What is a confounding variable?

Identifying and controlling potential confounding variables is the singlemost important task faced by researchers. If a confounding variable is allowedto affect the results of a study, no meaningful conclusions can be drawn fromthe hard work of designing and running the study. Consequently, the vastmajority of research design methodology is devoted to this single task.

What do we do now that we know that hypertension is a confounder?

Confounding variables - Handbook of Biological Statistics

Confounding Variable Definition and Example

There are also analytical techniques that provide a way of adjusting for confounding in the analysis, provided one has information on the status of the confounding factors in the study subjects. These techniques are:

[Solved] The confounding variable hypothesis states …

In this situation, computing an overall estimate of association is misleading. One common way of dealing with effect modification is examine the association separately for each level of the third variable. For example, if one were to calculate the odds ratio for the association between aspirin treatment during a viral infection and development of Reye's syndrome, the odds ratio would be substantially greater in children than in adults. As another example, suppose a clinical trial is conducted and the drug is shown to result in a statistically significant reduction in total cholesterol. However, suppose that with closer scrutiny of the data, the investigators find that the drug is only effective in subjects with a specific genetic marker and that there is no effect in persons who do not possess the marker. The effect of the treatment is different depending on the presence or absence of the genetic marker. This is an example of effect modification or "statistical interaction".

Not surprisingly, since most diseases have multiple contributing causes (risk factors), there are many possible confounders.
Thus, age meets the definition of a confounder (i.e., it is associated with the primary risk factor(obesity) and the outcome (CVD).

leading to an incorrect rejection of the null hypothesis.

Multivariable methods are computationally complex and generally require the use of a statistical computing package. Multivariable methods can be used to assess and adjust for confounding, to determine whether there is effect modification, or to assess the relationships of several exposure or risk factors on an outcome simultaneously. Multivariable analyses are complex, and should always be planned to reflect biologically plausible relationships. While it is relatively easy to consider an additional variable in a multiple linear or multiple logistic regression model, only variables that are clinically meaningful should be included.

For years, studies of heart disease conducted only on men excluded the possible confounding factor of gender.

3.5 - Bias, Confounding and Effect Modification | STAT …

In , factors such as age, gender, and educational levels often have impact on health status and so should be controlled. Beyond these factors, researchers may not consider or have access to data on other causal factors. An example is on the study of smoking tobacco on human health. Smoking, drinking alcohol, and diet are lifestyle activities that are related. A risk assessment that looks at the effects of smoking but does not control for alcohol consumption or diet may overestimate the risk of smoking. Smoking and confounding are reviewed in occupational risk assessments such as the safety of coal mining. When there is not a large sample population of non-smokers or non-drinkers in a particular occupation, the risk assessment may be biased towards finding a negative effect on health.

This textbook has a nice overview of confounding factors and how to account for them in design of experiments:

How do I find confounding variables

The thing that makes random assignment so powerful is that greatly decreases systematic error – error that varies with the independent variable. Extraneous variables that vary with the levels of the independent variable are the most dangerous type in terms of challenging the validity of experimental results. These types of extraneous variables have a special name, confounding variables. For example, instead of randomly assigning students, the instructor may test the new strategy in the gifted classroom and test the control strategy in a regular class. Clearly, ability would most likely vary with the levels of the independent variable. In this case pre-knowledge would become a confounding extraneous variable. (.)