Confounding is
one type of systematic error
that can occur in epidemiologic studies. … Confounding is the distortion of the association between an exposure and health outcome by an extraneous, third variable called a confounder.
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 in epidemiology?
In epidemiologic terms, the tobacco companies were claiming that air pollution (or any other factor that can cause cancer) is a confounding variable. A confounding variable is a
variable (say, pollution) that can cause the disease under study (cancer) and is also associated with the exposure of interest (smoking)
.
How do you identify confounding in epidemiology?
Most epidemiologists use
a 10% difference
as a “rule of thumb” for identifying the presence of confounding. The magnitude of confounding is the percent difference between the crude and adjusted measures of association, calculated as follows (for either a risk ratio or an odds ratio):
What is a confounder in a study?
A Confounder is
an extraneous variable whose presence affects the variables being studied so
that the results do not reflect the actual relationship between the variables under study. The aim of major epidemiological studies is to search for the causes of diseases, based on associations with various risk factors.
How do you know if confounding is present?
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.
Why does confounding occur?
Confounding occurs when
the experimental controls do not allow the experimenter to reasonably eliminate plausible alternative explanations for an observed relationship between independent and dependent variables
. … As a result, many variables are confounded, and it is impossible to say whether the drug was effective.
What’s an example of confounding?
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.
Is time a confounding variable?
Here, we consider “time-modified confounding,” which occurs when there is a
time-fixed or time-varying cause of disease
that also affects subsequent treatment, but where the effect of this confounder on either the treatment or outcome changes over time.
How do you stop confounding?
- 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)
What is the difference between covariate and confounder?
Confounders are variables that are
related to both the intervention and the outcome
, but are not on the causal pathway. … Covariates are variables that explain a part of the variability in the outcome.
What is the role of confounding factors in epidemiology?
Confounding is an important concept in epidemiology, because, if present,
it can cause an over- or under- estimate of the observed association between exposure and health outcome
. The distortion introduced by a confounding factor can be large, and it can even change the apparent direction of an effect.
How can confounding be controlled in epidemiology?
At that stage, confounding can be prevented by use of randomization, restriction, or matching. In contrast to other types of bias, confounding can also be controlled
by adjusting for it after completion of a study using stratification or multivariate analysis
.
What is the most common confounder in genomics?
Common confounders are attributes of the participants; for example, body mass index,
smoking status
, age at onset of illness, socioeconomic status, educational status, and extent of support network. Life events are also potential confounders.
What happens when we ignore confounding?
Ignoring confounding when assessing the associ- ation between an exposure and an outcome variable can lead to
an over- estimate or underestimate of the true association between exposure and outcome
and can even change the direction of the observed effect.
How do you prove confounder?
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.
