1. Cross-Sectional, Cohort and Case-Control Studies HSS4303B – Intro to Epidemiology

2. April 10, 2010 Your big poster day Is also a big epidemiology poster day for grad students and other students on campus: – http://www.csebottawa.com/index.php/en/resear ch-day-2010

3. CSEB-UofO Epidemiology & Biostatistics Research Day What: Research day for students presenting research that are planned, in progress, and completed. Students may present as poster or oral presentation. Top presentations will be eligible for prizes. Why: Preparatory conference to the 2010 Canadian Society for Epidemiology & Biostatistics National Student Conference in Kingston Who: undergraduate, graduate, and post-graduate students in Ottawa When: Saturday, April 10 th all day Where: Roger Guindon Hall, University of Ottawa For more information: http://www.csebottawa.com/index.php/en/research-day-2010 http://www.csebottawa.com/index.php/en/research-day-2010 *Undergraduate students presenting poster are not required to submit an abstract. Professors of HSS4303 are asked to submit to CSEB-UofO a list of student names and the titles of their presentations by March 20 th. However, to be eligible for oral presentation students must submit an abstract by March 15 th. See website for details.

4. 2010 Canadian Society for Epidemiology & Biostatistics (CSEB) National Student Conference What: National conference for students to present research in the fields of epidemiology and biostatistics that are planned, in progress, and completed. Students may present as poster or oral presentation. Top presentations will be eligible for prizes. Who: undergraduate, graduate, and post-graduate students from across Canada and abroad When: May 27-28, 2010 Where: Queen’s University, Kingston, Ontario For more information: www.studentcseb.cawww.studentcseb.ca * All students are required to submit an abstract by March 1 st, 2010. See website for details.

5. TODAY! Free seminar on how to make scientific posters Space is limited to 20 students Email kkadd016@uottawa.ca to RSVPkkadd016@uottawa.ca DMS12120, 2-5pm

6. Your poster assignments “Intent to submit” must be submitted by March 11 See website for description of content and instructions Marking scheme will be updated

7. Qualitative vs. Quantitative Descriptive vs. Analytic Observational vs. Experimental Case-control Cohort Cross-sectional Interventions Clinical trials Who gets the disease? When do they get it? Where do they get it?

8. Hierarchy of Laziness From most easy to most difficult: 1.Cross-sectional study 2.Cohort study 3.Case-control study 4.Experimentation

9. Types of Observational Studies CROSS-SECTIONAL STUDY – A slice of what’s happening RIGHT NOW – Good for measuring prevalence

10. Cross-Sectional Studies example: – People who currently live in farms are more likely to have Gonnakillyasoon Flu right now than are people who don’t live in farms How is this done? We look at all the people, compute the percentage who have the flu and who live in farms, and compute the percentage who have the flu who don’t live in farms, and see which number is higher.

11. Cross-Sectional Studies From this example: – People who live in farms are more likely to have Gonnakillyasoon Flu than people who don’t live in farms What is exposure? What is outcome? Farm/no farm The flu

12. Cross-Sectional Studies Most common example of cross-sectional studies? e.g./ Of 1000 AIDS patients taking ARVs, 55% are nauseous e.g./ areas with more smokers are also areas with more lung cancer SURVEYS Most public health information reported in media is based on cross-sectional data Major disadvantage: Cannot measure cause and effect e.g./ did the ARVs cause nausea? e.g./ did smoking cause the lung cancer?

13. 2. Case-Control Studies – Subjects are selected based on whether or not that they have the outcome you’re interested in You find people who have the outcome –cases You choose people who don’t – controls You look backwards to see what they were exposed to – Challenge: selecting “controls” accordingly A case-control study begins with the outcome(s) and works backwards to find the exposure(s)

14. Case-Control Studies Example: Question: Is taking of ARV drugs associated with developing nausea? Study design: Find some nauseous and non-nauseous people in the community and check to see if any of them took ARVs Example: Question: Is smoking associated with lung cancer? Study design: Choose a group of people with and without lung cancer and compare them for their history of smoking.

