Etiologic research

1. Etiologic research Study of the causes of disease Siti Setiati

2. Major Types of Clinical Epidemiologic Research

3. Etiologic research The research question: • Is there a relation between a determinant (risk factor) and a disease-outcome? • Research question for causal relation!

4. Etiologic researchCharacteristics • To demonstrate causality (cause-effect) • Cause comes before effect • Exposure or determinant occurs before the disease-outcome occurs • Determinant-outcome relation is not explained by other factors • Explanatory research • versus descriptive research

5. Hills’ Criteria • Temporal relationship, where the cause precedes the outcome • Strong association (OR,RR) • Dose-response relationship • Biological plausibility

6. Etiologic researchWhat study design? • Experimental • Exposure or determinant assigned by investigator versus • Observational • Exposure or determinant not assigned by investigator This lecture: observational research

7. Etiologic research What study design? Design of two observational studies todistinguish between cause and effect: • Cohort study • Case-control study

8. Cohort study • Also called follow-up study • Definition • Study in which persons, based on their exposure or determinant, and free of the disease outcome at the start of the study, are followed in time to assess the occurrence of the disease outcome.

9. Cohort study disease + cohort without disease outcome determinant + disease - disease + determinant - disease - time start study disease-outcome

10. Framingham Heart Study • 1948 – Framingham, MA • 5200 persons 30-62 years old • Aim: identification of risk factors for cardiovascular diseases • Remeasured every 2 years Example of a research question: Is hypertension a risk factor for MI?

11. Framingham Heart Study MI + cohort without myocardial infarction hypertension + MI - MI + hypertension - MI - time 1948 1998

12. Cohort studydeterminant-outcome relation MI + MI - hypertension + a b a/a+b=probability of MI for hypertension + = Incidence+ hypertension - c d c/c+d=probability of MI for hypertension - = Incidence - relative risk = incidence + / incidence -

13. Cohort study How do you get a cohort?

14. Cohort study How do you get a cohort? • Geographical data (Framingham Heart Study) • Birth cohort (British 1946 birth cohort) • Occupational cohort (Whitehall study)

15. Cohort study How do you follow the cohort? How do you find the disease-outcome?

16. Cohort study How do you follow the cohort? How do you find the disease-outcome? • After a certain time interval, send out a questionnaire or invite for interview or medical examination • Record disease outcomes via medical files or registrations

17. Cohort studysummary determinant disease-outcome

18. Case-control study • Also called patient-control study • Definition • Study in which patients with the disease-outcome and a control group without the disease-outcome are selected and in which it is determined how many people in both groups have been exposed to the determinant

19. Case-control study determinant + disease + (patients) determinant - determinant + disease – (controls) determinant - time start study

20. Creutzfeldt-Jakob’s Disease

21. Creutzfeldt-Jakob’s Disease • Fast, progressive form of dementia • In the 90s a new variant of Creutzfeldt-Jakob was discovered in Europe after an epidemic of mad-cow disease • Caused by eating beef? What research question? Why case control?

22. Creutzfeldt-Jakob’s Disease beef + patients with CJD beef - beef + controls from hospital beef - time start study

23. Case-control studydeterminant-outcome relation CJD + CJD - beef + a b beef - c d Odds Ratio b/d = odds beef+ in controls a/c = odds beef+ in cases = a x d / b x c

24. Case-control study How do you find cases/patients? How to selecet a control group?

25. Case-control study How do you find patients? • GP; hospital; cancer registration How to select a control group? • GP; hospital; general population • Patients and controls have to come from the same ‘source’ population.

