## What are confounders in statistics?

A Confounder is a variable whose presence affects the variables being studied so that the results do not reflect the actual relationship. There are various ways to exclude or control confounding variables including Randomization, Restriction and Matching.

### What is the concept of confounding?

Confounding is a distortion (inaccuracy) in the estimated measure of association that occurs when the primary exposure of interest is mixed up with some other factor that is associated with the outcome. Or, if the age distribution is similar in the exposure groups being compared, then age will not cause confounding.

#### What is a confounding variable example?

A confounding variable is an “extra” variable that you didn’t account for. They can ruin an experiment and give you useless results. For example, if you are researching whether lack of exercise leads to weight gain, then lack of exercise is your independent variable and weight gain is your dependent variable.

**What are potential confounders?**

Potential confounders were defined as variables shown in the literature to be causally associated with the outcome (HIV RNA suppression) and associated with exposure in the source population (hunger) but not intermediate variables in the causal pathway between exposure and outcome [4,31,32].

**How do you identify potential confounders?**

Identifying Confounding A simple, direct way to determine whether a given risk factor caused confounding is to compare the estimated measure of association before and after adjusting for confounding. In other words, compute the measure of association both before and after adjusting for a potential confounding factor.

## How do you address confounding in a study?

Strategies to reduce confounding are:

- randomization (aim is random distribution of confounders between study groups)
- restriction (restrict entry to study of individuals with confounding factors – risks bias in itself)
- matching (of individuals or groups, aim for equal distribution of confounders)

### How do you read a confounding variable?

Identifying Confounding In other words, compute the measure of association both before and after adjusting for a potential confounding factor. If the difference between the two measures of association is 10% or more, then confounding was present. If it is less than 10%, then there was little, if any, confounding.

#### Is time of day a confounding variable?

This third variable could be anything such as the time of day or the weather outside. In this situation, it is indeed the weather that acts as the confound and creates this correlation. Confounding bias is the result of the presence of confounding variables in your experiment.

**How do you know if confounding is present?**

**Is smoking a confounder or effect modifier?**

So, this means that smoking is neither a confounder nor an effect modifier.

## What is a positive confounder?

A positive confounder: the unadjusted estimate of the primary relation between exposure and outcome will be pulled further away from the null hypothesis than the adjusted measure. A negative confounder: the unadjusted estimate will be pushed closer to the null hypothesis.