15. Case-Control Studies Example: Question: Is getting a tetanus vaccination associated with developing autism among 4 year olds? Choose a group of 4 year olds with autism and a group of 4 year olds without autism and compare them for their history of vaccination

16. Case-Control Studies Great for studies in which the outcome (disease) is rare Cannot measure incidence

17. Cohort Studies But first…..

18. A brief definition What is a “cohort” – Group of people defined has having a shared event Eg, Irish women born in 1954 are a cohort 3 rd year students at the University of Ottawa are a cohort (based on Latin “cohort”, which was a military unit in the Roman army)

19. Cohort

20. 3. Cohort Studies A cohort study begins with the exposure and follows through to an outcome – Subjects are selected based on whether or not that they have the exposure you’re interested in Find some people who have the exposure Find some other similar people who don’t have the exposure Look forwards to see if they develop the outcome you’re interested in

21. Example: Question: Do ARV drugs cause nausea? Study design: look at group taking ARV and another group not taking ARV and see who develops nausea from each group.

22. Cohort Studies Example: Question: In this class, which study method is best for performing well on the exam, studying the slides or studying the textbook? Follow those who study only the slides and those who only study the textbook and see how they do on the exam

23. Cohort Studies Great for studies in which the exposure (risk factor) is rare Can determine incidence

24. Summary of Advantages ExposureOutcomeStudy Type RareCommonCohort CommonRareCase-control Does moonwalking cause diabetes? Does eating rice cause hermaphroditism? In which study type can you measure incidence of disease? Cohort Case-control Cohort

25. What Design Would You Use? Is there an association between climbing Everest and getting diabetes? Is there an association between left handedness and getting mad cow disease? Is there an association between left handedness and gender? What factors were likely responsible for the salmonella outbreak at the office Xmas party? Cohort Case-Control Cross-sectional Cohort Case-control Cross-sectional Case-control

26. Confounding Exposure Outcome Confounder Shop class/ Breathing English class problems Smoking

27. Classic Confounders Age Sex Socioeconomic status Smoking status That’s why, often analyses are stratified by these variables

28. Confounding A confounder is not in the causal pathway Example: Exposure: diet Outcome: heart disease cholesterol

29. And something else… But what do you call a variable that alters the relationship between the exposure and outcome? Effect modifier (Also called an “interactor”)

30. Effect Modifier Factor that modifies the effect of the putative causal factor(s) under study Different from a confounder – Does not mask or create a relationship – Modifies the nature or direction of the relationship

31. Example of Effect Modification The association between intense exercise and joint mobility:

32. Example of Effect Modification But when we only look at older subjects, we get this relationship: Therefore, in this study AGE is an effect modifier

33. Effect Modifier

34. Gender is an Age-Specific Effect Modifier for Papillary Cancers of the Thyroid Gland Cancer Epidemiol Biomarkers Prev 2009;18(4):1092–100 Background: Thyroid cancer incidence rates have increased worldwide for decades, although more for papillary carcinomas than other types and more for females than males. There are few known thyroid cancer risk factors except female gender, and the reasons for the increasing incidence and gender differences are unknown. Methods: We used the National Cancer Institute's Surveillance, Epidemiology, and End Results 9 Registries Database for cases diagnosed during 1976-2005 to develop etiological clues regarding gender-related differences in papillary thyroid cancer incidence. Standard descriptive epidemiology was supplemented with age-period- cohort (APC) models, simultaneously adjusted for age, calendar-period and birth- cohort effects. Results: The papillary thyroid cancer incidence rate among females was 2.6 times that among males (9.2 versus 3.6 per 100,000 person-years, respectively), with a widening gender gap over time. Age-specific rates were higher among women than men across all age groups, and the female-to-male rate ratio declined quite consistently from more than five at ages 20-24 to 3.4 at ages 35-44 and approached one at ages 80+. APC models for papillary thyroid cancers confirmed statistically different age-specific effects among women and men (P < 0.001 for the null hypothesis of no difference by gender), adjusted for calendar-period and birth- cohort effects. Conclusion: Gender was an age-specific effect modifier for papillary thyroid cancer incidence. Future analytic studies attempting to identify the risk factors responsible for rising papillary thyroid cancer incidence should be designed with adequate power to assess this age-specific interaction among females and males.

35. Measures of Association What do we mean when we say “Smoking is associated with lung cancer”? There are three main measures: – Relative Risk (RR) – Odds Ratio (OR) – Attributable Risk (AR)

36. Relative Risk Used in COHORT STUDIES N dc ba YesNo Disease Status Yes No Exposure Status a+b c+d b+d a+c Total Contingency table

37. N dc ba YesNo Disease Status Yes No Exposure Status a+b c+d b+d a+c Total What is cumulative incident rate among the exposed (I E )? What is cumulative incident rate among the unexposed (I O )? a/(a+b) c/(c+d)

