What is ‘confounding’ in etiology?
Confounding in etiological studies can be described as a ‘mixing’ of effects distorting the real effect of an exposure. As a result, a crude effect may not equal the ‘true’ effect of a risk factor.
What is an example of a confound in psychology?
Confounding occurs when a factor is associated with both the exposure and the outcome but does not lie on the causative pathway. For example, if you decide to look for an association between coffee and lung cancer, this association may be distorted by smoking if smokers are unevenly distributed between the two groups.
What are confounding factors and why are they important?
Confounding factors are a “nuisance” and can account for all or part of an apparent association between an exposure and a disease. Confounding factors simply need to be eliminated to prevent distortion of results.
How do you deal with confounding in research?
Like other types of bias, confounding can be addressed during study design. At that stage, confounding can be prevented by use of randomization, restriction, or matching.
Do confounding variables affect reliability?
The “something else” would be a confounding variable, defined as “an unforeseen and unaccounted-for variable that jeopardizes the reliability and validity of an experiment's outcome.”
Which of the following is used to reduce the effects of confounding variables in experiments?
There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization. In restriction, you restrict your sample by only including certain subjects that have the same values of potential confounding variables.
How do confounding variables affect results?
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.
When confounds are present in an experiment they result in?
If other variables differ between control and experimental groups, then the other variables are said to be confounds (i.e., variables that might influence the dependent variable and thereby negate the ability to make a cause-effect conclusion).
What is the difference between positive and negative confounding?
Confounding: A situation in which a measure of association or relationship between exposure and outcome is distorted by the presence of another variable. Positive confounding (when the observed association is biased away from the null) and negative confounding (when the observed association is biased toward the null) both occur.
How to consider effect modification?
To consider effect modification in the design and conduct of a study: Collect information on potential effect modifiers. Power the study to test potential effect modifiers - if a priori you think that the effect may differ depending on the stratum, power the study to detect a difference.
What is an effect modifier?
Effect modifier is a variable that differentially (positively and negatively) modifies the observed effect of a risk factor on disease status. Consider the following examples: The immunization status of an individual modifies the effect of exposure to a pathogen and specific types of infectious diseases.
How to increase precision of effect estimation?
to increase precision of effect estimation by taking into account groups that may be affected differently, to increase the ability to compare across studies that have different proportions of effect-modifying groups, and. to aid in developing a causal hypotheses for the disease.
What is bias in a study?
Bias Resulting from Study Design. Bias limits validity (the ability to measure the truth within the study design) and generalizability (the ability to confidently apply the results to a larger population) of study results. Bias is rarely eliminated during analysis. There are two major types of bias:
Why is the relationship between an outcome and smoking underestimated?
If controls are selected among hospitalized patients, the relationship between an outcome and smoking may be underestimated because of the increased prevalence of smoking in the control population. In a cohort study, people who share a similar characteristic may be lost to follow-up.
Is misclassification conditional upon exposure or disease status?
the probability of misclassification does not vary for the different study groups; is not conditional upon exposure or disease status, but appears random. Using the above example, if half the subjects (cases and controls) were randomly selected to be interviewed by the phone and the other half were interviewed in person, the misclassification would be nondifferential.
Why is confounding considered undesirable?
As most medical studies attempt to investigate disease etiology and causal relationships, confounding is regarded as undesirable, as it obscures the ‘real’ effect of an exposure. For this reason, confounding is something that investigators want to get rid of, for example, by so-called ‘adjustment for confounding variables’.
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.
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;
What happens when you match a confounder?
Therefore, in case–control studies, matching for confounding may result in overadjustment and even introduce confounding.
Does restriction take away confounding?
Although restriction would, at least partially, take away confounding by age, it hampers extrapolation of study results to other groups, in this case to patients below the age of 65 years. Another method of controlling confounding for age during study design is matching.
Is age a confounder in a study?
In such a study, age would be a potential confounder. The simplest way of controlling for confounding by age during the study design would be using restriction. The investigators might restrict their study to the group of dialysis patients above the age of 65 years.
What is confounding in health?
Confounding. Confounding occurs when a factor is associated with both the exposure and the outcome but does not lie on the causative pathway. For example, if you decide to look for an association between coffee and lung cancer, this association may be distorted by smoking if smokers are unevenly distributed between the two groups.
Why do confounding factors need to be eliminated?
Confounding factors simply need to be eliminated to prevent distortion of results. Effect Modification is not a “nuisance”, it in fact provides important information. The magnitude of the effect of an exposure on an outcome will vary according to the presence of a third factor.
What is an example of effect modification?
For example, imagine you are testing out a new treatment that has come onto the market, Drug X. If Drug X works in females but does not work in males, this is an example of effect modification.
Why Is Causality So Hard?
In my previous posts, I’ve discussed methodologies for calculating causal effects in scenarios in which we observe an explanatory variable and an outcome variable and wish to quantify a causal relationship.
Eliminating Confounding Bias, Achieving Independence
Remember that mathematically, “eliminating confounding bias” consists of ensuring independence between the potential outcomes Y x \color {#EF3E36}Y_x Y x of an observed individual and the corresponding value of an explanatory variable X i \color {#7A28CB}X_i X i , as if no confounding variable were present.