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Analytical epidemiology

Analytical epidemiology. Impact Causality Effect modification & confounding Stratification Significance testing Matching Multivariable analysis. Disease frequency Study design: cohorts & case control Choice of a reference group Biases. Alain Moren, 2006.

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Analytical epidemiology

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  1. Analytical epidemiology • Impact • Causality • Effect modification & confounding • Stratification • Significance testing • Matching • Multivariable analysis Disease frequency Study design: cohorts & case control Choice of a reference group Biases Alain Moren, 2006

  2. Exposure Outcome Third variable

  3. Two main complications (1) Effect modifier (2) Confounding factor - useful information - bias

  4. To analyse effect modification To eliminate confounding Solution = stratification stratified analysis Create strata according to categories inside the range of values taken by third variable

  5. Effect modifier Variation in the magnitude of measure of effect across levels of a third variable. Effect modification is not a bias butuseful information Happens when RR or OR is different between strata (subgroups of population)

  6. Effect modifier • To identify a subgroup with a lower or higher risk • To target public health action • To study interaction between risk factors

  7. Vaccine efficacy AR NV - AR V VE = ----------------------------- AR NV VE = 1 - RR

  8. Vaccine efficacy VE= 1 - RR = 1 - 0.28 VE= 72%

  9. Vaccine efficacy by age group

  10. Effect modification • Different effects (RR) in different strata (age groups) • VE is modified by age • Test for homogeneity among strata (Woolf test)

  11. Oral contraceptives (OC) and myocardial infarction (MI) Case-control study, unstratified data OC MI Controls OR Yes 693 320 4.8 No 307 680 Ref. Total 1000 1000

  12. Physical activity and MI

  13. Effect function Effect (OR or RR) is a function of the effect modifier Relative risk (RR) of dying from coronary heart disease for smoking physicians, by age groups, England & Wales, RR 6 * 5 4 3 * 2 * * 1 * 10 20 30 40 80 60 70 50 Age Doll et Hill, 1966

  14. Any statistical test to help us? • Breslow-Day • Woolf test • Test for trends: Chi square Heterogeneity

  15. Confounding • Distortion of measure of effect because of a third factor • Should be prevented • Needs to be controlled for

  16. Simpson’s paradox

  17. Second table

  18. Day 2, one table only

  19. Confounding To be a confounding factor, 2 conditions must be met: Exposure Outcome Third variable Be associated with exposure - without being the consequence of exposure Be associated with outcome - independently of exposure

  20. To identify confounding Compare crude measure of effect (RR or OR) to adjusted (weighted) measure of effect (Mantel Haenszel RR or OR)

  21. Are Mercedes more dangerous than Porsches? 95% CI = 1.3 - 1.8

  22. Crude RR = 1.5 Adjusted RR = 1.1 (0.94 - 1.27)

  23. Car type Accidents Confounding factor: Age of driver

  24. Age Porsches Mercedes < 25 years 550 (55%) 300 (30%) >= 25 years 450 700 Chi2 = 127.9 Age Accidents No accidents < 25 years 370(44%) 480 >= 25 years 130 (11%) 1020 Chi2 = 270.7

  25. Exposure Outcome Hypercholesterolaemia Myocardial infarction Third factor Atheroma Any factor which is a necessary step in the causal chain is not a confounder

  26. Salt Myocardial infarction Hypertension

  27. Any statistical test to help us? When is ORMH different from crude OR ? 10 - 20 %

  28. How to prevent/control confounding? Prevention • Restriction to one stratum • Matching Control • Stratified analysis • Multivariable analysis

  29. k SUM (ai di / ni) i=1 OR MH = ------------------- k SUM (bi cci / ni) i=1 Mantel-Haenszel summary measure Adjusted or weighted RR or OR Advantages of MH • Zeroes allowed

  30. k SUM (ai di / ni) i=1 OR MH = ------------------- k SUM (bi cci / ni) i=1

  31. Examples of stratified analysis

  32. Effect modifier Belongs to nature Different effects in different strata Simple Useful Increases knowledge of biological mechanism Allows targeting of PH action Confounding factor Belongs to study Weighted RR different from crude RR Distortion of effect Creates confusion in data Prevent (protocol) Control (analysis)

  33. How to conduct a stratified analysis • Perform crude analysisMeasure the strength of association • List potential effect modifiers and confounders • Stratify data according topotential modifiers or confounders • Check for effect modification • If effect modification present, show the data by stratum • If no effect modification present, check for confoundingIf confounding, show adjusted dataIf no confounding, show crude data

  34. How to define strata • In each stratum, third variable is no longer a confounder • Stratum of public health interest • If 2 risk factors, we stratify on the different levels of one of them to study the second • Residual confounding ?

  35. Logical order of data analysis How to deal with multiple risk factors: • Crude analysis • Multivariate analysis 1. stratified analysis 2. modelling linear regression logistic regression

  36. A train can mask a second train A variable can mask another variable

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