38. N dc ba YesNo Disease Status Yes No Exposure Status a+b c+d b+d a+c Total RR = I E / I O a/(a+b) = --------- c/(c+d)

39. Example A cohort study follows 100 smokers and 100 non-smokers over a 10 year period to determine who ends up with lung cancer

40. 200 9010 2575 YesNo Lung Cancer Yes No Smoking 100 115 85 Total RR = I E / I O 75/100 = --------- 10/100 =7.5

41. A Quick Lesson in Logic Necessary – A necessary condition of a statement must be satisfied for the statement to be true. – i.e., before B can be true, A must happen Sufficient – A sufficient condition is one that, if satisfied, assures the statement's truth – i.e., whenever A happens, B must follow Being a mammal is necessary, but not sufficient, to being human. A leads to B HIV is necessary to cause AIDS. But HIV is not sufficient to causing AIDs (since lots of people have HIV, but don’t get AIDS) Decapitation is sufficient to cause death; however, people can die in many other ways, so decapitation is not necessary to cause death

42. Attributable Risk There are very few instances in which an exposure is both necessary and sufficient to cause a disease. – Eg, smoking is neither necessary nor sufficient to cause lung cancer As a result, exposure to a risk factor will account for only a fraction of the incidence rate in the exposed group

43. Attributable Risk Some non-smokers also get lung cancer, so how can we tell how many cases of lung cancer are actually attributable to smoking? – We compute something called attributable risk (AR), sometimes called “excess risk” Invented by Bruce Levin in 1953

44. There Are Different Kinds of AR We will look at two types: – Population Attributable Risk (PAR) the reduction in incidence that would be observed if the population were entirely unexposed, compared with its current (actual) exposure pattern – Attributable Risk in the Exposed Group (AR) the difference in rate of a condition between an exposed population and an unexposed population – (Terminology varies tremendously between books and epidemiologists)

45. N dc ba YesNo Disease Status Yes No Exposure Status a+b c+d b+d a+c Total PAR = (I T – I O ) / I T Population Attributable Risk (PAR) = [(a+c)/N + c/(c+d)] / [(a+c)/N]

46. N dc ba YesNo Disease Status Yes No Exposure Status a+b c+d b+d a+c Total AR = (I E – I O ) / I E a/(a+b) - c/(c+d) = --------------------- a/(a+b) = (RR-1)/RR Attributable Risk In the Exposed Group (AR) Note: In textbook (p141) Contingency table is transposed

47. 200 9010 2575 YesNo Lung Cancer Yes No Smoking 100 115 85 Total AR = (I E - I O ) / I E 75/100 – 10/100 = -------------------- 75/100 = 65/75 = 86.7% 86.7% of lung cancer cases were likely due to smoking

48. Odds Ratio Relative Risk is a ratio of incidence rates Case-control studies cannot measure incidence, therefore we need to estimate the RR RR can be estimated using Odds Ratio (OR)…. Only when disease is rare – Can therefore be used in both COHORT and CASE-CONTROL studies – In case-control studies, OR and RR terms are used interchangeably When disease is not rare, the OR will overestimate the RR

49. N dc ba YesNo Disease Status Yes No Exposure Status a+b c+d b+d a+c Total Odds of exposure among the diseased = Odds of exposure among the undiseased = a/c b/d

50. N dc ba YesNo Disease Status Yes No Exposure Status a+b c+d b+d a+c Total Odds Ratio = (odds of exposure among diseased) ------------------------------------------- (odds of exposure among undiseased) a/c = --- b/d ad = -- bc

51. Example A study looks at 6 dudes with Creutzfeld-Jakob disease, and 10 dudes without… and looks back to see how many were exposed to Subway Sante Fe chicken sandwiches with double meat, extra dijon mustard, hot peppers and a pickle on the side. What kind of study is this?Case-control

52. 16 63 43 YesNo CJ Disease Yes No Subway Sandwich 7 9 10 6 Total Odds ratio = 3x6 ----- = 3x4 18/12 = 1.5

53. An example A study was done on 210 smokers and 240 no- smokers to see if smoking is associated with super-hot disease….

54. Smoking is not associated with being super hot

57. What’s Going On? Sex is a confounder There is a much higher prevalence of disease among females Most of the non-smokers are female

58. Homework 1012 men were followed for 1 year. Among the 512 who self-identified as gay, 116 left town at least once. Among the remaining men, 25% never left town – What is the relative risk of being gay and leaving town? – What is the odds ratio of being gay and leaving town? – What percentage of those who left town likely did so as a result of being gay? (i.e., AR) – What is the problem with the last question?