Analytical epidemiology Disease frequency Study design: cohorts & case control Choice of a reference group Biases Impact Causal inference Alain Moren, 2006 Stratification - Effect modification - Confounding Matching Significance testing Multivariable analysis
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Analytical epidemiology Disease frequency Study design: cohorts & case control Choice of a reference group Biases Impact Causal inference Alain Moren,
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Analytical epidemiology
Disease frequency Study design: cohorts & case control Choice of a reference group Biases Impact Causal inference
Effect modifierBelongs to natureDifferent effects in different strataSimpleUsefulIncreases knowledge of biological mechanismAllows targeting of PH action
Confounding factorBelongs to study
Weighted RR different from crude RRDistortion of effectCreates confusion in dataPrevent (protocol)
Control (analysis)
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
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 ?
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
A train can mask a second train
A variable can mask another variable
What happened?
Tables
% FittingHatColour
Hat fitting higher in Table I (83%) vs table II (13%)
Blue and red hats not evenly distributed between the 2 tables - table I, 33 % blue - table II, 66 % blue