Statistical Terms

1. See Also
   1. General
      1. Systems Evaluation
      2. Treatment Effect
   2. Epidemiology Rates
      1. Disease Attack Rate
      2. Disease Incidence Rate (Incidence)
      3. Disease Prevalence Rate (Prevalence)
      4. Mortality Statistics (Leading Causes of Death, Mortality Vital Statistics)
   3. Risks
      1. Odds Ratio
      2. Relative Risk
      3. Weighted Mean Difference
   4. Decision Analysis (Decision Tree, Chance Graph)
      1. Incremental Cost Effectiveness Ratio (ICER)
      2. Expected Value Theory (Expected Utility Theory, Time Trade Off, Standard Gamble, Visual Analogue)
      3. Quality Adjusted Life Year ( QALY)
   5. Screening Test
      1. Contingency Grid or Cross Tab (includes Statistics Example)
      2. Bayes Theorem (Bayesian Statistics)
      3. Fagan Nomogram
      4. Experimental Error (Experimental Bias)
      5. Lead-Time Bias
      6. Length Bias
      7. Selection Bias (Screening Bias)
      8. Likelihood Ratio (Positive Likelihood Ratio, Negative Likelihood Ratio)
      9. Number Needed to Screen (Number Needed to Treat, Absolute Risk Reduction, Relative Risk Reduction)
      10. Negative Predictive Value
      11. Positive Predictive Value
      12. Pre-Test Odds or Post-Test Odds
      13. Receiver Operating Characteristic
      14. Test Sensitivity (False Negative Rate)
      15. Test Specificity (False Positive Rate)
      16. U.S. Preventive Services Task Force Recommendations
2. Definition: Statistical Significance
   1. Statistical Significance is the probability that study findings are due to chance
      1. The risk of rejecting the null hypothesis when it is actually true
      2. In planning a study, a level of risk is chosen (e.g. 5% risk or P-Value of 0.05)
   2. P-Value is used to denote significance
      1. P-Value < 0.05: <5% that findings due to chance
      2. Reflects reproducibility of the study findings only
      3. Does not predict individual patient's effect
   3. Confidence Interval
      1. Provides a range of possible outcomes
      2. Example: Number Needed to Treat ranges from 20 to 100
      3. More clinically relevant
   4. Statistical Significance does not mean clinically useful
      1. See Clinical Significance below
3. Definition: Clinical Significance
   1. Reflects how much of an effect a patient sees
   2. Example:
      1. Study shows drug x significantly improves Hair Growth
      2. Reality: Even the patient cannot see the difference
4. Definition: Heterogeneity
   1. Step 1: Clinical Heterogeneity (study similarity)
      1. Assess similarity of who and what was evaluated across pooled, meta-analyzed studies
      2. Evaluates similarity between studies (systematic review, meta-analysis)
      3. Combined studies in meta-analysis should have similar protocols (e.g. inclusion and exclusion criteria)
   2. Step 2: Statistical Heterogeneity
      1. Assess similarity among study results (were they consistent)
      2. Assign a P-Value to a combined group of studies that reflects the difference in their results and the likelihood that this is due to random chance
      3. Large spread of data across more than one study (i.e. greater heterogeneity) suggests the studies should not be combined in meta-analysis
   3. References
      1. Newman in Majoewsky (2013) EM:Rap 13(7): 7