of observations. The observations are outcomes captured by a single variable, possibly repeatedly measured, or by several variables of any type (e.g. discrete, continuous, time to event). When several outcomes are considered, they must be prioritized. We show that generalized pairwise comparisons extend well-known non-parametric tests, and illustrate their interest using data from two randomized clinical trials. We also show that they lead to a general measure of the difference between the groups called the 'proportion in favor of treatment', denoted ..., which is related to traditional measures of treatment effect for a single variable. (ProQuest: ... denotes formulae/symbols omitted.) This paper extends the idea behind the U-statistic of the Wilcoxon-Mann-Whitney test to perform generalized pairwise comparisons between two groups of observations. The observations are outcomes captured by a single variable, possibly repeatedly measured, or by several variables of any type (e.g. discrete, continuous, time to event). When several outcomes are considered, they must be prioritized. We show that generalized pairwise comparisons extend well-known non-parametric tests, and illustrate their interest using data from two randomized clinical trials. We also show that they lead to a general measure of the difference between the groups called the 'proportion in favor of treatment', denoted Δ, which is related to traditional measures of treatment effect for a single variable.This paper extends the idea behind the U-statistic of the Wilcoxon-Mann-Whitney test to perform generalized pairwise comparisons between two groups of observations. The observations are outcomes captured by a single variable, possibly repeatedly measured, or by several variables of any type (e.g. discrete, continuous, time to event). When several outcomes are considered, they must be prioritized. We show that generalized pairwise comparisons extend well-known non-parametric tests, and illustrate their interest using data from two randomized clinical trials. We also show that they lead to a general measure of the difference between the groups called the 'proportion in favor of treatment', denoted Δ, which is related to traditional measures of treatment effect for a single variable. |
| Author | Buyse, Marc |
| Author_xml | – sequence: 1 givenname: Marc surname: Buyse fullname: Buyse, Marc email: marc.buyse@iddi.com organization: International Drug Development Institute, 30 avenue provinciale, 1340 Louvain-la-Neuve, Belgium |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/21170918$$D View this record in MEDLINE/PubMed |
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Probabilistic index: an intuitive non-parametric approach to measuring the size of the treatment effects (Letter and Authors' Reply). Statistics in Medicine 2006; 25:3944-3948. Finkelstein DM, Schoenfeld DA. Combining mortality and longitudinal measures in clinical trials. Statistics in Medicine 1999; 18:1341-1354. Buyse M. Reformulating the hazard ratio to enhance communication with clinical investigators (Letter). Clinical Trials 2008; 5:641-642. Schilsky RL. Endpoints in cancer clinical trials and the drug approval process. Clinical Cancer Research 2002; 8:935-938. Good P. Extensions of the concept of exchangeability and their applications. Journal of Modern Applied Statistical Methods 2002; 1:243-247. Hoeffding W. A class of statistics with asymptotically normal distribution. Annals of Mathematical Statistics 1948; 19:293-325. Molenberghs G, Kenward MG. Missing Data in Clinical Studies. Wiley: Hoboken, NJ, 2007. Wilcoxon F. Individual comparisons by ranking methods. 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Birkhauser: New York, 2006. – volume: 5 start-page: 3 year: 1976 end-page: 8 article-title: Primary, secondary and meta‐analysis of research publication-title: Educational Researcher – volume: 20 start-page: 215 year: 2001 end-page: 236 article-title: Non‐parametric estimates of overlap publication-title: Statistics in Medicine – volume: 135 start-page: 185 year: 1972 end-page: 207 article-title: Asymptotically efficient rank invariant procedures publication-title: Journal of the Royal Statistical Society, Series A – volume: 5 start-page: 248 year: 2008 end-page: 252 article-title: Reformulating the hazard ratio to enhance