Confounding can hide a real effect, or can produce the spurious appearance of a treatment effect when the real cause is a difference between the treatment and control groups other than the treatment. Individuals' responses to treatment differ, as do individuals' responses in the absence of treatment.
Full Answer
What is confoundconfounding in clinical research?
Confounding is an ever-present problem in clinical research and can obscure or confuse our interpretation of study results. Various study design and data analysis techniques are available, however, to help eliminate or reduce bias caused by confounding.
What are the conditions necessary for confounding to occur?
One of the conditions necessary for confounding to occur is that the confounding factor must be distributed unequally among the groups being compared. Consequently, one of the strategies employed for avoiding confounding is to restrict admission into the study to a group of subjects who have the same levels of the confounding factors.
What are the effects of confounding in psychology?
Effects of Confounding. May account for all or part of an apparent association. May cause an overestimate of the true association (positive confounding) or an underestimate of the association (negative confounding). The magnitude confounding can be quantified by computing the percentage difference between the crude and adjusted measures of effect.
How does confounding affect the validity of a study?
In this case the researchers are said to account for their effects to avoid a false positive (Type I) error (a false conclusion that the dependent variables are in a casual relationship with the independent variable). Thus, confounding is a major threat to the validity of inferences made about cause and effect (internal validity).
How does confounding variables affect the validity of the study?
To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables, or even find a causal relationship where none exists.
Which of the following is true about confounding variables?
Yeah, for the answer, the answer is a confounding variable creates an association that is misleading. This is the correct choice for the answer. That is a confounding variable creates an association that is misleading as confounded as can bias your study results and lead to erroneous conclusions here.
What is confounding quizlet?
What is meant by confounding? Confounding in a study occurs when the effects of two or more explanatory variables are not separated. Therefore, any relation that may exist between an explanatory variable and the response variable may be due to some other variable or variables not accounted for in the study.
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.”
Can confounding variables be controlled?
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 confounding in research?
What is confounding? Confounding is often referred to as a “mixing of effects”1,2 wherein 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.
What is it meant by confounding?
Confounding means the distortion of the association between the independent and dependent variables because a third variable is independently associated with both. A causal relationship between two variables is often described as the way in which the independent variable affects the dependent variable.
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.
What is a confounding variable in psychology quizlet?
Confounding variable. an extraneous variable whose presence affects the variables being studied so that the results you get do not reflect the actual relationship between the variables under investigation.
How do confounding variables affect results?
Since a confounding variable is a 3rd factor that is not accounted for in a research process, it can affect an experiment by producing inaccurate research results. For example, it can suggest a false correlational relationship between dependent and independent variables.
How do confounding variables matter in research?
Confounding variables are the extra, unaccounted-for variables that can stealthily have a hidden impact on the outcome being explored. The results of any study can easily be distorted due to one or more confounding variables.
When confounds are present in an experiment they result in group of answer choices?
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 confounding in psychology?
Confounding is a distortion of the association between an exposure and an outcome that occurs when the study groups differ with respect to other factors that influence the outcome. Unlike selection and information bias, which can be introduced by the investigator or by the subjects, confounding is a type of bias that can be adjusted for in ...
What is confounding by indication?
This type of confounding arises from the fact that individuals who are prescribed a medication or who take a given medication are inherently different from those who do not take the drug, because they are taking the drug for a reason. In medical terminology, such individuals have an "indication" for use of the drug. Even if the study population consists of subjects with the same disease, e.g., osteoarthritis, they may differ in the severity of their disease and may therefore differ in the need for medication. Aschengrau and Seage give the example of studies of the association between antidepressant drug use and infertility. The use of antidepressant medications may appear to be associated with an increased risk of infertility. However, depression itself is a known risk factor for infertility. As a result, there would appear to be an association between antidepressants and infertility. One way of dealing with this is to study the association in subjects who are receiving different treatments for the same underlying disease condition.
What are the disadvantages of stratifying?
However, a major disadvantage to stratification is its inability to control simultaneously for multiple confounding variables. For example, you might decide to control for gender, 3 levels of smoking exposure, 4 levels of age, and 4 levels of BMI. This would require 96 separate strata to control for all of these variables simultaneously, and as you increase the number of strata, you keep whittling away at the number of people in each stratum, so sample size becomes a major problem, since many of the strata will contain few or no people.
What are the conditions necessary for confounding?
There are three conditions that must be present for confounding to occur: The confounding factor must be associated with both the risk factor of interest and the outcome. The confounding factor must be distributed unequally among the groups being compared.
