Cited by 179Meta-analysis and systematic reviewsStatistical Methods in Clinical TrialsStatistical Methods in Epidemiology

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How to obtain nnt from cohen’s D: comparison of two methods

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Furukawa, Toshi A., Leucht, Stefan·2011·PLoS ONE

DOI 10.1371/journal.pone.0019070PMC PMC3083419

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Abstract

BACKGROUND: In the literature we find many indices of size of treatment effect (effect size: ES). The preferred index of treatment effect in evidence-based medicine is the number needed to treat (NNT), while the most common one in the medical literature is Cohen's d when the outcome is continuous. There is confusion about how to convert Cohen's d into NNT. METHODS: We conducted meta-analyses of individual patient data from 10 randomized controlled trials of second generation antipsychotics for schizophrenia (n = 4278) to produce Cohen's d and NNTs for various definitions of response, using cutoffs of 10% through 90% reduction on the symptom severity scale. These actual NNTs were compared with NNTs calculated from Cohen's d according to two proposed methods in the literature (Kraemer, et al., Biological Psychiatry, 2006; Furukawa, Lancet, 1999). RESULTS: NNTs from Kraemer's method overlapped with the actual NNTs in 56%, while those based on Furukawa's method fell within the observed ranges of NNTs in 97% of the examined instances. For various definitions of response corresponding with 10% through 70% symptom reduction where we observed a non-small number of responders, the degree of agreement for the former method was at a chance level (ANOVA ICC of 0.12, p = 0.22) but that for the latter method was ANOVA ICC of 0.86 (95%CI: 0.55 to 0.95, p<0.01). CONCLUSIONS: Furukawa's method allows more accurate prediction of NNTs from Cohen's d. Kraemer's method gives a wrong impression that NNT is constant for a given d even when the event rate differs.

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“This NNT can therefore be interpreted as the number of patients one would need to treat with the intervention in order to have one more patient to have an outcome better than a randomly selected one in the control group than if the same number had been given the control intervention. This definition is clinically abst…”
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Additional file 5 of Estimating and visualising the trade-off between benefits and harms on multiple clinical outcomes in network meta-analysis
Chiocchia, V., Furukawa, T. A., Schneider‐Thoma, J., Siafis, S., Cipriani, A., Leucht, S., & Salanti, G. (2024) · Figshare · doi:10.6084/m9.figshare.26638614
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Additional file 9 of Estimating and visualising the trade-off between benefits and harms on multiple clinical outcomes in network meta-analysis
Chiocchia, V., Furukawa, T. A., Schneider‐Thoma, J., Siafis, S., Cipriani, A., Leucht, S., & Salanti, G. (2024) · Figshare · doi:10.6084/m9.figshare.26638626
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Additional file 1 of Estimating and visualising the trade-off between benefits and harms on multiple clinical outcomes in network meta-analysis
Chiocchia, V., Furukawa, T. A., Schneider‐Thoma, J., Siafis, S., Cipriani, A., Leucht, S., & Salanti, G. (2023) · Figshare · doi:10.6084/m9.figshare.24548147
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Additional file 6 of Estimating and visualising the trade-off between benefits and harms on multiple clinical outcomes in network meta-analysis
Chiocchia, V., Furukawa, T. A., Schneider‐Thoma, J., Siafis, S., Cipriani, A., Leucht, S., & Salanti, G. (2024) · Figshare · doi:10.6084/m9.figshare.26638617
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Additional file 8 of Estimating and visualising the trade-off between benefits and harms on multiple clinical outcomes in network meta-analysis
Chiocchia, V., Furukawa, T. A., Schneider‐Thoma, J., Siafis, S., Cipriani, A., Leucht, S., & Salanti, G. (2024) · Figshare · doi:10.6084/m9.figshare.26638623
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Additional file 4 of Estimating and visualising the trade-off between benefits and harms on multiple clinical outcomes in network meta-analysis
Chiocchia, V., Furukawa, T. A., Schneider‐Thoma, J., Siafis, S., Cipriani, A., Leucht, S., & Salanti, G. (2024) · Figshare · doi:10.6084/m9.figshare.26638611
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Authors: Furukawa, T. A., & Leucht, S.
Affiliations: Kyoto University (JP)
TUM Klinikum (DE)
Corresponding author: Furukawa, Toshi A.
ORCID: Furukawa, Toshi A.
Leucht, Stefan
Venue: PLoS ONE
Publisher: Public Library of Science (PLoS)
Published: 2011-04-27
Volume: 6
Issue: 4
Pages: e19070
ISSN: 1932-6203 (ISSN-L)
DOI: 10.1371/journal.pone.0019070
PMC: PMC3083419
OpenAlex: W2057672284
Cited by: 179
References: 29
Citations by year: 2026: 9 · 2025: 18 · 2024: 23 · 2023: 14 · 2022: 8 · 2021: 12 · 2020: 20 · 2019: 14 · 2018: 25 · 2017: 6 · 2016: 8 · 2015: 6 · 2014: 5 · 2013: 6 · 2012: 3
FWCI: 5.47
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS ONE (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Institutional Repositories DataBase (IRDB) (submittedVersion, other-oa)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
Kyoto University Research Information Repository (Kyoto University) (submittedVersion, cc-by)
Funders: American Psychiatric Publishing
Bristol-Myers Squibb
Eli Lilly and Company
Pfizer
AstraZeneca
GlaxoSmithKline
Sanofi
Astellas Pharma
Shionogi
H. Lundbeck A/S
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords: number needed to treat, Cohen's d, effect size, atypical antipsychotics, schizophrenia, symptom severity reduction, treatment response definition, interobserver agreement
MeSH terms: Humans, Schizophrenia, Antipsychotic Agents, Sample Size, Evidence-Based Medicine

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