9.3.6 Confounding

Describes Confounding and Strategies to Reduce the Risk of Confounding: Randomisation, Restriction, Matching and Adjustment Using Stratification or Multivariable Regression Models

Confounding is a phenomenon in research where a third variable, called the confounder, affects the relationship between the independent variable and the dependent variable. The confounder is related to both the exposure and the outcome, creating a false association or obscuring the true relationship between the variables of interest. Confounding can lead to biased results and incorrect conclusions, making it essential to address it in research design and analysis.

Randomisation:

Randomization is the process of randomly allocating participants to different groups or treatment conditions in a study, ensuring that each participant has an equal chance of being assigned to any group. Randomization helps to balance confounding variables across the groups, minimizing the impact of potential confounders on the results. This method is most commonly used in experimental studies, such as randomized controlled trials.

Restriction:

Restriction involves limiting the study participants to a specific subgroup based on certain characteristics or criteria. By restricting the study population, researchers can control for potential confounders by excluding individuals with certain characteristics that may influence the relationship between the exposure and outcome. However, this approach may limit the generalizability of the study findings to the broader population.

Matching:

Matching is a technique used in observational studies to control for confounding by selecting comparison groups that are similar in terms of the confounding variables. In case-control studies, for example, researchers can match cases and controls based on specific characteristics, such as age, sex, or other relevant factors. Matching ensures that the distribution of potential confounders is similar between the groups, reducing the risk of confounding. However, it may be challenging to find appropriate matches, and the process can be time-consuming.

Adjustment using stratification or multivariable regression models:

Adjustment is a statistical technique that can be used to control for confounding during data analysis. Stratification involves dividing the data into separate strata based on the levels of the confounding variable and analyzing the relationship between the exposure and outcome within each stratum. This allows researchers to estimate the association between the exposure and outcome while controlling for the confounding variable.

Topic	Summary
Confounding	Confounding occurs when a variable is related to both the exposure and outcome, and distorts the true relationship between them.
Randomisation	Randomisation is the process of assigning participants to either the intervention or control group at random, which helps to reduce confounding.
Restriction	Restriction involves limiting the study sample to a specific group, based on certain characteristics, which can reduce the potential for confounding.
Matching	Matching is the process of selecting control group participants who have similar characteristics to the intervention group participants, which helps to reduce the potential for confounding.
Adjustment	Adjustment involves statistical techniques, such as stratification or multivariable regression models, to control for the confounding variable and reduce the risk of bias.

Overall, it is important for researchers to consider the potential for confounding in their study designs and to use appropriate strategies to reduce its impact.

References:

1. Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Philadelphia: Lippincott Williams & Wilkins.
2. Vandenbroucke, J. P., von Elm, E., Altman, D. G., Gøtzsche, P. C., Mulrow, C. D., Pocock, S. J., … & STROBE Initiative. (2007). Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration. PLoS medicine, 4(10), e297.