26. Selection of Cases • Ideally, investigator identifies & enrolls all incident cases in a defined population in a specified time period • Select cases from registries or hospitals, clinics • When all incident cases in a population are included, the study is representative; otherwise there is potential for bias (e.g. referral bias) • Use of prevalent vs incident cases

27. Essence case-control studies • Detection of cases • Sampling of controls • Asses exposure in cases and controls • Calculate measure of association (usually, etiology: odds ratio with 95% CI) NOTE Study of cases and controls instead of census (census: entire population, as in cohort studies and RCT)

28. Case-control study How do you assess exposure or determinant?

29. Case-control study How do you assess exposure to determinant? • Interview with participant • Interview with proxy • Medical file

30. Case-control studysummary determinant disease-outcome

31. Validity and bias • Validity: • absence of systematic errors (free from bias) in design,conduct or data-analysis of the research • Bias: • degree of disruption of the determinant–outcome relation caused by systematic errors – leads to reduced validity • 3 types of bias in etiologic research: • selection bias, information bias, confounding

32. What is Bias? Any trend in the collection, analysis, interpretation, publication or review of data that can lead to conclusions that are systematically different from the truth (Last, 2001) A process at any state of inference tending to produce results that depart systematically from the true values (Fletcher et al, 1988) Systematic error in design or conduct of a study (Szklo et al, 2000)

33. 1. Selection biasdefinition • Distortion of the determinant-outcome relation caused by systematic errors in the selection of study participants (cases and/or controls)

34. Selection Bias • Selective differences between comparison groups that impacts on relationship between exposure and outcome • Usually results from comparative groups not coming from the same study base and not being representative of the populations they come from

35. Selection biasexample 1 Oral anticonception and probability of DVT ? Patients: women with DVT admitted to hospital. Controls: healthy women between 25-45 years old Patients turned out to use oral anticonception more often. Oral anticonception should be the cause of DVT. How could selection bias play a role here?

36. Selection biasexample 1 • Medical circuit: 'oral anticonception could lead to DVT’ • Women with DVT complaints, who use oral anticonception, will be more often referred than those that do not use oral anticonception • Because of this selective referral all oral anticonception users will have a higher probability to come into the study as a case and the effect of oral anticonception on DVT will be overestimated

37. Selection biasexample 2 • Patients from hospital – control group from hospital: • In the hospital co-morbidity and unhealthy lifestyles occur more often than in the population • Relation between smoking and cancer can be underestimated due to over-representation of controls who smoke

38. 2. Information biasdefinition • Distortion of the determinant-outcome relation caused by systematic errors in the measurement of the determinant and/or outcome. • Who knows an example?

39. Information / Measurement / Misclassification Bias Sources of information bias: Subject variation Observer variation Deficiency of tools Technical errors in measurement

40. Information biasexamples • Misclassification of determinant • Self reporting more accurate for cases than controls (or the other way around) • Misclassification of outcome • Disease better diagnosed in people with determinant • In what cases can this play a role? • Can this also play a role in cohort research?

41. Information / Measurement / Misclassification Bias Reporting bias: Individuals with severe disease tends to have complete records, therefore more complete information about exposures and greater association found Individuals who are aware of being participants of a study behave differently (Hawthorne effect)

42. Controlling for Information Bias • - Blinding • prevents investigators and interviewers from knowing case/control or exposed/non-exposed status of a given participant • - Form of survey • mail may impose less “white coat tension” than a phone or face-to-face interview • - Questionnaire • use multiple questions that ask same information • acts as a built in double-check • - Accuracy • multiple checks in medical records • gathering diagnosis data from multiple sources

43. 3. Confoundingdefinition • Determinant – disease outcome relation is disturbed by the effect of another factor (the confounder) (“mixing of effects”) • Can you think of an example?

44. Confoundingexample • Children with a higher birth order more often have Down’s syndrome What could be a confounder?

45. Confounding determinant (birth order) disease outcome (Down sydrome) Confounder (age mother) • Confounder is determinant of the disease outcome • Confounder is associated with the determinant • Confounder is no factor in the causal chain

46. Confounding Birth Order Down Syndrome Maternal Age Maternal age is correlated with birth order and a risk factor even if birth order is low

47. Confounding determinant disease outcome Confounder Think of another example of confounding

48. Confounding Coffee CHD Smoking Smoking is correlated with coffee drinking and a risk factor even for those who do not drink coffee

49. Confounding ? Smoking CHD Coffee Coffee drinking may be correlated with smoking but is not a risk factor in non-smokers

50. Confounding Alcohol Lung Cancer Smoking Smoking is correlated with alcohol consumption and a risk factor even for those who do not drink alcohol