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Measures of association in clinical trials: definition and interpretation

António Vaz Carneiro1

  • 1Centro de Estudos de Medicina Baseada na Evidência, Faculdade de Medicina de Lisboa, Lisboa, Portugal.

Revista Portuguesa De Cardiologia : Orgao Oficial Da Sociedade Portuguesa De Cardiologia = Portuguese Journal of Cardiology : an Official Journal of the Portuguese Society of Cardiology
|February 11, 2004
PubMed

Insights

Randomized controlled trials (RCTs) establish causality for treatments. This paper explains how to interpret key measures like relative risk and number needed to treat from RCTs for better clinical decisions.

Area of Science:

  • Clinical Epidemiology
  • Biostatistics
  • Evidence-Based Medicine

Background:

  • Randomized controlled trials (RCTs) are the gold standard for establishing causality between interventions and outcomes.
  • Cardiologists need reliable evidence, such as RCT results, to guide treatment decisions for conditions like congestive heart failure (CHF).
  • Clinical trial results are typically presented as event proportions in experimental and control groups.

Purpose of the Study:

  • To clarify the meaning and interpretation of various measures of association used in randomized controlled trials.
  • To enhance the understanding of how to evaluate the effectiveness and safety of therapeutic interventions.

Main Methods:

  • Discussion of statistical measures commonly reported in RCTs.
  • Explanation of dichotomous outcomes and their presentation in clinical trials.
  • Focus on interpreting absolute risk, relative risk, odds ratios, and number needed to treat/harm.

Main Results:

  • RCTs allow for the establishment of a causal relationship between an intervention and an outcome.
  • Understanding measures of association is crucial for interpreting the clinical significance of trial findings.
  • Standardized presentation of results in RCTs facilitates evidence-based decision-making.

Conclusions:

  • Accurate interpretation of statistical measures from RCTs is essential for evidence-based practice.
  • This paper provides a guide to understanding key metrics for evaluating therapeutic interventions.
  • Improved interpretation of RCT data supports better patient care and clinical decision-making.

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