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Updated: May 9, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
[Number needed to treat: Interpretation and estimation in multivariable analyses and censored data]
Inés Gómez-Acebo1, Trinidad Dierssen-Sotos2, Javier Llorca3
1Facultad de Medicina, Universidad de Cantabria, Santander, España.
Medicina Clinica
|July 16, 2013
Summary
Number needed to treat (NNT) is a key metric for clinical trials. This study revises NNT estimation methods beyond simple 2x2 tables, including logistic regression, Kaplan-Meier, and Cox regression for more accurate results.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Medical Statistics
Context:
- Number Needed to Treat (NNT) is widely used to report clinical trial outcomes.
- Traditional NNT calculation from 2x2 tables has limitations.
- Confidence intervals and adjustments for confounders are often overlooked.
Purpose:
- To revise and present methods for calculating Number Needed to Treat (NNT).
- To provide accurate point estimations and confidence intervals for NNT.
- To address NNT calculation in various statistical contexts.
Summary:
- This paper reviews the estimation of Number Needed to Treat (NNT).
- It covers NNT calculation from 2x2 tables, logistic regression, Kaplan-Meier method, and Cox regression.
- Methods for obtaining point estimations and confidence intervals are discussed for each situation.
Impact:
- Improves the accuracy and reliability of NNT reporting in clinical research.
- Provides clinicians and researchers with better tools for interpreting treatment effects.
- Enhances the understanding and application of NNT in evidence-based medicine.
Keywords:
Absolut risk reductionControlled clinical trialsCox regressionEnsayos clínicosEpidemiologic methodEstimador de Kaplan-MeierEstudios epidemiológicosKaplan-Meier estimationLogistic regressionNumber needed to treatNúmero necesario de tratamientosReducción absoluta de riesgosRegresión de CoxRegresión logísticaRelated Concept Videos
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