Why is age a confounding factor?
Age is a confounding factor because it is associated with the exposure (meaning that older people are more likely to be inactive), and it is also associated with the outcome (because older people are at greater risk of developing heart disease).
How to determine if a risk factor caused 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. 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. How to do this will be addressed in greater detail below.
What is effect modification?
The term effect modification is applied to situations in which the magnitude of the effect of an exposure of interest differs depending on the level of a third variable. Reye's syndrome is a rare, but severe condition characterized by the sudden development of brain damage and liver dysfunction after a viral illness. The syndrome is most commonly seen in children between the ages of 4-14 who have been treated with aspirin while recovering from a viral illness, most commonly chickenpox or influenza. Fortunately, Reye's syndrome has become very uncommon since aspirin is no longer recommended for routine use in children. While Reye's syndrome can occur in adults, it is distinctly more common in children. Thus, the effect of aspirin treatment for a viral illness is very clearly modified by age.
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.
What is a confounding variable?
Confounding variables or confounders are often defined as the variables correlate (positively or negatively) with both the dependent variable and the independent variable (1). A Confounder is an extraneous variable whose presence affects the variables being studied so that the results do not reflect the actual relationship between ...
Why are confounders not adjusted?
In many studies, confounders are not adjusted because they were not measured during the process of data gathering. In some situation, confounder variables are measured with error or their categories are improperly defined (for example age categories were not well implied its confounding nature) (10).
When is matching used in case control studies?
Matching is commonly used in case-control studies (for example, if age and sex are the matching variables, then a 45 year old male case is matched to a male control with same age). But all these methods mentioned above are applicable at the time of study design and before the process of data gathering.
How does stratified analysis work?
Stratified analysis works best in the way that there are not a lot of strata and if only 1 or 2 confounders have to be controlled. If the number of potential confounders or the level of their grouping is large, multivariate analysis offers the only solution.
Why is confounding important in clinical trials?
Confounding produces a challenge in interpreting data from clinical trials and observational studies, and it is crucial to identify and then eliminate or reduce potential confounding variables when appropriate and feasible.
Why is confounding important in research?
Understanding confounding is important for conducting a good research study. Study design techniques provide the best way to control for confounders, but when not possible to alter study design, data analysis techniques can also provide an effective control.
What is a confounder in neurosurgery?
A confounder is a variable that is unevenly distributed between study groups and is associated with either the intervention or outcome. If the confounder is not recognized, it will give the false impression that the potential predictor is the causal factor, rather than the confounder. A common example of this is when a certain prognostic variable is distributed in an unbalanced fashion between treatment arms, giving the false impression that the better outcomes seen in one of the treatment arms is because of the treatment itself, rather than the over-representation of the favorable prognostic variable. An example from pediatric neurosurgery is seen when treatment failure outcomes of endoscopic third ventriculostomy (ETV) are compared to ventriculoperitoneal shunt for hydrocephalus. 2 Comparing the nonrandomized data of a moderately large cohort, one gets the impression that ETV results in a more favorable time-to-failure outcome (Figure 1 ). 2 However, what is not accounted for in this raw analysis is that ETV patients were chosen to undergo this procedure by their surgeons when they met specific age and etiology criteria. Specifically, ETV patients were generally older and rarely had etiologies like myelomeningocele, postinfectious hydrocephalus, or posthemorrhagic hydrocephalus of prematurity. Therefore, there was an over-representation of better prognostic factors (older age and etiologies like aqueductal stenosis) in the ETV group. So, in this example, both age and etiology are acting as confounders. When the effect of these confounding variables is minimized via post hoc statistical analyses (in this case, propensity score matching—a technique we will describe later), the comparison of the time-to-failure outcome looks much less favorable for ETV than it had before the confounding had been accounted for (Figure 2 ). 2
Why is randomization important?
The main purpose of randomization is to avoid systematic bias of confounding by balancing the distribution of patient characteristics that may influence the outcome (potential confounders) between treatment arms. The principle is simple: if only pure chance determines which intervention a patient receives, removed from the potential biases of patients and physicians, then, over the course of enough patients, chance should provide a roughly equal distribution of patient characteristics between the intervention arms. This would eliminate one of the requirements of confounding: that it must be associated with the intervention. When randomized controlled trials (RCTs) are well powered, they offer the best method to interpret evidence-based causations between interventions and outcomes. Importantly, randomization is the only technique available to control for the effect of both known and unknown confounders. All other techniques require the explicit identification of confounders. In this way, randomization remains the gold standard for drawing causal inference for interventions.