communication with clinical investigators publication-title: Clinical Trials – volume: 5 start-page: 641 year: 2008 end-page: 642 article-title: Reformulating the hazard ratio to enhance communication with clinical investigators (Letter) publication-title: Clinical Trials – volume: 4 start-page: 831 year: 1965 end-page: 853 – volume: 19 start-page: 293 year: 1948 end-page: 325 article-title: A class of statistics with asymptotically normal distribution publication-title: Annals of Mathematical Statistics – year: 2007 – year: 2000 – volume: 5 start-page: 445 year: 2004 end-page: 464 article-title: Analyzing incomplete longitudinal clinical trial data publication-title: Biostatistics – volume: 23 start-page: 1579 year: 2004 end-page: 1592 article-title: Combining several ordinal measures in clinical studies publication-title: Statistics in Medicine – volume: 70 start-page: 659 year: 1983 end-page: 663 article-title: Discrete sequential boundaries for clinical trials publication-title: Biometrika – volume: 1 start-page: 243 year: 2002 end-page: 247 article-title: Extensions of the concept of exchangeability and their applications publication-title: Journal of Modern Applied Statistical Methods – volume: 8 start-page: 935 year: 2002 end-page: 938 article-title: Endpoints in cancer clinical trials and the drug approval process publication-title: Clinical Cancer Research – volume: 351 start-page: 2805 year: 2004 end-page: 2816 article-title: Pegaptanib for neovascular age‐related macular degeneration publication-title: New England Journal of Medicine – year: 1990 – volume: 25 start-page: 591 year: 2006 end-page: 602 article-title: Probabilistic index: an intuitive non‐parametric approach to measuring the size of treatment effects publication-title: Statistics in Medicine – volume: 34 start-page: 187 year: 1972 end-page: 220 article-title: Regression models and life tables (with Discussion) publication-title: Journal of the Royal Statistical Society, Series B – volume: 16 start-page: 1303 year: 1997 end-page: 1306 article-title: Testing for individual and population equivalence based on the proportion of similar responses (Letter and Authors' Reply) publication-title: Statistics in Medicine – volume: 79 start-page: 314 year: 1994 end-page: 316 article-title: Probability of the superior outcome of one treatment over another publication-title: Journal of Applied Psychology – volume: 114 start-page: 1804 year: 2007 end-page: 1809 article-title: Visual acuity as an outcome measure in clinical trials of retinal diseases publication-title: Ophthalmology – volume: 52 start-page: 203 year: 1965 end-page: 223 article-title: A generalized Wilcoxon test for comparing arbitrarily singly censored samples publication-title: Biometrika – volume: 7 start-page: 19 year: 2008 article-title: Phenotyping genetic diseases using an extension of µ‐scores for multivariate data publication-title: Statistical Applications in Genetics and Molecular Biology – volume: 25 start-page: 3944 year: 2006 end-page: 3948 article-title: Probabilistic index: an intuitive non‐parametric approach to measuring the size of the treatment effects (Letter and Authors' Reply) publication-title: Statistics in Medicine – year: 2002 – volume: 36 start-page: 721 year: 1980 end-page: 727 article-title: A new approach to the analysis of clinical drug trials with withdrawals publication-title: Biometrics – volume: 1 start-page: 368 year: 2004 end-page: 376 article-title: Are missing outcome data adequately handled? 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| SubjectTerms | Antineoplastic Combined Chemotherapy Protocols - therapeutic use Aptamers, Nucleotide - therapeutic use Clinical trials Colorectal Neoplasms - drug therapy Comparative analysis Disease-Free Survival Fluorouracil - therapeutic use generalized pairwise comparisons Humans Leucovorin - therapeutic use Macular Degeneration - drug therapy measure of treatment effect Measurement techniques Medical treatment Organoplatinum Compounds - therapeutic use prioritized outcomes randomization test Randomized Controlled Trials as Topic - methods Statistics Statistics as Topic - methods Treatment Outcome Visual Acuity - drug effects |
| Title | Generalized pairwise comparisons of prioritized outcomes in the two-sample problem |
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