Presentation on theme: "Chance, bias and confounding"— Presentation transcript:
1 Chance, bias and confounding The observed statistical association between a certain outcome and and the hypothesized exposure could be a matter of chanceOr it could be the result of systematic errors in collection of data (sampling, disease and exposure ascertainment) or its interpretation: the role of biasOr it could be due to the effect of additional variables that might be responsible for the observed association: the role of confoundingOr it could be a real association
2 ConfounderIs a factor that distorts the true relationship between an exposure and the disease outcome on account of its being associated with both the exposure as well as the diseaseThis distortion (over/underestimation) of the true relation between exposure and disease can occur only if this factor is unequally distributed between the exposed and unexposed groups
3 ConfoundingA confounder is a third factor that is associated with the exposure and independently affects the risk of developing the diseaseIt distorts the estimate of true relationship between the exposure and disease: it may result in association being observed when none in fact exists; or no association being observed when a true relationship does exist
4 ConfounderA potential confounder must be predictive of disease independently of its association with the exposure under studyThis means that there must be an association between the confounder and disease even amongst the group unexposed to the exposure under investigation
5 ConfounderThis third factor should not be merely an intermediate step in the cause and effect relationship between the exposure and the disease outcomeThe association between the confounder and the disease need not be causal. It may a marker for for a risk factor other than the one under investigation in a study.
6 ConfoundingConfounding can lead to the observation of apparent differences between the study groups when they do not truly exist, or conversely, the observation of of no difference when they do exist.
7 An example of confounding A number of observational epidemiological studies have shown an inverse association between the consumption of vegetables rich in β carotene with the risk of cancerIt is however possible that this association is confounded by other differences between the consumers and non-consumers of vegetables such as fiber, which is known to reduce the risk of cancer
8 Confounding: another example An observed association between the consumption of coffee and the risk of MI could be due, at least in part, to the effect of cigarette smoking, since coffee drinking is associated with smoking , and independent of coffee drinking, smoking is a risk factor for MIThe potential or true confounders are not always as obvious as they are in the examples cited above
9 How to avoid confounding? If a confounding factor does not vary between the exposed and non-exposed, or those diseases and non-diseased, then by definition, there can be no confounding by that variableThus if by design or analysis, the association between disease and exposure is evaluated only amongst those who are similar with respect to the confounding factor, there can be no confounding
10 Controlling confounders Restriction of the study populationMatchingRandomization of exposureStratificationMultivariable analysis
11 Common confoundersAge and sex are almost universal confounders for all exposure – disease associationsThis is because they are markers for a whole lot of cumulative exposures. They may not be causally related to disease, but are markers for many other exposures which might be truly related to disease.
12 Confounding: the intermediate link Moderate consumption of alcohol is associated with reduced risk of CADHDL cholesterol also is protective for CADModerate alcohol consumption increases HDLIf one controls for HDL, the association between alcohol intake and the risk of CAD becomes weak or statistically insignificant.Being an intermediate link between alcohol and the risk of CAD, should HDL be considered a confounder at all? Should it be controlled?
13 Positive and negative confounding Tobacco smoking would be a positive confounder in association between coffee drinking and CADThe association between physical activity and CAD would be negatively confounded by gender, since women have lower risk of CAD and they also exercise less than men.
14 Randomization Applicable only to interventional studies Most powerful method to control for known, potential or unknown confounders if the sample size is sufficiently large
15 Restriction Reduces the number of eligible subjects for enrollment It limits generalizability of observations to only the restricted population use for drawing the random sample
16 Matching It includes elements of both design and analysis Mostly applicable to case-control study designIt is expensive, difficult and time consumingBy design, the effect of risk factor which has been matched can not be studiedConfounding is avoided not just by matching but by special method of matched table analysis
17 AnalysisStratified analyses: Stratum specific estimates of association are calculated, and the differences amongst the strata are assessed by eyeballing, or performing appropriate tests of statistical significanceSummary statistic for the pooled data is calculated as per the method of Mantel and HaenszelThe magnitude of confounding is assessed by looking at the discrepancy between the crude and adjusted estimates (without applying any tests of statistical significance)
18 Confounding and effect modification Confounding distorts the true relationship between the exposure and disease and should be controlledEffect modification tells us that the association between exposure and disease is modified by a third factor. It should not be controlled for, the magnitude of effect modification should be reported and biological explanation for its presence sought.
19 BiasThe study must be designed and conducted in such a manner that that every possibility of introducing a bias is anticipated and steps are taken to minimize its occurrenceIn spite of these precautions, the observed association should be carefully examined to see if it could be explained by bias.If indeed the study has elements of bias, it can not be rectified at the stage of analysis (unlike confounding)
20 Types of biasSelection bias: A particular problem in case control and retrospective cohort studies where both exposure and disease have occurred at the time of selection of individuals for the studyInformation bias
21 Selection biasDifferential surveillance, diagnosis or referral of individuals in the study: e.g., women using estrogen have uterine bleeding more often, and seek medical attention for this symptom. Hence they are more likely to seek diagnostic evaluation than those who are not on estrogens resulting in more frequent diagnosis of uterine cancer in women on estrogens
22 Multivariate regression analysis Several potential confounders can be controlled; this is not easy in stratified analysisIt is an efficient method of data analysisSeveral models for regression exist. Choice depends on the type of data to be analysed.