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Biostatistics Series Module 8: Assessing Risk.

Avijit Hazra1, Nithya Gogtay2

  • 1Department of Pharmacology, Institute of Postgraduate Medical Education and Research, Kolkata, West Bengal, India.

Indian Journal of Dermatology
|April 13, 2017
PubMed
Summary
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This study explains risk assessment in observational and interventional research, focusing on incidence, prevalence, and confidence intervals (CIs). It details calculating odds ratios (OR) and relative risks (RR) to understand risk factors and intervention effectiveness using number needed to treat (NNT) and number needed to harm (NNH).

Area of Science:

  • Epidemiology
  • Biostatistics
  • Clinical Research

Background:

  • Risk assessment is crucial in observational and interventional studies.
  • Understanding incidence and prevalence are foundational for risk evaluation.
  • Statistical measures like confidence intervals (CIs) are vital for interpreting study findings.

Purpose of the Study:

  • To elucidate methods for estimating risk associated with events and exposures.
  • To explain the calculation and interpretation of key risk metrics.
  • To highlight the importance of measures like odds ratio (OR), relative risk (RR), number needed to treat (NNT), and number needed to harm (NNH).

Main Methods:

  • Utilizes concepts of incidence rate and prevalence for risk assessment.
  • Employs 2x2 contingency tables for calculating ratios.
Keywords:
Absolute risk reductionattributable fractionattributable riskincidenceincidence ratenumber needed to harmnumber needed to treatodds ratioprevalencerate ratiorelative riskrelative risk reduction

Related Experiment Videos

  • Defines and explains the calculation of odds ratio (OR) and relative risk (RR).
  • Introduces absolute risk reduction (ARR) and its reciprocal, number needed to treat (NNT).
  • Explains number needed to harm (NNH) as an extension of NNT for adverse outcomes.

    • Main Results:

    • Odds ratio (OR) quantifies the odds of an event in an exposed group versus a non-exposed group.
    • Relative risk (RR) compares the probability of an event between exposed and non-exposed groups.
    • When events are rare, OR and RR values are similar.
    • NNT and NNH provide practical insights into intervention effectiveness and potential harm.

    Conclusions:

    • Accurate risk estimation requires understanding incidence, prevalence, and appropriate statistical measures.
    • OR and RR, presented with 95% CIs, are essential for assessing risk factor importance.
    • NNT and NNH are critical metrics for evaluating interventions and informing policy decisions.