What is meant by confounding?
tr.v. con·found·ed, con·found·ing, con·founds. 1. To cause to become confused or perplexed. See Synonyms at perplex. 2. To fail to distinguish; mix up: Don't confound fiction and fact. 3. To make (something bad) worse: Do not confound the problem by losing your temper. 4.
What does confounding mean in statistics?
In statistics, a confounder (also confounding variable, confounding factor, extraneous determinant or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association.Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations. The existence of confounders is an important ...
What is a confound in an experiment?
n. in an experiment, an independent variable that is conceptually distinct but empirically inseparable from one or more other independent variables. Confounding makes it impossible to differentiate that variable’s effects in isolation from its effects in conjunction with other variables.
What does confound mean?
Some common synonyms of confound are bewilder, distract, dumbfound, nonplus, perplex, and puzzle. While all these words mean "to baffle and disturb mentally," confound implies temporary mental paralysis caused by astonishment or profound abasement.
How do you explain confounding?
A confounder can be defined as a variable that, when added to the regression model, changes the estimate of the association between the main independent variable of interest (exposure) and the dependent variable (outcome) by 10% or more.
How do you identify confounding factors in a study?
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.
What is confounding in study design?
Confounding occurs when the exposure effect mixes with effects of other risk factors for the outcome differentially across the exposed and unexposed. Confounding by known factors can be addressed through study design or analytically, but confounding by unmeasured factors may remain.
What is an example of a confounding variable?
Example of a confounding variable You collect data on sunburns and ice cream consumption. You find that higher ice cream consumption is associated with a higher probability of sunburn. Does that mean ice cream consumption causes sunburn?
How do you know if something is a confounding variable?
A confounding variable is one that has an impact on both the dependent and independent variable. It is possible that the amount of sleep a student gets is related to caffeine intake, which in turn affects the grade a student receives on a test or assignment.
How can we prevent confounding in research?
To control for confounding in the analyses, investigators should measure the confounders in the study. Researchers usually do this by collecting data on all known, previously identified confounders. There are mostly two options to dealing with confounders in analysis stage; Stratification and Multivariate methods.
What is confounding error in research?
What is confounding? Confounding is the distortion of the association between an exposure and health outcome by an extraneous, third variable called a confounder.
What type of bias is confounding?
The bias that results from inadequate adjustment of a covariate that is simultaneously predictive of treatment and outcome, for example, has been referred to as “confounding bias”, “confounding by indication bias” and “treatment-selection bias”.
How do confounding variables affect a research study?
A confounding variable, in simple terms, refers to a variable that is not accounted for in an experiment. It acts as an external influence that can swiftly change the effect of both dependent and independent research variables; often producing results that differ extremely from what is the case.
What are possible confounding factors?
For example, in looking at the association between exercise and heart disease, other possible confounders might include age, diet, smoking status and a variety of other risk factors that might be unevenly distributed between the groups being compared.
What are the 3 criteria for categorizing a confounding?
This paper explains that to be a potential confounder, a variable needs to satisfy all three of the following criteria: (1) it must have an association with the disease, that is, it should be a risk factor for the disease; (2) it must be associated with the exposure, that is, it must be unequally distributed between ...
How do you identify extraneous and confounding variables?
An extraneous variable is any variable that you're not investigating that can potentially affect the dependent variable of your research study. A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.
How is confounding measured?
The 10% Rule for 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): If the % difference is 10% or greater, we conclude that there was confounding.
What are possible confounding factors?
For example, in looking at the association between exercise and heart disease, other possible confounders might include age, diet, smoking status and a variety of other risk factors that might be unevenly distributed between the groups being compared.
What is a confounding variable?
A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect r...
What is the difference between confounding variables, independent variables and dependent variables?
A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the su...
What’s the difference between extraneous and confounding variables?
An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study....
Why do confounding variables matter for my research?
To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you mig...
How do I prevent confounding variables from interfering with my research?
There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical contro...
What is a confounding variable?
Confounding variables (a.k.a. confounders or confounding factors) are a type of extraneous variable that are related to a study’s independent and dependent variables. A variable must meet two conditions to be a confounder:
What is the difference between an independent and a confounding variable?
An independent variable represents the supposed cause, while the dependent variable is the supposed effect. A confounding variable is a third variable that influences both the independent and dependent variables. Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent ...
Why is randomization important in a study?
