Always Start with PECO

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1 Goals of This Course Be able to understand a study design (very basic concept) Be able to understand statistical concepts in a medical paper Be able to perform a data analysis Understanding: PECO study design Randomized Controlled Trials (RCT) Observational studies Cohort study Case control study Cross sectional study

2 Always Start with PECO Population Exposure Comparison Outcome

3 Population? Patients with an echo for possible coronary disease Exposure? Use of Aspirin at the index visit (baseline) Control? No use of Aspirin at the index visit (baseline) Outcome? Long term mortality (median FU of 3.1 years)

4 Types of Epidemiologic Studies Experimental Studies Randomized Clinical Trials Non-Randomized Clinical Trials Observational Studies Cross-Sectional Studies Cohort Studies Case-Control Studies 4

5 Criteria for Study Design Sampled data or Census data? Timing of data collection

6 Classification of study by timing and sampling. Prospective cohort Retrospective cohort Cross-sectional Retrospective case control Prospective case control Past Current Future 6

7 Cross sectional Exposure (E) and Outcome (O) at one moment in time Prevalence of disease Prevalence of exposure Efficient But Often biased for questions involving time between exposure and outcome.

8 Cross-sectional Study: example Asanuma Y, Oeser A, Shintani A and Stein CM. Premature Coronary Artery Atherosclerosis in Systemic Lupus Erythematosis. N Engl J Med (2003) 349: Sampled data or Census data? Samples: Patients with Lupus vs Healthy controls Timing of data collection Only this matters! Past Current Future Exposure: Lupus Cardiovascular risk factors Outcomes: Coronary Atherosclerosis Cross sectional 8

9 Cross sectional 9

10 Cohort Study 1. Define a cohort without disease at T0 2. Document baseline factors which may associate a study outcome 3. Follow up for a period time to the outcome Also called as a follow-up, incidence or prospective study. Target Population Exposure No Exposure Outcome No outcome Outcome No outcome Current Future Prospective Cohort 10 Past Current Retrospective Cohort

11 Advantage of a cohort study Rare exposure (exposures) Several diseases Exposure precedes disease Less sensitive for selection bias Less sensitive for information (recall) bias Dis advantage of a cohort study Costly and time consuming due to census approach If cohort: loss of subjects due to migration, death, etc (selection bias) Inefficient when Rare disease (e.g. side effects of therapies)

12 Cross sectional study can be a part of a cohort study. Cross-sectional Cohort study A. Initially identify a population: Defined population Prevalent cases B. Determine who has the disease and who does not: Had the disease Did not have the disease C. Follow up only those who did not have the disease at baseline Did not have the disease Incidence cases D. Re-screen these individuals at one year Developed disease Did not developed disease At Baseline 12

13 Case control Study Sampled data based on outcome Sampled data or Census data? Timing of data collection Key feature = Sampling (no census) more efficient 1. Select subjects with outcome (cases) 2. Sample from the population where cases emerge from subjects without outcome (controls) 3. Assess an exposure(s) in both cases and controls Note: exposure conceptually happens before outcome

14 Case Control study Advantages: * Efficient due to sampling approach * Rare disease (e.g. side effects) * When exposure measurements are expensive Blood measurements Radiographic readings Disadvantages: One disease Rare exposure CI (Risk) cannot be estimated More sensitive to selection bias More sensitive to information (recall) bias

15 Example: Cohort Study In Framingham study, the association of smoking with coronary heart disease (CHD) is investigated by selecting a group of 3,000 smokers (exposed) and a group of 5,000 non-smokers who are free of CHD at baseline. The incidence of CHD in both groups is compared. Status CHD No CHD Total Smokers 84 2,916 3,000 Nonsmokers 87 4,913 5,000 Total 171 7,829 8,000 Cumulative incidence (risk) of CHD among smokers = 84/3000 = Cumulative incidence (risk) of CHD among non-smokers = 87/5000 = RR=0.028/ =

16 Examle: Case control Study Assume that we conducted a case-control study, 100 cancer cases were identified, and we randomly chose 100 subjects without the cancer. We then retrospectively identify there smoking status before there were diagnosed with cancer. Prostate Cancer Yes No Total Smokers Non-smokers Total Now can you compute cumulative incidence proportion (risk) of Cancer for each group of exposure? Risk of Cancer = 49% vs 37%, are they too high? Odds of smoking (among Cancer) = 49/51 RR =49%/37% =1.32 Odds of smoking (among Non-Cancer) = 37/63 Odds Ratio (OR) of PSA = 49/51 / (37/63) =

17 Analytical Challenge in an Observational Study Case study in an Observational Study Case study in a Randomized Controlled Trial

18 Analytical Challenge in an Observational Study Case study in an Observational Study Case study in a Randomized Controlled Trial

19 Observational Studies Prospective cohort study, Cleveland Clinic Mean follow up 3.1years 6174 patients undergoing stress echocardiography for evaluation of known or suspected coronary disease

20 Results?? Patients using aspirin had 8% increased risk of death, though no statistical significance was achieved. Some thing not right?????

