Epidemiology: What is Bias, Chance, and Confounders in Epidemiology?

In this article, I will discuss Bias and its effect and how one can minimize the bias. We will also discuss how we can overcome confounders at various levels of the study.

The p-value is not enough!- in Epidemiology

In my previous post, we looked at the overview of study designs, which try to establish the relation between exposure and outcome, in other words, whether an association exists or not!


Does exposure always mean the outcome?

Sometimes there may be spurious associations because the exposure and outcome are not measured using robust study designs like RCT. For example, there are high rates of sunburns and also high rates of ice cream consumption during hot seasons. But eating ice cream does not lead to sunburn but this third-factor heat is responsible for both high ice-cream eating and sunburns.

Third factor: Tertium quid – Epidemiology

The error in establishing an association may be due to pure chance, bias, or confounders.

Bias is the systematic deviation from the truth due to faulty study design while confounders confuse us by intermingling with the actual cause, check the video below to understand it better.

epidemiologist in search

Statistics and Effect Size in Epidemiology: Must-have tools!

Amidst the confusion created by confounders and other factors not obviously causing the disease, epidemiologist use statistics and derive conclusions using p-value or probability which measure chance! One step ahead of the p-value is measuring the effect size, how big or large the difference between different exposure and the outcome. For example smoking case cancer 20 times more than red meat (only 0.2 times). Hence, the p-value is not enough!

How We Can Overcome Confounders In the Different Stages of Study?

Although confounders can impact the validity of results in studies, we can overcome them by taking certain steps at different stages of the study. Here are some ways to overcome confounders in different stages of a study:

A. Study design:

  • Matching: In observational studies, matching on the confounder can help to control for its effects by ensuring that the exposed and unexposed groups have a similar distribution of the confounder.
  • Stratification: Another way to control for confounders in observational studies is to stratify the data based on the confounder and analyze the exposure-outcome relationship within each stratum.
  • Randomization: In experimental studies, randomization can be used to control for confounders by creating similar distributions of the confounder across the exposure groups.

B. Data collection:

  • Measuring the confounder: It is important to accurately measure the confounder to be able to control its effects.
  • Controlling for the confounder in the study design: The study design can be modified to control for the confounder by including the confounder as a covariate in the analysis.

C. Analysis:

  • Adjustment for confounders: Confounders can be adjusted for in the analysis by including them as covariates in the regression model or by using stratified analysis.
  • Sensitivity analysis: Sensitivity analysis can be performed to assess the impact of the confounder on the exposure-outcome relationship by comparing the results before and after controlling for the confounder.


What is a confounder?

A confounder is a variable that influences the relationship between an exposure and an outcome and confounds the causal relationship between them.

What is the difference between bias and error?

Bias refers to a systematic error or deviation in results or estimates from the true value. Bias can occur in various stages of the research process, including measurement, selection, and analysis. Bias can result in overestimates or underestimates of a parameter of interest.

Error, on the other hand, refers to the random variation or fluctuation in results or estimates from the true value. The error can occur due to random variation in the sample, measurement error, or random variation in the process of estimating the parameter of interest. Error is usually quantified using measures such as standard error.

Why is it important to control for confounders in studies?

Controlling for confounders helps to establish a causal relationship between an exposure and an outcome and minimize the impact of other factors that might influence the relationship.

How can you identify a confounder?

A confounder can be identified as a variable that is associated with both the exposure and the outcome and has a causal relationship with the outcome.

What is the difference between confounding and mediation?

Confounding occurs when a third variable influences the relationship between an exposure and an outcome, while mediation occurs when a variable explains the relationship between an exposure and an outcome.

How can you control for confounders in studies?

Confounders can be controlled for by adjusting for them in the statistical analysis or by matching or stratifying the confounder in the study design.

What is the difference between confounders and confounder surrogates?

Confounders are variables that influence the relationship between an exposure and an outcome, while confounder surrogates are variables that are used as proxies for confounders in studies.

How can confounding be minimized in observational studies?

Confounding can be minimized in observational studies by using a well-designed study, controlling for confounders in the analysis, and using techniques such as matching or stratifying on the confounder.

#confoundingfactors #epidemiology #researchmethods

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