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Guidelines to understand and compute the number needed to treat
  1. Valentin Vancak1,
  2. Yair Goldberg2,
  3. Stephen Z Levine3
  1. 1Department of Statistics, University of Haifa, Haifa, Israel
  2. 2The Faculty of Industrial Engineering and Management, Technion Israel Institute of Technology, Haifa, Israel
  3. 3Department of Community Mental Health, University of Haifa, Haifa, Israel
  1. Correspondence to Valentin Vancak, Statistics, University of Haifa, Haifa 3498838, Israel; valentin.vancak{at}gmail.com

Abstract

Objective We aim to explain the unadjusted, adjusted and marginal number needed to treat (NNT) and provide software for clinicians to compute them.

Methods The NNT is an efficacy index that is commonly used in randomised clinical trials. The NNT is the average number of patients needed to treat to obtain one successful outcome (ie, response) due to treatment. We developed the nntcalc R package for desktop use and extended it to a user-friendly web application. We provided users with a user-friendly step-by-step guide. The application calculates the NNT for various models with and without explanatory variables. The implemented models for the adjusted NNT are linear regression and analysis of variance (ANOVA), logistic regression, Kaplan-Meier and Cox regression. If no explanatory variables are available, one can compute the unadjusted Laupacis et al’s NNT, Kraemer and Kupfer’s NNT and the Furukawa and Leucht’s NNT. All NNT estimators are computed with their associated appropriate 95% confidence intervals. All calculations are in R and are replicable.

Results The application provides the user with an easy-to-use web application to compute the NNT in different settings and models. We illustrate the use of the application from examples in schizophrenia research based on the Positive and Negative Syndrome Scale. The application is available from https://nntcalc.iem.technion.ac.il. The output is given in a journal compatible text format, which users can copy and paste or download in a comma-separated values format.

Conclusion This application will help researchers and clinicians assess the efficacy of treatment and consequently improve the quality and accuracy of decisions.

  • schizophrenia & psychotic disorders

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Footnotes

  • Twitter @szlevine

  • Contributors VV programmed the web application, simulated the datasets and drafted the manuscript. SZL and YG, who are the PhD advisors of VV, edited the manuscript, guided the process and helped conceptualise the study. All authors contributed to and have approved the final manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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