Presentation on theme: "Confounding and effect modification"— Presentation transcript:
1 Confounding and effect modification Preben Aavitsland
2 Can we believe the result? RiceSalmonellosisOR = 3.9
3 Systematic error Does not decrease with increasing sample size Selection biasInformation biasConfounding
4 Confunding - 1“Mixing of the effect of the exposure on disease with the effect of another factor that is associated with the exposure.”ExposureDiseaseConfounder
5 Confounding - 2 Key term in epidemiology Most important explanation for associationsAlways look for confounding factorsSurgeonPost op inf.Op theatre I
6 Criteria for a confounder 1 A confounder must be a cause of the disease (or a marker for a cause)2 A confounder must be associated with the exposure in the source population3 A confounder must not be affected by the exposure or the diseaseUmbrellaLess tub.21Class3
10 Downs’ syndrome by birth order and maternal age groups
11 Find confounders”The Norwegian comedian Marve Fleksnes once stated: I am probably allergic to leather because every time I go to bed with my shoes on, I wake up with a headache the next morning.”Sleep shoesHeadacheAlcohol
12 Find confounders“A study has found that small hospitals have lower rates of nosocomial infections than the large university hospitals. The local politicians use this as an argument for the higher quality of local hospitals.”Small hospFew infectionsWell patients
13 Controlling confounding In the designRestriction of the studyMatchingBefore data collection!In the analysisRestriction of the analysisStratificationMultivariable regressionAfter data collection!
14 RestrictionRestriction of the study or the analysis to a subgroup that is homogenous for the possible confounder.Always possible, but reduces the size of the study.UmbrellaLess tub.LowerclassClass
15 Restriction We study only mothers of a certain age Many children Downs’35 year old mothers
16 Matching“Selection of controls to be identical to the cases with respect to distribution of one or more potential confounders.”Many childrenDowns’Maternal age
17 Disadvantages of matching Breaks the rule: Control group should be representative of source populationTherefore: Special ”matched” analysis neededMore complicated analysisCannot study whether matched factor has a causal effectMore difficult to find controls
18 Why match? Random sample from source population may not be possible Quick and easy way to get controlsMatched on ”social factors”: Friend controls, family controls, neighbourhood controlsMatched on time: Density case-control studiesCan improve efficiency of studyCan control for confounding due to factors that are difficult to measure
19 Should we match? Probably not, but may: If there are many possible confounders that you need to stratify for in analysis
20 Stratified analysis Calculate crude odds ratio with whole data set Divide data set in strata for the potential confounding variable and analyse these separatelyCalculate adjusted (ORmh) odds ratioIf adjusted OR differs (> 10-20%) from crude OR, then confounding is present and adjusted OR should be reported
21 Procedure for analysis When two (or more) exposures seem to be associated with diseaseChoose one exposure which will be of interestStratify by the other variableMeaning. Making one two by two table for those with and one for those without the other variable (for example, one table for men and one for women)Repeat the procedure, but change the variables
22 Example Salmonella after wedding dinner Disease seems to be associated with both chicken and riceBut many had both chicken and rice
23 ConfoundingIs rice a confounder for the chicken salmonellosis association?Stratify: Make one 2x2 table for rice-eaters and one for non-rice-eaters (e.g. in Episheet)ChickenSalmonellosisRice
24 No confounding Because: OR for chicken alone = ORmh for chicken ”controlled for rice”
25 ConfoundingIs chicken a confounder for the rice salmonellosis association?Stratify: Make one 2x2 table for chicken-eaters and one for non-chicken-eaters (e.g. in Episheet)RiceSalmonellosisChicken
26 ConfoundingBecause:OR for rice alone = ORmh for rice ”controlled for chicken”Not 3,9
27 Conclusion Chicken is associated with salmonellosis Rice is not associated with salmonellosisconfounding by chicken because many chicken-eaters also had ricerice only appeared to be associated with salmonellosisStratification was needed to find confoundingCompare crude OR to adjusted OR (ORmh)If > 10-20% difference confounding!
28 Multivariable regression Analyse the data in a statistical model that includes both the presumed cause and possible confoundersMeasure the odds ratio OR for each of the exposures, independent from the othersLogistic regression is the most common model in epidemiologyBut explore the data first with stratification!
29 Controlling confounding In the designRestriction of the studyMatchingIn the analysisRestriction of the analysisStratificationMultivariable methods
30 Effect modificationDefinition: The association between exposure and disease differ in strata of the populationExample: Tetracycline discolours teeth in children, but not in adultsExample: Measles vaccine protects in children > 15 months, but not in children < 15 monthsRare occurence
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