Confounding.pptx

Confounding

1. Confounding Dr Vinodh Kumar O.R Senior Scientist Division of Epidemiology

2. Introduction Confounder  (also confounding variable, confounding factor, or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association . Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations . When a non-casual association is observed between a given exposure and outcome is as a result of the influence of a third variable, it is termed confounding, with the third variable termed a confounding variable . A confounding variable is causally associated with the outcome of interest , and non-causally or causally associated with the exposure , but is not an intermediate variable in the causal pathway between exposure and outcome ( Szklo & Nieto, 2007).

3. There are three conditions that must be present for confounding to occur The confounding factor must be associated with both the risk factor of interest and the outcome. The confounding factor must be distributed unequally among the groups being compared. A confounder cannot be an intermediary step in the causal pathway from the exposure of interest to the outcome of interest.

4. Confounding example

5. Confounding example

6. Confounding example Demonstrates that coffee (exposure) was associated with an increased risk of developing pancreatic cancer (disease) with the dark arrow. A third factor, smoking, which is a confounder is actually correlated with an increased risk of developing pancreatic cancer (light arrow). Coffee was spuriously correlated with increasing the risk of developing pancreatic cancer and once the confounding variable, smoking is taken into account the correlation between coffee and pancreatic cancer disappears. It should be noted that both smoking and coffee are also both correlated for confounding to occur (two-way arrow).

7. Controlling of confounding Case-control studies : Assign confounders to both groups, cases and controls, equally. Cohort studies : A degree of matching is also possible and it is often done by only admitting certain age groups or a certain sex into the study population, creating a cohort of people who share similar characteristics and thus all cohorts are comparable in regard to the possible confounding variable. Double blinding : Conceals from the trial population and the observers the experiment group membership of the participants.

8. Controlling of confounding Randomized controlled trial: The study population is divided randomly in order to mitigate the chances of self-selection by participants or bias by the study designers. Stratification:   for small number of confounders.

9. Types O perational confounding: Occur in both experimental and non-experimental research designs. This type of confounding occurs when a measure designed to assess a particular construct inadvertently measures something else as well. Procedural confounding: O ccur in a laboratory experiment or a quasi-experimen t. This type of confound occurs when the researcher mistakenly allows another variable to change along with the manipulated independent variable. Person confounding: O ccurs when two or more groups of units are analyzed together (e.g., workers from different occupations), despite varying according to one or more other (observed or unobserved) characteristics (e.g., gender ).

10. Effects of Confounding May cause an overestimate of the true association (positive confounding) or an underestimate of the association (negative confounding).

11. Preventive steps Randomization is the best way to reduce the risk of confounding. Stratification and statistical adjustment can reduce the risk of confounding. Use of propensity scores, in which potential confounders are used to build a statistical model that assigns to each person a number called their propensity score: the people with high scores are more likely to have certain confounders, and those with low scores are less likely.  A very large effect size can outweigh the combined effects of plausible confounders.

12. Statistical methods Small number of potential confounders: Stratification Larger number of potential confounders : multivariate analysis, logistic or linear regression

13. Determining Whether a Variable is a Confounder Perform formal tests of hypothesis to assess whether the variable is associated with the risk factor and with the outcome. Other investigators do not conduct statistical tests but instead inspect the data, and, if there is a practically important or clinically meaningful relationship between the variable and the risk factor and between the variable and the outcome (regardless of whether that relationship reaches statistical significance), the variable is said to be a confounder. Still other investigators determine whether there is confounding by estimating the measure of association before and after adjusting for a potential confounding variable . A change in the estimated measure of association of 10% or more would be evidence that confounding was present, but if the measure of association changes by <10%, there is likely to be little, if any, confounding by that variable. Using the example above, the adjusted risk ratio would be about 1.43 and the crude risk ratio (before adjustment) = 1.78. So the % difference = (1.78-1.43)/1.43 = 24.5% difference. Therefore, there was confounding by age. This is discussed further in the section on multiple linear regression later in this module .

14. Identification of confounder in SPSS by regression

15. Effect modifier Presence or absence of an effect modifier changes the association of an exposure with the outcome of interest . The situation where the magnitude of the effect of an exposure variable on an outcome variable differs depending on a third variable. occurs when an exposure has a different effect among different subgroups. Effect modification is associated with the outcome but not the exposure . For example, imagine you are testing out a new treatment that has come onto the market, Drug X. If Drug X works in females but does not work in males, this is an example of effect modification . It provides important information. The magnitude of the effect of an exposure on an outcome will vary according to the presence of a third factor.

16. Confounding factors  are a “nuisance” and can account for  all  or  part  of an apparent association between an exposure and a disease. Confounding factors simply need to be eliminated to prevent distortion of results.