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Related Experiment Videos

Pooling data for number needed to treat: no problems for apples.

R Andrew Moore1, David J Gavaghan, Jayne E Edwards

  • 1Pain Research & Nuffield Department of Anaesthetics, University of Oxford, Oxford Radcliffe Hospital, The Churchill, Headington, Oxford, UK. andrew.moore@pru.ox.ac.uk

BMC Medical Research Methodology
|February 28, 2002
PubMed
Summary

Calculating the number needed to treat (NNT) for smoking cessation interventions can yield different results based on the statistical method used. Analysis revealed nursing interventions were effective in hospital settings but not primary care.

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Area of Science:

  • Medical Statistics
  • Public Health
  • Evidence-Based Practice

Background:

  • The number needed to treat (NNT) is a crucial metric for evaluating intervention effectiveness.
  • Different methods for calculating NNT, such as using risk difference or odds ratio, can produce varying results.
  • This variability can impact the interpretation of intervention efficacy, particularly in systematic reviews.

Purpose of the Study:

  • To investigate the discrepancies in NNT calculations for nursing interventions aimed at smoking cessation.
  • To analyze how different statistical approaches (risk difference, odds ratio, raw pooled events) affect NNT values.
  • To assess the impact of clinical heterogeneity on NNT calculations using data from a Cochrane review.

Main Methods:

  • Utilized data from a Cochrane review of nursing interventions for smoking cessation.

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  • Employed L'Abbé plots to evaluate clinical heterogeneity across studies.
  • Analyzed intervention effectiveness separately for primary and secondary care settings.
  • Calculated NNT using different statistical scales (risk difference, odds ratio).

    • Main Results:

    • NNT calculations varied significantly depending on the method employed.
    • Nursing interventions showed no effect in primary care settings with low baseline quit rates (4%).
    • In hospital settings with high baseline quit rates (25%), nursing interventions were effective, yielding an NNT of 14 (95% CI 9 to 26).

    Conclusions:

    • Clinical heterogeneity, identified through raw data and L'Abbé plots, influences NNT outcomes.
    • The choice of statistical scale for NNT calculation impacts the perceived effectiveness of interventions.
    • While NNT is a valuable tool, its sensible application requires careful consideration of underlying data and potential biases.