WEEK 5 INTERPRET RESULTS 2

Key Concepts of Evidence Informed Health Practice

• Confidence Intervals (CI)

  • A range of values that is likely to contain the true parameter.
  • Example: A 95% CI suggests that we are 95% confident that the true effect size lies within this range.
  • Interpretation: If a CI includes the no effect point (e.g., 0 for mean difference or 1 for relative risk), the results are not statistically significant.

• P-Values

  • Indicates the probability that the observed results occurred due to chance.
  • A p-value less than 0.05 (i.e., p < 0.05) indicates statistical significance.
  • If p > 0.05, we cannot reject the null hypothesis, meaning we cannot definitively say there's an effect.

• Relative Risk (RR)

  • Measures the risk of an outcome in an exposed group compared to a non-exposed group.
  • RR = 1 indicates no difference; RR > 1 indicates a higher risk among the exposed; RR < 1 indicates a lower risk among the exposed.
  • Example: A RR of 2.5 indicates that those exposed are 2.5 times more likely to experience the outcome.

• Odds Ratios (OR)

  • Used to compare the odds of an outcome occurring in an exposed group versus a non-exposed group.
  • OR = 1 indicates no association; OR > 1 indicates a positive association; OR < 1 indicates a negative association.
  • An OR of 13.17 suggests that the odds of the outcome are 13.17 times greater in the exposed group.

• Clinical Significance

  • Refers to the practical importance of a treatment effect or outcome; an effect that is statistically significant may not always have real-world relevance.
  • Minimum Clinically Important Difference (MCID): The smallest change in a treatment outcome that a patient identifies as important.

• Minimum Important Difference (MCID)

  • Useful to determine if an intervention is worth its time, effort, and expense.
  • The MCID threshold (e.g., 2 points improvement on the VAS) helps assess whether the treatment offers meaningful benefits to clients.

• Case Study Examination

  • Understand how statistical results (like RR and OR) apply to real-life scenarios.
  • Consider factors such as the confidence intervals and p-values while interpreting outcomes for clients to ensure recommendations are evidence-based.

Interpretation of Data

• Interpreting Statistical Significance

  • Look at CI and p-values when analyzing results from studies.
  • Formulate null and alternate hypotheses based on preliminary data and ensure interpretations align with these hypotheses.
  • For instance, examining fatigue’s effect on anxiety levels provides insights into the relationship's significance for patients.

• Application to Patients

  • Assessing client scenarios, like Yana's experience with potential anxiety, can utilize these statistical measures to inform treatment options.
  • Ensure to address issues that clients may face and potential symptoms if overlooked.
  • Provide actionable insights derived from statistical analysis to encourage informed decisions regarding health interventions.

Research Methodology

• Types of Evidence

  • Primary Research: Direct data collection from studies (e.g., randomized control trials).
  • Secondary Research: Reviews and syntheses of existing data to extract meaningful conclusions and recommendations.

• Evaluating Supplements for Health Issues

  • Critical analysis of supplements' effectiveness based on statistical significance and CSID, particularly for conditions like osteoarthritis.
  • Distinguish between supplements that demonstrate both statistical and clinical significance versus those that do not provide meaningful benefits to the patient, keeping in mind cost-benefit analysis for therapeutic interventions.