1. Introduction to Cancer Epidemiology Epidemiology and Molecular Pathology of Cancer: Bootcamp course Tuesday, 3 January 2012

2. Learning Objectives  To define causality in epidemiological research  To clarify causal association vs. statistical association  To give an introduction to study designs in epidemiology  To present patterns of global burden of cancer

3. CAUSALITY AND CAUSAL INFERENCE

4. Causation  Definition: A cause of disease is an event, condition or characteristic that preceded the disease and without which the disease would not have occurred, or would not have occurred at that time. Rothman and Greenland, Am J Public Health 2005

5. Causation A disease can be caused by more than one causal mechanism Causal mechanism involves joint actions of component causes Necessary, sufficient and component causes: Smoking and lung cancer Rothman and Greenland, Am J Public Health 2005 smoking Genetic Suscept- ibility smoking Passive smoke genetics Air pollu- tion genetics gender

6. Causation Most causes are neither necessary nor sufficient  However, elimination of the cause may result in elimination of substantial proportion of disease Estimating causal associations is paramount in epidemiological research and a prerequisite for prevention Rothman and Greenland, Am J Public Health 2005

7. Causation and Causal inference  Follow group of exposed (smoking) individuals over time and observe outcome  What would have happened to same group if not been exposed to smoking?  If the two outcomes differ  causal effect  If the two outcomes same  no causal effect “ Time Machine ” Y smoking Y No smoking Michael10 Jennifer10 Linda00 Jeremy00 Axel10 Sophia11 Elisa10 Hernan, J Epidemiol Comm Health 2004 Counterfactual outcomes of lung cancer among individuals

8. Causal inference  Causal inference – scientific reasoning that allows one to arrive at the conclusion that something is or is likely to be cause  Goal in epidemiology is to approximate counterfactual to estimate causal effects of exposure on disease risk Study design to approximate the counterfactual  Unexposed group should be a proxy of counterfactual experience for the exposed group

9. CAUSAL VERSUS STATISTICAL ASSOCIATIONS

10. Causal vs. statistical association  Statistical associations are what we measure in epidemiological study or randomized trial Relative measures: Odds ratios, rate ratios, hazard ratios Absolute measures: Risk difference, rate difference, number needed to treat/screen  “Men who drink coffee regularly have a 60 percent lower risk of lethal prostate cancer compared to men who don’t drink coffee”

11. Measures of association  Statistical associations Provide estimate of the size of the association  E.g. Compared to healthy weight individuals, does obesity influence risk of postmenopausal breast cancer by a little or alot? Informs direction of the effect  Does the exposure increase risk of disease or decrease risk of disease compared to not being exposed  E.g. Compared to nonusers, individuals who take aspirin are at lower risk of colorectal cancer Aim to approximate causal associations

12. Causal vs. statistical association  Statistical associations can arise: E D Causal association D E Reverse causation/recall bias C E D Confounding Statistical associations can also arise due to misclassification, missing data, selection bias

13. Confounding example Physical activity Lung cancer ? Smoking + _ Women with vigorous physical activity had 80 percent lower risk of lung cancer compared to women who did not exercise

14. Confounding In epidemiology, nonrandom allocation of the exposure It is a mixing of effects. Association between exposure and disease is distorted because it is mixed with the effect of another factor. The result of confounding is to distort the true causal association between an exposure and disease The direction of the distortion can be either toward the null or away from the null. Extent of confounding depends on pr[C], RR[D]|[C], RR[E]|C

15. 95% Confidence Intervals  Range of plausible values consistent with data If no bias or confounding  Surround the measure of association (point estimate)  Relative risk = 2.5, 95% Confidence Interval = 1.7 – 3.9 Size of confidence interval is based on size of cohort, number of outcomes, and prevalence of exposure

16. 95% Confidence Intervals  Relative risk = 2.5, 95% Confidence Interval = 1.7 – 4.1 UPPER 95% CI

17. STUDY DESIGN

18. Randomized Studies  Investigator randomly assigns who gets exposure  Cannot directly observe individual effects  Compare outcomes in exposed vs. unexposed Placebo group is proxy for what would have happened to statin group if not exposed to statins 10,000 people Statin N=5,000 Cancer? Statin N=5,000 TimeTime

19. Cohort studies Analagous to the experiment, but investigator does not assign exposure Cohorts are groups of individuals followed over time Cohorts are longitudinal and outcome assessed over time E Ē Intervention study E Ē Cohort study

20. Person 1 2 3 4 5 6 7 8 Exposed Unexposed Case Time  Cohort = group defined by membership defining event  Once a member, always a member until death  Once defined and follow-up begins, no one is added

21. Cohort Study Person 1 2 3 4 5 6 7 8 Exposed Unexposed Case Time Most exposures vary over time Weight, diet, smoking, infections, blood pressure

22. Case control studies  An efficient and valid alternative to cohort study Case-control study attempts to observe a population more efficiently Efficiency comes from use of control series in place of complete assessment of cohort experience

23. Case control studies  Identify and enroll cases  Determine “cohort” that gave rise to cases  Cases give information about numerators of rates that would have calculated in cohort  Controls should be selected from the same cohort  Controls should estimate exposure in the population from where the cases came

24. Case Control: Risk set sampling Time Person 1 2 3 4 5 6 7 8 9 10 Exposed Unexposed Case

25. Case Control: Risk set sampling Time Person 1 2 3 4 5 6 7 8 9 10 Exposed Unexposed Case

26. 1992 1994 1996 1998 2000 2002 Person 1 2 3 4 5 6 7 8 9 10 11 Exposed Unexposed Cancer case Case Control: Case-cohort sampling

27. 1992 1994 1996 1998 2000 2002 Person 1 2 3 4 5 6 7 8 9 10 11 Exposed Unexposed Cancer case Case Control: Case-cohort sampling

28. 1992 1994 1996 1998 2000 2002 Person 1 2 3 4 5 6 7 8 9 10 11 Exposed Unexposed Cancer case Cases Case Control: “Traditional” sampling

29. GLOBAL BURDEN OF CANCER

30. Global burden of cancer (2008)  12 million new cases of cancer worldwide  7.6 million cancer deaths 3 rd leading cause of death annually  24.6 million persons alive with cancer (within 5 years of diagnosis)  In 2030  26 million new cases, 17 million deaths: WHY?

31. Global burden of cancer Thun et al, 2010

32. US burden of cancer (2011): 303 million  Estimated 1,596,000 million people will be diagnosed with cancer in 2011 822,000 men; 744,000 women  Estimated 572,000 will die of cancer in 2011 (300,000 men; 272,000 women) 5-year relative survival 68% American Cancer Society, 2011

36. Data from the International Agency For Research on Cancer (IARC) website www.iarc.fr Cancer Epidemiology databases, www.iarc.fr Globocan 2002 and Cancer Incidence in Five Countries (CI-VIII, IX)  What are the major cancers among men and women?

37. Cancer Incidence in the World (Men)

38. Cancer Incidence in the World (Women)

39. Cancer incidence rates in Africa, 2002

40. Cervix Uteri Age-standardized incidence rate per 100,000

41. Stomach, Males Age-Standardized incidence rate per 100,000

42. Stomach, Females Age-standardized incidence rate per 100,000

43. Testis, Age-Standardized incidence rate per 100,000

44. Trends in Stomach Cancer Mortality over time: Men