1. Introduction to Study Design and RCTs Simon Thornley

2. Some things don’t require an epidemiological study This is one of them…

3. Excuse me… but it’s been three hours… And… he’s not quite dead yet… I’m sick of these stories…

4. Overview  By the end of this lecture you will be able to:  Describe the main types of analytical epidemiological studies  Describe when to use different designs for different research questions.

5. Participatory epidemiology  Epidemic of dental caries, we think sugary drinks may be responsible....  Cohort/Cross-sectional study  Outcome  Filling, root canal or extraction due to caries (not wisdom tooth) in last 5 years  Exposure  At least one sugary soft drink per week?

6. Participatory epidemiology  Case – control study  Sample by outcome, rather than exposure.  Outcome – dental decay  Exposure – sugary soft drinks.

7. Epidemiological studies Analytic Descriptive  Aim  To assess the cause of a disease  Identify point for intervention, to either prevent disease occurring or improve prognosis of people with disease.  Aim  To assess the health status of the population  Used for time trends/planning health services.  To perform an accurate sample, you need a sampling frame  Surveys  Sample based (simple/cluster)  Capture-Recapture

8. Types of studies Observational Cohort Cross sectional Case control Experimental RCT Ideal study is RCT; all other studies are trying to emulate this design.

9. Elements of epidemiological study Participants Outcomes Exposures

10. Hypothesis  Usually that a certain exposure causes a disease  For example, sugar intake in pregnant women causes childhood asthma  Ideally written before study started.  Hypotheses need to be stated in such a way so that they may be proven wrong (Karl Popper – falsifiability).  Analytical study tests hypothesis that exposure is associated with disease.

11. How to express a hypothesis?  What we want to know  Does sugar exposure cause asthma?  But we have limited resource... So we must use the data available…  “Among countries that took part in the ISAAC study is there an association between reported severe asthma symptoms in 6 year old children and average sugar disappearance data, collected six years before the children were surveyed?”

12. What makes a half decent hypothesis? Screening questions  Feasible (you can do it)  Interesting (it is something that people want to know)  Novel (hasn’t been done before)  Ethical (you won’t end up in prison for doing it)  Relevant (people will take some notice)

13. In pictures... Truth in the universe Truth in the study Infer Design Error Target population Phenomena of interest Intended sample Intended variables

14. Overview of research…  Is there statistical support for my hypothesis?  Do the results match my beliefs?  Belief = “alternate hypothesis”  No evidence for belief = “null hypothesis”  Which one is most likely (statistics helps… p-values)  If they don’t, maybe, I have to change my beliefs?

15. Overview  Is the exposure more likely in those with the disease?  “statistical evidence of association”  If so, are there any other explanations for the association I’ve found?  Confounding  Measurement error  Selection bias.  If not, then association is probably causal… that is… “the exposure causes disease”.  Also consider Bradford-Hill criteria… (a separate lecture)

16. A word about probability (risk)  A number between 0 (it won’t happen) and 1 (it definitely will happen) that describes the long run frequency of an outcome  What is the probability of rolling 1 on a six sided dice?  What about the probability of diarrhoea after eating contaminated sandwhiches?

18. CVD P (High risk | CVD) ≈ 1 P (CVD | High risk ) ≈ 0 High risk

19. Odds and probability  Probability= risk = relative frequency  = number of events/ number of trials  Range (probability) = 0 to 1.  Odds =Probability/(1-Probability)  Range(Odds)= 0 to infinity.  Why favour odds over probability?  What if: P(diarrhoea | chicken salad) =1/4=0.25  P(no diarrhoea | chicken salad)=3/4=0.75  Odds(diarrhoea | chicken salad)=1/3=0.333  Odds(no diarrhoea | chicken salad)=3/1=3.000

20. Conditional probability  Risk of events after people are exposed (or not) represent “conditional probabilities”.  In all studies, we compare probabilities among exposed and unexposed  If exposure not related to outcome, then risk after being exposed (conditional prob) is similar to the risk for the total population (marginal prob.).  In epidemiological study:  Can represent prevalence (cross-sectional study) or incidence proportion (cohort or RCT).

