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confounding variable also known as extraneous variables or intervening variables confounding variables “muddy the waters” alternate causal factors or contributory factors which unintentionally influence the results of an experiment, but aren’t the subject of the study

Cases of Down Syndrome by Birth Order EPIET (www)

Cases of Down Syndrome by Age Groups EPIET (www)

Cases of Down Syndrome by Birth Order and Maternal Age EPIET (www)

Confounding A third factor which is related to both exposure and outcome, and which accounts for some/all of the observed relationship between the two Confounder not a result of the exposure e.g., association between child’s birth rank (exposure) and Down syndrome (outcome); mother’s age a confounder? e.g., association between mother’s age (exposure) and Down syndrome (outcome); birth rank a confounder?

mediating variable a.k.a. moderating, intervening, intermediary, or mediating variables a 2nd or 3rd variable that can increase or decrease the relationship between an independent and a dependent variable. for example, whether listeners are persuaded more by the quality or quantity of arguments is moderated by their degree of involvement in an issue.

Confounding Exposure Outcome Third variable To be a confounding factor, two 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 (not an intermediary)

Confounding Birth Order Down Syndrome Maternal Age Maternal age is correlated with birth order and a risk factor even if birth order is low

Confounding ? Maternal Age Down Syndrome Birth Order Birth order is correlated with maternal age but not a risk factor in younger mothers

Confounding Coffee CHD Smoking Smoking is correlated with coffee drinking and a risk factor even for those who do not drink coffee

Confounding ? Smoking CHD Coffee Coffee drinking may be correlated with smoking but is not a risk factor in non-smokers

Confounding Alcohol Lung Cancer Smoking Smoking is correlated with alcohol consumption and a risk factor even for those who do not drink alcohol

Not related to the outcome Not an independent risk factor Confounding ? Smoking CHD Yellow fingers Not related to the outcome Not an independent risk factor

Confounding ? Diet CHD Cholesterol On the causal pathway

Confounding Imagine you have repeated a positive finding of birth order association in Down syndrome or association of coffee drinking with CHD in another sample. Would you be able to replicate it? If not why? Imagine you have included only non-smokers in a study and examined association of alcohol with lung cancer. Would you find an association? Imagine you have stratified your dataset for smoking status in the alcohol - lung cancer association study. Would the odds ratios differ in the two strata? Imagine you have tried to adjust your alcohol association for smoking status (in a statistical model). Would you see an association?

Confounding Imagine you have repeated a positive finding of birth order association in Down syndrome or association of coffee drinking with CHD in another sample. Would you be able to replicate it? If not why? You would not necessarily be able to replicate the original finding because it was a spurious association due to confounding. In another sample where all mothers are below 30 yr, there would be no association with birth order. In another sample in which there are few smokers, the coffee association with CHD would not be replicated.

Confounding Imagine you have included only non-smokers in a study and examined association of alcohol with lung cancer. Would you find an association? No because the first study was confounded. The association with alcohol was actually due to smoking. By restricting the study to non-smokers, we have found the truth. Restriction is one way of preventing confounding at the time of study design.

Confounding For confounding to occur, the confounders should be differentially represented in the comparison groups. Randomisation is an attempt to evenly distribute potential (unknown) confounders in study groups. It does not guarantee control of confounding. Matching is another way of achieving the same. It ensures equal representation of subjects with known confounders in study groups. It has to be coupled with matched analysis. Restriction for potential confounders in design also prevents confounding but causes loss of statistical power (instead stratified analysis may be tried).

Confounding Randomisation, matching and restriction can be tried at the time of designing a study to reduce the risk of confounding. At the time of analysis: Stratification and multivariable (adjusted) analysis can achieve the same. It is preferable to try something at the time of designing the study.

Effect modification is similar to interaction in statistics. In an association study, if the strength of the association varies over different categories of a third variable, this is called effect modification. The third variable is changing the effect of the exposure. The effect modifier may be sex, age, an environmental exposure or a genetic effect. Effect modification is similar to interaction in statistics. There is no adjustment for effect modification. Once it is detected, stratified analysis can be used to obtain stratum-specific odds ratios.

HOW TO CONTROL FOR CONFOUNDERS? IN STUDY DESIGN… RESTRICTION of subjects according to potential confounders (i.e. simply don’t include confounder in study) RANDOM ALLOCATION of subjects to study groups to attempt to even out unknown confounders MATCHING subjects on potential confounder thus assuring even distribution among study groups

HOW TO CONTROL FOR CONFOUNDERS? IN DATA ANALYSIS… STRATIFIED ANALYSIS using the Mantel Haenszel method to adjust for confounders IMPLEMENT A MATCHED-DESIGN after you have collected data (frequency or group) RESTRICTION is still possible at the analysis stage but it means throwing away data MODEL FITTING using regression techniques

WILL ROGERS' PHENOMENON Assume that you are tabulating survival for patients with a certain type of tumor. You separately track survival of patients whose cancer has metastasized and survival of patients whose cancer remains localized. As you would expect, average survival is longer for the patients without metastases. Now a fancier scanner becomes available, making it possible to detect metastases earlier. What happens to the survival of patients in the two groups? The group of patients without metastases is now smaller. The patients who are removed from the group are those with small metastases that could not have been detected without the new technology. These patients tend to die sooner than the patients without detectable metastases. By taking away these patients, the average survival of the patients remaining in the "no metastases" group will improve. What about the other group? The group of patients with metastases is now larger. The additional patients, however, are those with small metastases. These patients tend to live longer than patients with larger metastases. Thus the average survival of all patients in the "with-metastases" group will improve. Changing the diagnostic method paradoxically increased the average survival of both groups! This paradox is called the Will Rogers' phenomenon after a quote from the humorist Will Rogers ("When the Okies left California and went to Oklahoma, they raised the average intelligence in both states"). (www) See also Festenstein, 1985 (www)