Confounding Variable

A confounding variable is a factor that can cause or prevent the outcome of interest, is not an intermediate step between exposure and outcome, and is associated with the factor under investigation. Understanding the role and impact of confounding variables is crucial in the design and interpretation of clinical research studies. Below you will find a plain-language definition, what a confounder means in statistics, examples from clinical trials and observational studies, and the main methods researchers use to identify and control confounding.

Confounding variables can significantly influence the results of a study, leading to inaccurate conclusions if not properly controlled. They can introduce bias, distort relationships between variables, and even mask real associations. Therefore, it is essential for researchers to be aware of potential confounders and to take steps to minimize their impact.

Confounding variable: definition and meaning

The term ‘confounding variable’ is derived from the word ‘confound’, which means to mix up or confuse. In the context of clinical research, a confounding variable is an extraneous variable that is associated with both the independent and dependent variables, and can distort the perceived relationship between them. It is a ‘third variable’ that can introduce ‘confounding bias’ into the study.

Confounding variables play a significant role in clinical research. They can lead to erroneous conclusions about the relationship between the variables of interest. For instance, a study might find a strong association between two variables, but this association could be due to the influence of a confounding variable rather than a true cause-and-effect relationship. Therefore, identifying and controlling for confounding variables is a critical aspect of study design and data analysis.

What is a confounding variable in statistics?

In statistics and epidemiology, a variable is treated as a confounder when three conditions are met:

1. it is associated with the exposure or treatment (for example, it is more common in one group than in the other);
2. it is a risk factor for the outcome in its own right, independent of the exposure;
3. it is not caused by the exposure, so it is not a step on the causal pathway (that would make it a mediator).

When all three apply, the crude (unadjusted) estimate of the exposure effect mixes the effect of the exposure with the effect of the confounder. A practical check is to compare the crude estimate with an estimate adjusted for the suspected confounder: if the two differ meaningfully, confounding is likely.

Examples of confounding variables in clinical trials and studies

Confounding variables can be numerous and varied, depending on the nature of the study. For instance, in a study investigating the relationship between smoking and lung cancer, age could be a confounding variable. Older individuals are more likely to have been exposed to other risk factors for lung cancer, such as occupational hazards or air pollution, and are also more likely to have smoked for a longer duration.

Another example could be a study examining the impact of physical activity on heart disease. Here, diet could be a confounding variable. Individuals who engage in regular physical activity might also be more likely to follow a healthy diet, which could independently reduce the risk of heart disease.

Clinical trial example: imagine a non-randomized study in which physicians decide which patients receive a new blood-pressure drug. If they tend to prescribe it to younger patients with milder disease, those patients would have done better anyway. Age and disease severity are then confounders: they are linked to who received the drug and, independently, to the outcome. This pattern is often called confounding by indication, and it is a key reason why randomized, concurrently controlled trials are the preferred design for confirming efficacy.

Identification of Confounding Variables

Identifying potential confounding variables is a critical step in the design of a clinical research study. This process involves considering all the factors that could be associated with both the independent and dependent variables, and could potentially distort the perceived relationship between them. The identification of confounding variables often requires a thorough understanding of the subject matter, as well as a comprehensive review of the existing literature.

Statistical methods can also be used to identify confounding variables. These methods include stratification, regression analysis, and propensity score matching. However, these methods can only identify confounders that have been measured and included in the analysis, and cannot account for unmeasured or unknown confounders.

Stratification

Stratification is a method used to identify confounding variables by dividing the study population into subgroups, or strata, based on the potential confounder. The relationship between the independent and dependent variables is then examined within each stratum. If the stratum-specific estimates are similar to each other but clearly different from the crude estimate for the whole population, the stratifying variable is acting as a confounder. If the estimates genuinely differ from one stratum to the next, the variable is an effect modifier instead (see the comparison below).

For example, in a study investigating the relationship between smoking and lung cancer, the study population could be stratified by age. If the smoking–lung cancer association becomes weaker once people of the same age band are compared with each other, part of the crude association was due to age, which means age was confounding the result.

Regression Analysis

Regression analysis is a statistical method used to identify confounding variables by examining the relationship between the independent and dependent variables while controlling for other variables. If the inclusion of a potential confounder in the regression model changes the estimated effect of the independent variable on the dependent variable, this suggests that the variable is a confounder.