21. Conditional probability and effect measures  Effect measures (e.g. risk ratio, odds ratio, incidence rate ratio) compares the conditional probabilities in exposed and unexposed to see if they differ.  Is difference due to chance (null hypothesis) or not (exposure =alternate hypothesis)?

22. Testing our beliefs?  GATE frame (Prof. Rod Jackson) ab cd Time

23. What does effect measure mean?  E.g. Relative risk = 2.98 (95% CI 0.98 to 4.95)  if 1, no difference.  if >1 then the exposure ↑ the probability of outcome  if <1, the exposure ↓ probability of outcome.

24. Participants Need to define and sample Ideal is to have similar population that differs only by exposure of interest In reality, many confounding factors... Need sufficient heterogeneity of exposure Access Inclusion or exclusion criteria based on potential confounding factors

25. Exposure GATE frame assumes binary (Yes/No) exposure In reality, often measured on continuous scale (e.g. age or blood pressure) Need to check the accuracy of what is being measured Validity (agreement compared to ”gold standard”) and reliability (same over time) Objective vs subjective measures e.g. Cotinine vs self-reported smoking status

26. Outcome Seek outcome that is objective and easily measured eg. Death, 1st cardiovascular disease event Objective vs. subjective measurement Same outcome measured on all participants, regardless of exposure

27. Why favour one study over another?

28. Epidemiology in a nutshell Aim does exposure cause disease? does drug treat disease? Design study Can I randomise? Ethical? Clinical equipoise? Yes No? Observational study Rare disease? One outcome? Case-control (report OR) Rare Exposure? Many outcomes? Cohort (report RR) Randomised study Report (RR) Define case and exposure status Statistical power calculation (type-1, type-2 error, prevalence of disease in unexposed, minimum detectable effect) Is change in exposure distribution temporally related with change in disease distribution?

29. Table 1 Are there systematic differences between exposure and unexposed groups (confounding) What population is the study sample drawn from? Yes (shouldn’t be in RCT!) Are they adjusted for in the analysis if confounders? Is it representative of underlying population or is there likely selection bias? Population divided by exposure status? Check missing data, duplicates, data range, bivariate scatterplots and lowess curves

30. Results: Analysis Continuous Categorical Chi-square or Fisher exact test if cell counts <5 t-test Confounders? Review scientific literature… is there likely to be a “Shared common cause of exposure and disease”? Multiple linear regression Logistic regression and or stratification Outcome variable? Check data distributions Transform? Report adjusted measures of association (OR/RR) Report ‘crude’ or univariate measures of association (OR/RR/HR) If difference between crude and adjusted >10%, then Statistical evidence of confounding

31. Interpret study results Is there an association between exposure and outcome? Is P <0.05 or 95% CI for measure of association contain null value (1)? Bias Yes Exposure is associated with disease No Hypothesis likely false Is there another explanation? Consider type-2 error; confounding, bias, other studies Confounding Information (recall) Selection (survivor; loss to follow up, hosp. controls) Could study design be improved? Type-1 error (consider strength of association) Shared common cause of exposure and disease? Regression or stratified analysis Estimate OR/RR and 95% C. I. How does my study compare with others?

32. Discussion Is the association I have detected causal? Bradford Hill criteria Temporality: (cohort study? Not cross sectional or case-control which do not separate exposure and disease) Strength of association: (odds ratio or relative risk, does it indicate >50% increase) Dose response: is there increasing association with increased exposure? Biological plausibility: (are there any laboratory studies to support your assertions?) Consistency: (do other studies using different methods, with different groups come up with similar findings?) Experimental evidence: (Any randomised studies?) Analogy: (Any similar findings from related fields of science?) Specificity: Is exposure to the cause reliably followed by disease? Also: are there any other competing explanations? Are there any studies which shed light on these? If not then… Yes (on balance) Exposure causes disease Calculate Risk difference, NNT and PPAR.

