1. Introduction to Study Design and RCTs Simon Thornley

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3. Cohort By measurement

4. Cohort study eg Framingham Patients without disease Group by exposure Can use a variety of exposures Follow until disease develops

5. Cohort advantages Exposure precedes disease Disease status does not influence selection Several outcomes possible Good for rare exposures Control group obvious (compare case-control)

6. Cohort disadvantages Prospective costly Inefficient for rare diseases with long latency Several outcomes possible Exposed followed more closely than unexposed? Loss to follow up causes bias

7. Do computer screens cause spontaneous abortions? 1991 Computer screens No computers abortionNo abortion Female telephone operators 54312 82434 Time

8. Do computer screens cause spontaneous abortions? Incidence in exposed Relative risk =---------------------------- Incidence in unexposed 54/(54+312) = ------------------- = 0.93 82/(82+434)

9. In pictures - Actual

10. Null hypothesis; No effect

12. Cross sectional Participants sampled at one point or short duration Exposures and outcomes assessed at same point in time By measurement

13. Cross-sectional Advantages Describes pattern of disease Variety of outcomes and exposures Cheap Inexpensive Disadvantages Prevalent rather than incident cases Can not distinguish cause and effect Must survive long enough to be included in study Short duration diseases under-represented (e.g. Influenza)

14. Cross-Sectional study - bias Imagine...  People with disease that are sedentary die early  Cross-sectional study of disease (outcome) and exercise (exposure)  Only sample survivors, so find high proportion of people who exercise with disease  What would you infer about causal relationships?

15. Does wearing fluoro gear protect you from bike crashes? Fluoro colours No fluoro colours Bike crashNo bike crash Cyclists Taupo bike race 162323 588 1343

16. Do computer screens cause spontaneous abortions? Cum. Incidence in exposed Relative risk =---------------------------- Cum. Incidence in unexposed 162/(162+323) = ------------------- = 1.10 588/(588+1343)

17. In pictures- Actual

18. Independent

19. Case-control Investigator selects cases and controls based on disease status Carefully defined population (cases = control population) Exposure history examined Derive P(exposure | case status)

20. Case-control study: Cases Ideally, p case =p disease Prevalent From survey people with disease at particular point in time selection bias/favours long lived, chronic cases Incident from population registry exposure and disease tied only to development of disease, not duration or prognosis.

21. Case control study: Controls Ideally, p control =p undiseased Population vs. hospital controls Hospital controls likely to have disease related to exposure, even if not disease of interest. Population controls, from source of cases, generally better approach, but $$ can be prohibitive Electoral rolls Random digit dialling

22. Reality  More complex; rarely have matches, but frequency matching more common.  E.g. Cot death study  Cases – infants who died from cot death (area)

23. Method  Sampling frame – all births in geographic area  Frequency matched  Control randomly allocated age for interview similar to age distribution to cot deaths from previous years (about 3 months old)  DOB calculated and adjusted to fit day of week (weekends higher chance of becoming cases)  Obstetric hospital randomly chosen in proportion to number of births in previous financial year

26. CC - advantages Good for long latency/ rare diseases Look at many exposures Smaller sample size

27. CC - disadvantages Only one disease Can't estimate disease risk, because work backwards from disease to exposure* More susceptible to selection bias as exposure has already occurred. More susceptible to information bias Not efficient for rare exposures (Why?)

28. Case-Control study -example Fenoterol Ventolin/other CasesControls Adults in hospital with asthma Fenoterol study, Neil Pearce (guest lecturer) 60189 57279

29. Effect measure odds of exposure in cases Odds ratio=---------------------------- odds of exposure in controls 60/57 = -------------- = 1.55 189/279

30. Actual

31. Null hypothesis; No effect

32. Questions  Which study design is best for assessing causation, assuming no other limitations are present?  A) Cross-sectional study  B) Randomised controlled trial  C) Case-control study  D) Cohort study  E) Case-series

33. Questions  In a cross sectional study of risk factors for angina, a random sample of elderly subjects were asked the question “Do you smoke cigarettes?” Answers were used to classify respondents as smokers or non-smokers. Further, subjects were classified as positive for angina if they had, at some time in the past, been told by a doctor that they suffered from this condition.  When the data from the study was analysed, no statistically significant association was found between cigarette smoking status and angina status.

34.  Has the study measured incidence or prevalence of angina? Explain your answer.  A considerable body of past evidence suggests that the risk of angina increases with increasing tobacco consumption. Suggest reasons why the study described here failed to find an association.  Suggest an alternative design of study that would be more suitable for investigating whether smoking causes angina. Consider the question(s) that you would ask the chosen subjects about their smoking habits.

35. Summary CharacteristicCross- sectional Case-controlCohortRCT Selection biasMediumHighLow Recall biasHigh Low Loss to follow upNA High ConfoundingMedium Low Time requiredLowMediumHigh CostMedium High

36. Summary Observational Cohort Many outcomes, exposures limited Case- control One outcome, many exposures Cross – sectional Many exposure, many outcomes; Temporality limits causal inference Experimental Randomised controlled trial Ethical constraints Ideal design