Randomization ensures that with a sufficiently large sample, all potential confounding variables—even those you cannot directly observe in your study—will have the same average value between different groups. Since these variables do not differ by group assignment, they cannot correlate with your independent variable and thus cannot confound your study.
What is the effect of a potential confounding variable on the dependent variable?
Any effect that the potential confounding variable has on the dependent variable will show up in the results of the regression and allow you to separate the impact of the independent variable. Statistical control example.
How to minimize the impact of confounding variables?
Randomization. Another way to minimize the impact of confounding variables is to randomize the values of your independent variable. For instance, if some of your participants are assigned to a treatment group while others are in a control group, you can randomly assign participants to each group.
What is an extraneous variable?
An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study. A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.
What is the purpose of confounding variables in research?
To ensure the internal validity of your research, you must account for confounding variables. If you fail to do so, your results may not reflect the actual relationship between the variables that you are interested in..
What is negative confounding?
Confounding can bias the primary measure of association toward the null, causing an underestimate of the association. This is referred to as negative confounding. As illustrated below, if the true (adjusted) risk ratio or odds ratio was 3 and the crude, i.e., confounded estimate was OR or RR=2, that would be an underestimate. Similarly, if the true ratio was 0.25 (a preventive effect) and the crude estimate was 0.5, that would be an underestimate of the preventive effective.
What is crude measure of association?
A crude measure of association is one that has not yet been adjusted for confounding factors, while an adjusted measure of association is one that has been adjusted to minimize confounding and provides an estimate that is closer to the true value. Confounding can bias the primary measure of association toward the null, ...
What is the unequal distribution of age?
The unequal distribution of age (another risk factor) exaggerates the apparent effect of inactivity. Age differences between active and sedentary people confound the association between activity and CHD. Confounding is the distortion of a measure of association that occurs when other risk factors for the outcome are unevenly distributed between the groups being compared
How are negative and positive confounding distinguished?
So, negative and positive confounding are distinguished not by whether it causes the measure of association to appear smaller or larger than the true value, but by whether it causes the estimated measure of association to move toward the null (negative) or away from the null (positive).
Is type 2 diabetes a confounding factor?
Given this sequence of events in the causal chain, type 2 diabetes would not be considered a confounding factor for the association between obesity and coronary artery disease, because it is the mechanism by which obesity leads to coronary heart disease.
Is a confounding factor an intermediary factor?
3) A confounding factor cannot be an intermediary factor in the causal pathway between the exposure and the outcome. For example, obesity is a cause of type 2 diabetes, and type 2 diabetes is a cause of coronary heart disease.
Is age an independent risk factor for CHD?
In this example, older age is an independent risk factor for CHD.
What is confounding in science?
What is confounding? Confounding is often referred to as a “mixing of effects”1,2wherein the effects of the exposure under study on a given outcome are mixed in with the effects of an additional factor (or set of factors) resulting in a distortion of the true relationship.
How to adjust for confounding?
There are a number of ways of assessing and adjusting for confounding, however a detailed discussion of this is beyond the scope of this article. Briefly, a few examples of how this could be accomplished include: 1 During study planning, inclusion could be restricted by specific confounding variables, such as age. 2 Several methods of “adjusting” the effect estimate as part of the analysis can be used. Stratification (as shown above) is one that can be relatively straightforward and involves looking at the association between the exposure and outcome for each factor category (or stratum) by calculating a stratum-specific estimate. 3 Multivariate analysis, a set of statistical methods which allows for adjustment of multiple variables simultaneously via mathematical modeling, can also be used to “control” for confounding.
What is multivariate analysis?
Multivariate analysis, a set of statistical methods which allows for adjustment of multiple variables simultaneously via mathematical modeling, can also be used to “control” for confounding .
What are patient characteristics?
Patient characteristics are an often underreported or misreported set of measurements in spine care studies but are extremely important to quantify and report as they may be potential confounders. Diagnostic features, comorbidities, and any factor that might affect patient outcome needs to be measured and reported for each study group as well. Any and all of these characteristics, features, and factors may be potential confounders of the relationship between your “exposure of interest” (eg, a surgical treatment) and the outcome (eg, patient function). Planning for and measuring these attributes goes a long way toward dealing with the role of confounding.
What is stratification analysis?
Stratification (as shown above) is one that can be relatively straightforward and involves looking at the association between the exposure and outcome for each factor category (or stratum) by calculating a stratum-specific estimate.
Is smoking a factor in vertebroplasty?