33. Randomised controlled trials  Randomisation  treatment or prevention  Confounding both known and unknown  Blinding  subject/investigator or both  Ethics  Not harmful treatments  Intention to treat analysis:  ”analyse what you randomise”,  even if subjects switch treatment during follow up

34. Randomised controlled trials  Key point is allocation of exposure by investigator Random allocation ab cd

35. Measure of effect For binary outcome Cum. Incidence in exposed Relative risk =------------------------------------------- Cum. Incidence in unexposed a/(a+b) = -------------- c/(c+d)

36. Randomised controlled trial  Best for assessing causation  (like child play, learning – best to manipulate).  Randomisation  balances known and unknown risk factors into exposure groups (numbers large)  Ethics  “clinical equipoise” (true uncertainty?)  Best for straight forward interventions (eg. drugs)  Difficult for things like diet and lifestyle (e.g. smoking)  Different designs:  separate arms, crossover, factorial.

37. Does pre-quit nicotine treatment improve quitting? Smoking cessation therapy Schuurmans MM, Andreas HD, Xandra van B, et al. Effect of pre-treatment with nicotine patch on withdrawal symptoms and abstinence rates in smokers subsequently quitting with the nicotine patch: a randomized controlled trial. Addiction. 2004;99(5):634-40. Pre-quit NRT Placebo NRT SmokingQuit Adult smokers 1862 880

38. Does pre-quit nicotine treatment improve quitting? Incidence in exposed Relative risk = ---------------------------- Incidence in unexposed 18/(18+62) = -------------- = 2.5 8/(8+80)

39. In pictures - Actual

40. Null hypothesis; no effect

41. Measure of effect

42. Question  You are planning to do a randomised study of the effect of vitamin D to reduce the incidence of cardiovascular disease. What ethical issue is of most importance when choosing this design?  A) Cost  B) Clinical equipoise  C) Informed consent  D) Ethnic inequalities in the incidence of cardiovascular disease  E) Vitamin D is natural.

43. Summary

44. Study design: Introduction and RCTs  Studies allow us to collect statistical evidence to test our hypothesis that an exposure influences a disease outcome.  RCT is ideal study design to assess causation as randomisation balances all other factors except the exposure of interest, assuming that you have large enough numbers…  Need to be true uncertainty about whether the exposure influences disease…

45. Cohort By measurement

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

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

48. Cohort disadvantages  Costly  Inefficient for rare diseases with long latency  Wait forever for disease to develop!  Several outcomes possible  Compared with case-control  Exposed followed more closely than unexposed?  Loss to follow up  may cause bias

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

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

51. In pictures - Actual

52. Null hypothesis; No effect

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

55. 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)

56. 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?

57. 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

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

59. In pictures- Actual

60. Independent

61. Case control Investigator selects cases and controls based on disease status Carefully defined population (cases = control population Exposure history examined

62. CC - advantages Good for long latency/ rare diseases Evaluate variety of exposures Smaller sample size

63. CC - disadvantages Can't study several diseases Can't estimate disease risk, because work backwards from disease to exposure* More susceptible to selection bias as exposure already occurred. More susceptible to information bias Not efficient for rare exposures

64. Case control study Incident vs Prevalent cases Incident cases from population registry Prevalent – people with disease at particular point in time Incident – exposure and disease tied only to development of disease, not duration or prognosis. Prevalent – selection bias/favours long lived, chronic cases

65. Case control study: selection of controls Hospital controls Population controls  more likely to have disease related to exposure, even if not disease of interest.  Selection on hospital treatment causes spurious association  Likely Berkson’s bias  from same source as cases,  better than hospital controls  costly. Smoke Cancer Hospital treatment

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

69. 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

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

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

72. Actual

73. Null hypothesis; No effect

74. 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

75. 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.

76.  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.

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

78. 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