Chapter 9

1. Chapter 9 Measures of Effect Revised by Susan Bailey, Ph.D.

2. Learning Objectives • Explain absolute and relative effects • Calculate measures of risk difference and etiologic fraction • Discuss the role of statistical tests • Apply five criteria for evaluation of epidemiologic associations

3. Measures of Effect • To determine the strength of the effect of specific exposures on outcomes EXPOSURE OUTCOME EXPOSURE OUTCOME

4. Components of the Measures • The measures compare indicators of morbidity and mortality between exposed and unexposed groups. • Indicators can be: • Incidence • Prevalence • Rates

5. Absolute vs. Relative Effects • Absolute • Risk difference • Population risk difference • Relative • Relative risk • Etiologic fraction • Population etiologic fraction

6. Risk Difference (Attributable Risk) • Risk difference--the difference between the incidence rate of disease in the exposed group (Ie) and the incidence rate of disease in the nonexposed group (Ine). • Risk difference = Ie - Ine

7. Calculation of Risk Difference • For women younger than age 75, the incidence (Ie) of hip fractures per 100,000 person-days was highest in the winter (0.41), and the incidence (Ine) was lowest in the summer (0.29). The risk difference between the two seasons (Ie - Ine) was 0.41 - 0.29, or 0.12 per 100,000 person-days.

8. Population Risk Difference • Measures the benefit to the population derived by modifying a risk factor. • Indicates the potential decrease in risk due to the elimination of the harmful exposure. Pop. Risk Difference = Ip - Ine (Ip is the Incidence for the total population, regardless of exposure group)

9. Relative Risk • Ratio of the incidence rate in the exposed (Ie) relative to the incidence rate in the nonexposed groups (Ine). RR = Ie/Ine • Also called a risk ratio. • Remember the exposed rate is always in the numerator.

10. Interpretation of Relative Risk • RR = 1 implies no difference in risk of the outcome for the exposed relative to the unexposed group • RR < 1 implies decreased risk of the outcome for the exposed relative to the unexposed group (i.e., the exposure is protective) – less likely • RR > 1 implies increased risk of the outcome for the exposed relative to the unexposed group (i.e., the exposure is hazardous) – more likely

11. Etiologic Fraction • Measures the proportion of disease in the exposed group that is due to the exposure. • This is a relative effect - there is a denominator. • Also called attributable risk or attributable fraction. Etiologic fraction = (Ie-Ine)/Ie

12. Population Etiologic Fraction • Provides an indication of the effect of removing a particular exposure on the burden of the disease in the population. Pop. Etiologic fraction = (lp-Ine)/Ip

13. Statistical Measures of Effect • Significance tests • The P value • Confidence interval

14. Null Hypothesis • Underlying all statistical tests is a null hypothesis, which states that there is no difference among the groups being compared. (H0) • The typical goal of a study is to reject the null hypothesis to suggest that there is a difference between the exposed and nonexposed groups.

15. Significance Tests • Used to decide whether to reject or fail to reject a null hypothesis. • Involves computation of a test statistic, which is compared with a critical value obtained from statistical tables. • The critical value is set by the significance level of the test. • The significance level is the chance of rejecting the null hypothesis when, in fact, it is true.

16. Normal Distribution (Z) 95% 1.96 -1.96 2.5% 2.5%

17. The P Value • Indicates the probability that the findings observed could have occurred by chance alone. • However, a nonsignificant difference is not necessarily attributable to chance alone.

18. The P Value (cont’d) • Possible meaning of nonsignificant differences: For studies with a small sample size the sampling error may be large, which can lead to a nonsignificant test even if the observed difference is caused by a real effect.

19. Confidence Interval (CI) • A computed interval of values that, with a given probability, contains the true value of the population parameter. • The degree of confidence is usually stated as a percentage; commonly the 95% CI is used. • Influenced by variability of the data and sample size.

20. Clinical vs. Statistical Significance • While small differences in disease frequency or low magnitudes of relative risk (RR) may be significant, they may have no clinical significance. • Conversely, with small sample sizes, large differences or measures of effect may be clinically important.

21. Statistical Power • The ability of a study to demonstrate an association if one exists. • Determined by: • Frequency of the condition under study. • Magnitude of the effect. • Study design. • Sample size.

22. Evaluating Epidemiologic Associations • Five key questions to be asked: • Could the association have been observed by chance? • Determined through the use of statistical tests. • Could the association be due to bias? • Bias refers to systematic errors, i.e., how samples were selected or how data was analyzed.

23. Evaluating Epidemiologic Associations (cont’d) • Could other confounding variables have accounted for the observed relationship? • To whom does this association apply? • Representativeness of sample • Participation rates (lower rates weaken results) • Does the association represent a cause-and-effect relationship? • Considers criteria of causality.

24. Types of Associations between Factors and Outcomes • Not statistically associated (independent) • Statistically associated

25. Statistical Association • When a factor and outcome are statistically associated, the relationship can be: • Non-causal factor outcome • Causal • Indirect factor ? outcome • Direct factor outcome

26. Multiple Causality • “…requirement that more than one factor be present for disease to develop…”

27. Models of Multiple Causality • Web of causation, e.g., in avian influenza • Wheel model, e.g., childhood lead poisoning • Pie model, e.g., lung cancer

28. Web of Causation (Avarian Flu)

29. Wheel Model

30. Pie Model Smoking Ionizing Radiation Carcinogenic Solvents Lung Cancer Example. Pies show 3 sufficient causes of disease. Factor A (common to all 3) could be a genetic predisposition to lung cancer.