BACKGROUND: Number-needed-to-treat describes the magnitude of the effect of an intervention, underpins health economic analyses, and is typically calculated for binary events. Ordered categorical outcomes provide more clinical information and their analysis using ordinal approaches is usually more efficient statistically. However, to date, techniques to calculate number-needed-to-treat based on ordinal outcomes for parallel group trials have had important limitations. Aims Numbers-needed-to-treat may be calculated for ordinal data from parallel group trials by using an unmatched comparison of all subjects or by generating matched pairs of patients nested within the study. METHODS: The above approaches were assessed and compared with numbers-needed-to-treat calculated for binary outcomes using individual patient data from acute and prevention stroke trials testing the effect of interventions of varying utility and efficacy. RESULTS: Numbers-needed-to-treat were generally lower numerically for ordinal vs. binary, and matched vs. unmatched analyses, and the lowest in highly efficacious interventions: hemicraniectomy, ordinal matched 2.4 vs. ordinal unmatched 2.5 vs. binary matched 12 vs. binary unmatched 9 (one trial, 12 month outcome); alteplase, 4.5 vs. 6.6 vs. 8.4 vs. 8.4 (one trial with two parts, three-months); aspirin, 42 vs. 58 vs. 76 vs. 80 (one trial, six-months); and stroke units, 3.6-5.3 vs. 6.2 vs. 4.7-5.9 vs. 6.3-7.0 (two trials, three- to 60 months). Similar trends were seen for aspirin/dipyridamole vs. aspirin in secondary prevention, 22 vs. 20 vs. 31 vs. 31 (one trial, 24 months). CONCLUSIONS: Number-needed-to-treat may be calculated for ordinal outcome data derived from parallel group stroke trials; such numbers-needed-to-treat are lower than those calculated for binary outcomes. Their use complements the use of ordinal statistical approaches in the analysis of ordered categorical data.
BACKGROUND: Number-needed-to-treat describes the magnitude of the effect of an intervention, underpins health economic analyses, and is typically calculated for binary events. Ordered categorical outcomes provide more clinical information and their analysis using ordinal approaches is usually more efficient statistically. However, to date, techniques to calculate number-needed-to-treat based on ordinal outcomes for parallel group trials have had important limitations. Aims Numbers-needed-to-treat may be calculated for ordinal data from parallel group trials by using an unmatched comparison of all subjects or by generating matched pairs of patients nested within the study. METHODS: The above approaches were assessed and compared with numbers-needed-to-treat calculated for binary outcomes using individual patient data from acute and prevention stroke trials testing the effect of interventions of varying utility and efficacy. RESULTS: Numbers-needed-to-treat were generally lower numerically for ordinal vs. binary, and matched vs. unmatched analyses, and the lowest in highly efficacious interventions: hemicraniectomy, ordinal matched 2.4 vs. ordinal unmatched 2.5 vs. binary matched 12 vs. binary unmatched 9 (one trial, 12 month outcome); alteplase, 4.5 vs. 6.6 vs. 8.4 vs. 8.4 (one trial with two parts, three-months); aspirin, 42 vs. 58 vs. 76 vs. 80 (one trial, six-months); and stroke units, 3.6-5.3 vs. 6.2 vs. 4.7-5.9 vs. 6.3-7.0 (two trials, three- to 60 months). Similar trends were seen for aspirin/dipyridamole vs. aspirin in secondary prevention, 22 vs. 20 vs. 31 vs. 31 (one trial, 24 months). CONCLUSIONS: Number-needed-to-treat may be calculated for ordinal outcome data derived from parallel group stroke trials; such numbers-needed-to-treat are lower than those calculated for binary outcomes. Their use complements the use of ordinal statistical approaches in the analysis of ordered categorical data.
Authors: Juan Carlos Martinez-Gutierrez; Thabele Leslie-Mazwi; Ronil V Chandra; Kevin L Ong; Raul G Nogueira; Mayank Goyal; Felipe C Albuquerque; Joshua A Hirsch Journal: Interv Neuroradiol Date: 2019-06-27 Impact factor: 1.610
Authors: Sherry Kit Wa Chan; Stephanie Wing Yan Chan; Herbert H Pang; Kang K Yan; Christy Lai Ming Hui; Wing Chung Chang; Edwin Ho Ming Lee; Eric Yu Hai Chen Journal: JAMA Psychiatry Date: 2018-05-01 Impact factor: 21.596
Authors: Lisa J Woodhouse; Jason P Appleton; Polly Scutt; Lisa Everton; Gwenllian Wilkinson; Valeria Caso; Anna Czlonkowska; John Gommans; Kailash Krishnan; Ann C Laska; George Ntaios; Serefnur Ozturk; Stephen Phillips; Stuart Pocock; Kameshwar Prasad; Szabolcs Szatmari; Joanna M Wardlaw; Nikola Sprigg; Philip M Bath Journal: EClinicalMedicine Date: 2022-01-24