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Analytical epidemiology

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Presentation on theme: "Analytical epidemiology"— Presentation transcript:

1 Analytical epidemiology
Disease frequency Study design: cohorts & case control Choice of a reference group Biases Impact Causal inference Stratification - Effect modification - Confounding Matching Significance testing Multivariable analysis 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 but useful 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

8 Vaccine efficacy VE = RR = 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 No Ref. Total


13 Physical activity and MI


15 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 50 60 70 80 Age Doll et Hill, 1966

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

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

18 Simpson’s paradox

19 Second table

20 Day 2, one table only



23 Birth order Down syndrom Age or mother


25 Confounding Exposure Outcome Third variable
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

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

27 Are Mercedes more dangerous than Porsches?
95% CI =

28 Crude RR = 1.5 Adjusted RR = 1.1 ( )

29 Car type Accidents Confounding factor: Age of driver

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

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

32 Salt Myocardial infarction

33 Any statistical test to help us?
When is ORMH different from crude OR ? %

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

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

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

37 Examples of stratified analysis

38 Weighted RR different from crude RR
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)

39 How to conduct a stratified analysis
Perform crude analysis Measure the strength of association List potential effect modifiers and confounders Stratify data according to potential modifiers or confounders Check for effect modification If effect modification present, show the data by stratum If no effect modification present, check for confounding If confounding, show adjusted data If no confounding, show crude data

40 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 ?

41 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

42 A train can mask a second train
A variable can mask another variable


44 What happened? Hat % Fitting Colour
Blue and red hats not evenly distributed between the 2 tables table I, 33 % blue table II, 66 % blue Hat fitting higher in Table I (83%) vs table II (13%) Tables

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