Calculation of numbers-needed-to-treat in parallel group trials assessing ordinal outcomes: case examples from acute stroke and stroke prevention

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Abstract

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.

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