For example, in a study investigating the relationship between physical activity and heart disease, a regression model could be used to estimate the effect of physical activity on heart disease while controlling for diet. If the estimated effect of physical activity changes significantly when diet is included in the model, this suggests that diet is a confounding variable.

How to control confounding

Once confounding variables have been identified, steps must be taken to control their impact on the study results. There are several strategies to control for confounding variables, including randomization, restriction (limiting the study to one level of the confounder, for example only non-smokers), matching, stratification, and statistical adjustment. The choice of method depends on the nature of the study and the specific confounders involved.

It’s important to note that no method can completely eliminate the impact of confounding variables. However, these strategies can help to minimize their influence and improve the validity of the study results.

Randomization

Randomization is a method used to control for confounding variables in experimental studies. In a randomized controlled trial, participants are randomly assigned to the treatment or control group. On average, this balances known and unknown confounding variables between the groups, thereby reducing the potential for confounding bias. In smaller trials, chance imbalances can still occur, which is why trials often stratify randomization by the most important prognostic factors and pre-specify covariate adjustment in the statistical analysis plan, as described in ICH E9.

However, randomization is not always feasible or ethical in clinical research. For instance, it would not be ethical to randomly assign individuals to smoke or not smoke in a study investigating the relationship between smoking and lung cancer. In such cases, other methods must be used to control for confounding variables.

Matching

Matching is a method used to control for confounding variables in observational studies. In a matched study, each participant in the treatment group is matched with one or more participants in the control group who have similar values of the confounding variables. This ensures that the treatment and control groups are similar with respect to the confounders, thereby reducing the potential for confounding bias.

However, matching can be challenging to implement, especially when there are multiple confounders. It can also lead to a loss of statistical power if suitable matches cannot be found for all participants.

Statistical Adjustment

Statistical adjustment is a method used to control for confounding variables in the analysis phase of a study. This involves using statistical techniques, such as regression analysis or propensity score adjustment, to adjust for the effect of the confounders. This allows the researcher to estimate the effect of the independent variable on the dependent variable, controlling for the confounders.

However, statistical adjustment can only control for confounders that have been measured and included in the analysis. It cannot account for unmeasured or unknown confounders. Furthermore, statistical adjustment relies on certain assumptions, such as linearity and additivity, which may not always hold.

Confounder vs. effect modifier vs. mediator

These three terms are easy to mix up, but they call for different handling in the analysis:

Is a confounding variable the same as an extraneous variable?

Not quite. Every confounder is an extraneous variable (a variable outside the exposure–outcome pair you are studying), but an extraneous variable only becomes a confounder when it is related to both the exposure and the outcome.

Does randomization remove confounding completely?

Randomization removes systematic confounding at the moment of allocation, but it cannot rule out chance imbalance, especially in small trials, and it cannot protect against bias introduced later, for example through missing data or protocol deviations. That is why data quality during the trial matters as much as the design.

Conclusion

Understanding the concept of confounding variables is crucial in clinical research. These variables can significantly influence the results of a study, leading to inaccurate conclusions if not properly controlled. Therefore, it is essential for researchers to identify potential confounders and to implement strategies to control their impact.

While no method can completely eliminate the influence of confounding variables, understanding their role and implementing appropriate control strategies can help to minimize their impact and improve the validity of study results. This, in turn, can contribute to the advancement of medical knowledge and the improvement of patient care.

Sources

1. ICH E9: Statistical Principles for Clinical Trials (ICH guideline)
2. ICH E10: Choice of Control Group and Related Issues in Clinical Trials (ICH guideline)
3. Skelly AC, Dettori JR, Brodt ED. Assessing bias: the importance of considering confounding. Evid Based Spine Care J. 2012 (NIH/NCBI PMC)

Where you learn this at VIARES

Randomization and stratified block randomization, the design tools that protect a trial against confounding, are covered in Section 6, “Clinical Trial Design & Clinical Research Roles and Responsibilities”, of the VIARES Clinical Research Associate course. The section’s assignment asks you to explain the bias-reducing effect of design choices such as stratified block randomization, double-dummy, cross-over and factorial designs.