21 Baseline Characteristics Confounding Patients with aspirin has reasons to use the medication, which often biases toward worsening prognosis. Thus we do not know if the observed difference is due to the effect of aspirin or due to the difference in prognosis.

22 Dealing with confounding Randomization Statistical Adjustment (Regression Analysis, ie Multivariable Analysis)

23 Dealing with confounding Randomization Statistical Adjustment (Regression Analysis, ie Multivariable Analysis)

24 Result

25 Analytical Challenge in an Observational Study Case study in an Observational Study Case study in a Randomized Controlled Trial

26 RCT Example THE LANCET Vol 357 January 13, 2001

27 RCT:Baseline Characteristics

28 RCT: Results

29 Why RCT is optimal? RCT is considered the best study design to study causation (Gold Standard of all research design) Because of randomization, not only observed but unobserved factors are thought to be balanced between comparison groups. Statistical adjustment can deal only with imbalance only in observed factors. Thus, RCT is recommended whenever possible.. 29

30 Feasibility Randomization is not feasible in many cases. More costly, longer duration Not suitable for disease with rare outcomes Generalizability (External validity) Many exclusion or Inclusion criteria make patients population skewed which does not reflect general population. Self selection to study enrollment (Healthy worker s effect). Hawthorneeffects makes prognosis better than unmonitored population. Confounding (Internal Validity) Patients with no adherence Intention to Treat RCT Problems

31 Feasibility Randomization is not feasible in many cases. More costly, longer duration Not suitable for disease with rare outcomes Generalizability (External validity) Many exclusion or Inclusion criteria make patients population skewed which does not reflect general population. Self selection to study enrollment (Healthy worker s effect). Hawthorneeffects makes prognosis better than unmonitored population. Confounding (Internal Validity) Patients with no adherence Intention to Treat RCT Problems

32 Feasibility Randomization is not feasible in many cases. More costly, longer duration Not suitable for disease with rare outcomes Generalizability (External validity) Many exclusion or Inclusion criteria make patients population skewed which does not reflect general population. Self selection to study enrollment (Healthy worker s effect). Hawthorneeffects makes prognosis better than unmonitored population. Confounding (Internal Validity) Patients with no adherence Intention to Treat RCT Problems

33 Exclusion and Inclusion Criteria Inclusion criteria were defined as: old age (65 years); hypertension (systolic blood pressure [SBP] 160 mm Hg or diastolic blood pressure [DBP] 95 mm Hg on at least three separate occasions); hypercholesterolaemia (total blood cholesterol 6 4 mmol/l on at least two separate occasions); diabetes mellitus (fasting venous plasma glucose concentration 7 8 mmol/l on at least two separate occasions [chronic drug treatment for any of the three latter conditions was also a criterion for inclusion]); obesity (body mass index 30 kg/m2); and family history of myocardial infarction before 55 years of age in at least one parent or sibling. Exclusion criteria were: treatment with antiplatelet drugs (history of vascular events or diseases); chronic use of anti inflammatory agents or anticoagulants; contraindications to aspirin; diseases with predictable poor short term prognosis; and predictable psychological or logistical difficulties affecting compliance with the trial requirements.

34 RCT difficulty in generalizability Healthy Workers effect, Hawthorne effect 1.0 Proportion Surviving Randomized (n=5051) Non-randomized (n=12217) Years after Randomization Fiore LD et al. Department of Veterans Affairs Cooperative Studies Program Clinical Trial comparing combined warfarin and aspirin with aspirin alone in survivors of acute myocardial infarction: primary results of the CHAMP study. Circulation Feb 5;105(5):

35 Feasibility Randomization is not feasible in many cases. More costly, longer duration Not suitable for disease with rare outcomes Generalizability (External validity) Many exclusion or Inclusion criteria make patients population skewed which does not reflect general population. Self selection to study enrollment (Healthy worker s effect). Hawthorneeffects makes prognosis better than unmonitored population. Confounding (Internal Validity) Patients with no adherence Intention to Treat RCT Problems

36

37 37

38

39 Likelihood of Truth by Study Design

40 Summary of this lecture Observational studies are easier to conduct than an experimental studies such as RCT s Such observational study is classified with (sampling vs census, timing of data collection) and they include: Cross sectional Cohort Case control * Risk cannot be referred in a case control study nor in a cross sectional study An observational study faces a challenge in statistical analysis > Typically using multivariable analysis with a regression method to control for confounding > RCT may be OK with univariable analysis (unadjusted analysis) Don t rely on Likelihood of Truth by Study Design, trust your own judgment!

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