1. 1 Case-Control Study Design Two groups are selected, one of people with the disease (cases), and the other of people with the same general characteristics but without the disease (controls) Compare the past exposures of both groups

2. 2 Case Control Study Design Target Population Diseased (Cases) Not Diseased (Controls) Exposed Not Exposed Exposed Not Exposed

3. 3 Case-Control Study Design Limitations: Cannot yield incidence rates because subjects are selected on outcome An estimate of the ratio of incidence rates or risks (RR) is obtained by calculating an odds ratio (OR)

4. 4 Odds Ratio Calculation B / D Odds of exposure for controls = Odds Ratio A / C Odds of exposure for cases Outcome D B Controls C Not Exposed AExposed CasesExposure (estimates the relative risk)

5. 5 Comparing Odds Ratios and Relative Risks 11001000100 730 370 Outcome 700 300 Controls 30 Not Exposed 70Exposed CasesExposure OR = AD/BC = 5.44 RR = I e /I n = 4.41

6. 6 Stating your results OR = 5.44 Those with the disease are 5.44 times as likely to have had the exposure compared to those without the disease RR = 4.41 Those with the exposure are 4.41 times as likely to develop the disease compared to those without the exposure

7. 7 Summary of Strengths and Limitations of Prospective Cohort and Case-Control Studies Limitations:  Possible bias in measuring risk factors after disease has occurred  Possible bias in selecting control group  Identified cases may not represent exposure of all cases Limitations:  Useful for rare disease  Relatively inexpensive  Relatively quick results Strengths:  Useful for rare disease  Relatively inexpensive  Relatively quick results Strengths:  Opportunity to measure risk factors before disease occurs  Can study multiple disease outcomes  Can yield incidence rates as well as relative risk estimates Case-ControlProspective Cohort

8. 8 Randomized Clinical Trials (RCT) The Gold Standard Cohort Study

9. 9 Schematic diagram of a clinical trial Randomization Intervention or new treatment Improved Study Population Non-participants Participants Not Improved Control Treatment arm Control arm ImprovedNot Improved

10. 10 Crossover Design Subjects are randomized to a sequence of two or more treatments Each subject serves as his own control GroupSequencePeriod 1WashoutPeriod 2 IA, BAB IIB, ABA

11. 11 Factorial Design Two or more treatments are evaluated simultaneously in the same set of subjects using varying combinations of treatments Randomization Placebo Treatment A PlaceboTreatment B PlaceboTreatment B

12. How do we evaluate whether cancer studies are valid? Understand bias and confounding

13. 13 Testing for a true association Examine the methodology for bias Examine the analysis for confounding Examine the results for statistical significance

14. 14 Examine the study design for Bias Selection Bias Errors in the process of identifying the study population and selecting the subjects Information/Observation Bias Errors in measurements of exposure or disease status

15. 15 Confounding Confounding is an apparent association between disease and exposure caused by a third factor not taken into consideration

16. 16 Examples of Confounders Study A found an association between gambling and lung cancer. The study may be confounded by smoking. Study B found a larger crude death rate in Florida than in Alaska. The rate may be confounded by differences in the population age structure.

17. 17 Testing for Confounding 1. Calculate the crude rate 2. Calculate a rate adjusted for the confounding variable 3. Compare the two measures The two measures will be different if the variable is a confounder (in practice, when the crude and adjusted measures differ by at least 10%)

18. 18 (3) x (4) = (5) (4) (1) / (2) = (3) (2)(1) 288,039 226,500,000 xxx45,000115Total 171,419 25,700,000 6.67 per 1000 15,00010065+ 56,120 140,300,000 0.40 per 1000 25,0001019-64 60,500 60,500,000 1.00 per 1000 5,00050-18 Expected Number of Deaths 1980 U.S. Standard Population ASR Population at risk Cancer Deaths Age Age-Adjusted Rate (288,039 / 226,500,000) x 1000 1.27 per 1,000 Crude Rate (115 / 45,000) x 1000 2.56 per 1,000 AGE IS A CONFOUNDER FOR DEATH FROM CANCER Not equal

19. 19 Evaluating Statistical Significance The probability that you would get your results by chance alone is the p-value A low p-value ( < 0.05 ) says that chance is not likely to explain your results A 95% confidence interval (CI) is the range of values in which the true value will be found 95% of the time Large samples yield small confidence intervals Small samples yield large confidence intervals

20. 20 Evaluating Results RR = 1: No difference in disease between exposed and unexposed groups OR = 1: No difference in exposure between cases and controls Examples: RR = 1.8 (1.6, 2.0) is statistically significant RR = 1.8 (0.8, 2.9) is not statistically significant OR = 0.7 (0.6, 0.8) is statistically significant OR = 0.7 (0.4, 1.2) is not statistically significant

21. How do we assess whether associations between cancer and risk factors are causal? Understand criteria for causality

22. 22 To Show Cause Chronic disease and complex conditions require the use of Hill’s Postulates 1. Strength of association 2. Consistency of association 3. Specificity of association 4. Temporality 5. Biologic gradient 5. Plausibility 6. Coherence 7. Experiment 8. Analogy

23. How much of the morbidity and mortality from cancer might be prevented by interventions? Understand the impacts of education and screening programs

24. 24 Principles of Screening Validity Sensitivity: correctly identify those with disease Specificity: correctly identify those without disease + Predictive Value: proportion of correct positive tests - Predictive Value: proportion of correct negative tests Reliability: ability of test to give consistent results Yield: amount of unrecognized disease brought to treatment due to screening

25. 25 Calculating Measures of Validity a+b+c+db+da+cTotal c+dd c Negative a+bbaPositive Total No Disease DiseaseTest Result True Diagnosis Positive Predictive Value = a/(a+b) Negative Predictive Value = d/(c+d) Sensitivity = a/(a+c) Specificity = d/(b+d)

26. 26 64,81064,633177Total 63,69563,65045Negative 1,115983132Positive TotalNo DiseaseDisease Mammogram Results Sensitivity = 132/177 = 74.6% Specificity = 63,650/64,633 = 98.5% Positive Predictive Value = 132/1,115 = 11.8% Negative Predictive Value = 63,650/63,695 = 99.9% Example: Breast Cancer Screening

27. 27 Keys to Screening Sensitivity: detect a sufficient number of preclinical cases to be useful Prevalence: screen high-risk populations Frequency: one-time screening does not allow for differences in individual risk or differences in onset Participation: tests unacceptable to the target population will not be utilized Follow-up: those with positive tests need to be provided with a plan of action

28. 28 Advice for Reading the Literature Identify the study design Understand how subjects are selected Understand how exposure is defined Evaluate potential bias and confounding Determine if the statistical evaluation is appropriate Make decisions about whether the outcome measures are statistically significant and/or clinically important Use good judgment

29. End