Thus, smoking was a confounding factor distorting the true relationship between vertebroplasty and the risk of subsequent vertebral fractures.
What are the three categories of significance testing?
Bias can be divided into three general categories: (1) selection bias; (2) information bias; and (3) confounding. This article focuses on confounding.
WHEN ARE VARIABLES POTENTIAL CONFOUNDERS?
In order for a variable to be a potential confounder, it needs to have the following three properties: (1) the variable must have an association with the disease, that is , it should be a risk factor for the disease; (2) it must be associated with the exposure, that is, it must be unequally distributed between the exposed and nonexposed groups; and (3) it must not be an effect of the exposure, nor (linked to this) be a factor in the causal pathway of the disease.
What is stratification in science?
Stratification is an effective means for adjusting for confounding when the number of confounding factors is limited. Increasing the number of these factors will rapidly increase the number of strata, as the numbers of categories are multiplied. The stratification for sex and for the four age categories will use eight strata;
How to control confounding for age?
Another method of controlling confounding for age during study design is matching . In a cohort study, the patients in the exposed and unexposed groups could be matched in pairs for potential confounders. In the study on the relationship between diabetes and ischemic heart disease, for each ‘exposed’ person with diabetes mellitus the investigator may select an ‘unexposed’, that is, nondiabetic, patient of the same age. In this way, the potential confounding effect of age on outcome will be reduced. In cohort studies, the technique of matching is infrequently used, and it may be viewed as a special case of stratification (see later). In case–control studies, however, matching is frequently used. Still, the choice of matching variables needs careful attention because, as will be described later, errors are frequently made.
How to avoid confounding in a study?
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. Obviously, adjusting for confounding at this later stage can only take place if information on the confounding factors has been collected during the study.
What happens when you match a confounder?
Therefore, in case–control studies, matching for confounding may result in overadjustment and even introduce confounding.
What are some examples of incorrect matching?
An example of incorrect matching would be if these investigators would have decided to match for BMI (or even for glucose intolerance), which are factors that may result from the same polymorphism and may even be in the causal pathway. Most of the real effect of the polymorphism would disappear, and the adjusted odds ratio for the effect of the polymorphism on the development of diabetic nephropathy would be much closer to 1.0.
What is confounding bias?
epidemiology. Confounding, sometimes referred to as confounding bias, is mostly described as a ‘mixing’ or ‘blurring’ of effects. 1 It occurs when an investigator tries to determine the effect of an exposure on the occurrence of a disease (or other outcome), but then actually measures the effect of another factor, a confounding variable.
Why Are Confounding Variables Problematic?
1. Confounding variables can make it seem that cause-and-effect relationships exist when they don’t.
What is a confounding variable?
Confounding variable: A variable that is not included in an experiment, yet affects the relationship between the two variables in an experiment. This type of variable can confound the results of an experiment and lead to unreliable findings. For example, suppose a researcher collects data on ice cream sales and shark attacks and finds ...
How does confounding affect the validity of a study?
In technical terms, confounding variables affect the internal validity of a study, which refers to how valid it is to attribute any changes in the dependent variable to changes in the independent variable. When confounding variables are present, we can’t always say with complete confidence that the changes we observe in ...
What is the independent variable of a diet?
The independent variable is the new diet and the dependent variable is the amount of weight loss. However, a confounding variable that will likely cause variation in weight loss is gender. It’s likely that the gender of an individual will effect the amount of weight they’ll lose, regardless of whether the new diet works or not.
What are the two main variables in an experiment?
In any experiment, there are two main variables: The independent variable: the variable that an experimenter changes or controls so that they can observe the effects on the dependent variable. The dependent variable: the variable being measured in an experiment that is “dependent” on the independent variable. ...
What is the practice of dividing individuals in a study into “blocks” based on some value of?
2. Blocking . Blocking refers to the practice of dividing individuals in a study into “blocks” based on some value of a confounding variable to eliminate the effect of the confounding variable. For example, suppose researchers want to understand the effect that a new diet has on weight less.
What is random assignment?
Random assignment refers to the process of randomly assigning individuals in a study to either a treatment group or a control group. For example, suppose we want to study the effect of a new pill on blood pressure.
How to reduce the impact of confounding variables
It is important to identify all possible confounding variables and consider the impact of them in your research design in order to ensure the internal validity of your results.
Frequently asked questions about confounding variables
1. What is the difference between an extraneous variable and a confounding variable?
