BACKGROUND AND PURPOSE: There is debate regarding the approach for analysis of modified Rankin scale scores, the most common functional outcome scale used in acute stroke trials. METHODS: We propose to use tests to assess treatment differences addressing the metric, "if a patient is chosen at random from each treatment group and if they have different outcomes, what is the chance the patient who received the investigational treatment will have a better outcome than will the patient receiving the standard treatment?" This approach has an associated statement of treatment efficacy easily understood by patients and clinicians, and leads to statistical testing of treatment differences by tests closely related to the Mann-Whitney U test (Wilcoxon Rank-Sum test), which can be tested precisely by permutation tests (randomization tests). RESULTS: We show that a permutation test is as powerful as are other approaches assessing ordinal outcomes of the modified Rankin scores, and we provide data from several examples contrasting alternative approaches. DISCUSSION: Whereas many approaches to analysis of modified Rankin scores outcomes have generally similar statistical performance, this proposed approach: captures information from the ordinal scale, provides a powerful clinical interpretation understood by both patients and clinicians, has power at least equivalent to other ordinal approaches, avoids assumptions in the parameterization, and provides an interpretable parameter based on the same foundation as the calculation of the probability value.
BACKGROUND AND PURPOSE: There is debate regarding the approach for analysis of modified Rankin scale scores, the most common functional outcome scale used in acute stroke trials. METHODS: We propose to use tests to assess treatment differences addressing the metric, "if a patient is chosen at random from each treatment group and if they have different outcomes, what is the chance the patient who received the investigational treatment will have a better outcome than will the patient receiving the standard treatment?" This approach has an associated statement of treatment efficacy easily understood by patients and clinicians, and leads to statistical testing of treatment differences by tests closely related to the Mann-Whitney U test (Wilcoxon Rank-Sum test), which can be tested precisely by permutation tests (randomization tests). RESULTS: We show that a permutation test is as powerful as are other approaches assessing ordinal outcomes of the modified Rankin scores, and we provide data from several examples contrasting alternative approaches. DISCUSSION: Whereas many approaches to analysis of modified Rankin scores outcomes have generally similar statistical performance, this proposed approach: captures information from the ordinal scale, provides a powerful clinical interpretation understood by both patients and clinicians, has power at least equivalent to other ordinal approaches, avoids assumptions in the parameterization, and provides an interpretable parameter based on the same foundation as the calculation of the probability value.
Authors: Michael D Hill; Renee H Martin; Yuko Y Palesch; Diego Tamariz; Bonnie D Waldman; Karla J Ryckborst; Claudia S Moy; William G Barsan; Myron D Ginsberg Journal: Stroke Date: 2011-05-05 Impact factor: 7.914
Authors: Heleen M den Hertog; H Bart van der Worp; H Maarten A van Gemert; Ale Algra; L Jaap Kappelle; Jan van Gijn; Peter J Koudstaal; Diederik W J Dippel Journal: Lancet Neurol Date: 2009-03-16 Impact factor: 44.182
Authors: Kennedy R Lees; Justin A Zivin; Tim Ashwood; Antonio Davalos; Stephen M Davis; Hans-Christoph Diener; James Grotta; Patrick Lyden; Ashfaq Shuaib; Hans-Göran Hårdemark; Warren W Wasiewski Journal: N Engl J Med Date: 2006-02-09 Impact factor: 91.245
Authors: Y Lampl; M Boaz; R Gilad; M Lorberboym; R Dabby; A Rapoport; M Anca-Hershkowitz; M Sadeh Journal: Neurology Date: 2007-10-02 Impact factor: 9.910
Authors: Tolulope T Sajobi; Yukun Zhang; Bijoy K Menon; Mayank Goyal; Andrew M Demchuk; Joseph P Broderick; Michael D Hill Journal: Stroke Date: 2015-05-28 Impact factor: 7.914
Authors: Bruce C V Campbell; Peter J Mitchell; Leonid Churilov; Nawaf Yassi; Timothy J Kleinig; Richard J Dowling; Bernard Yan; Steven J Bush; Vincent Thijs; Rebecca Scroop; Marion Simpson; Mark Brooks; Hamed Asadi; Teddy Y Wu; Darshan G Shah; Tissa Wijeratne; Henry Zhao; Fana Alemseged; Felix Ng; Peter Bailey; Henry Rice; Laetitia de Villiers; Helen M Dewey; Philip M C Choi; Helen Brown; Kendal Redmond; David Leggett; John N Fink; Wayne Collecutt; Thomas Kraemer; Martin Krause; Dennis Cordato; Deborah Field; Henry Ma; Bill O'Brien; Benjamin Clissold; Ferdinand Miteff; Anna Clissold; Geoffrey C Cloud; Leslie E Bolitho; Luke Bonavia; Arup Bhattacharya; Alistair Wright; Abul Mamun; Fintan O'Rourke; John Worthington; Andrew A Wong; Christopher R Levi; Christopher F Bladin; Gagan Sharma; Patricia M Desmond; Mark W Parsons; Geoffrey A Donnan; Stephen M Davis Journal: JAMA Date: 2020-04-07 Impact factor: 56.272
Authors: Sunil A Sheth; Reza Jahan; Jan Gralla; Vitor M Pereira; Raul G Nogueira; Elad I Levy; Osama O Zaidat; Jeffrey L Saver Journal: Ann Neurol Date: 2015-08-17 Impact factor: 10.422
Authors: Kennedy R Lees; Jonathan Emberson; Lisa Blackwell; Erich Bluhmki; Stephen M Davis; Geoffrey A Donnan; James C Grotta; Markku Kaste; Rüdiger von Kummer; Maarten G Lansberg; Richard I Lindley; Patrick Lyden; Gordon D Murray; Peter A G Sandercock; Danilo Toni; Kazunori Toyoda; Joanna M Wardlaw; William N Whiteley; Colin Baigent; Werner Hacke; George Howard Journal: Stroke Date: 2016-08-09 Impact factor: 7.914