What is stratified randomization?
Unlike blocked randomization, stratified randomization is aimed at trying to balance a potential confounding variable between treatment arms. This technique involves using separate randomization lists for each subgroup of patients within a potential confounder. The investigators of the Swedish Spinal Stenosis Study, 6 for example, evaluated postoperative outcomes after treatment of spinal stenosis by decompression alone or decompression and fusion. The authors felt, however, that preoperative degenerative spondylolisthesis (DS) could be a potential confounder. They, therefore, stratified the population by the presence or absence of DS, each within its own randomization scheme, which was further block randomized to ensure balance in treatment arm allocation. In this case, it was ultimately shown that the presence or absence of DS resulted in outcomes that were similar ( P > .05 for all outcomes), and the authors concluded DS was not a confounding variable. Stratified randomization can be extended to stratify based on 2 or more potential confounders; however, this can result in a dramatic reduction in sample size of each group, making it impractical for smaller studies.
Why are RCTs not feasible?
In surgery, RCTs may not be feasible because of ethics, feasibility, or funding. The alternative observational study must be rigorous to ensure the study sample is the closest representation of a true “randomized” cohort, although it will never reach the gold standard of an RCT. A prospective cohort design generally ensures more accurate and complete data because it necessitates collecting contemporaneous data. Known confounders can potentially be addressed upfront, as subjects can be grouped according to the identification of prespecified data before any of the subjects have developed any of the outcomes of interest. Regardless of its rigor, however, a prospective study still cannot deal with unknown confounders in the way that an RCT can. When prospective studies are not possible, retrospective study must utilize one of the above techniques (matching or restriction of sample) to best resemble a randomized cohort. In the event of performing a retrospective study, potential confounders should be recognized prior to data collection. The identification of demographics and potential confounders should be clear and explicitly presented in a table to ensure that readers can assess significant differences between cohorts.
What is the role of confounding variables in neurosurgery?
A confounding variable, or confounder, affects the association between a potential predictor and an outcome.
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: It must be correlated with the independent variable. This may be a causal relationship, but it does not have to be.
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 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 ...
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.
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.
How to reduce confounding variables?
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.
Why do you need to account for confounding variables?
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.. For instance, you may find a cause-and-effect relationship that does not actually exist, because the effect you measure is caused ...
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.
What are the conditions necessary for confounding?
There are three conditions that must be present for confounding to occur: The confounding factor must be associated with both the risk factor of interest and the outcome. The confounding factor must be distributed unequally among the groups being compared. A confounder cannot be an intermediary step in ...
How to determine if a risk factor caused 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. 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. How to do this will be addressed in greater detail below.
What are some confounding factors that could influence an association?
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.
Why is socioeconomic status a confounder?
For example, socioeconomic status may be a confounder in this example because lower socioeconomic status is a marker for a complex set of poorly understood factors that seem to carry a higher risk of heart disease. As a result, there may be many possible confounding factors that could influence an association.
Is aspirin a confounding factor?
If those people who exercised regularly were more likely to take aspirin, and aspirin reduces the risk of heart disease, then aspirin use would be a confounding factor that would tend to exaggerate the benefit of exercise. A confounder can also be a surrogate or a marker for some other cause of disease.
Is increased HDL a confounder?
If increased HDL is a consequence of alcohol consumption and part of the mechanism by which it lowers the risk of heart disease, then it is not a confounder .. Not surprisingly, since most diseases have multiple contributing causes (risk factors), there are many possible confounders. A confounder can be another risk factor for the disease.
Is age a confounder?
For example, in the hypothetical cohort study testing the association between exercise and heart disease, age is a confounder because it is a risk factor for heart disease. Similarly a confounder can also be a preventive factor for the disease.
What is confounding in epidemiology?
Confounding reflects the fact that "epidemiologic studies are conducted among individuals with unevenly distributed characteristics.". Example: Risk of dementia among adults with diabetes: Confounding factors. Age: Subjects with diabetes were, on average, older than those without diabetes.
What is the definition of confounding?
Confounding: basic definition. A mixing of effects between the exposure, the outcome and a third extraneous variable known as a confounder. Confounding variable. A confounding variable is independently associated with both the risk factor (exposure) and the disease (outcome).
What are the disadvantages of a confounder?
Disadvantages: -Costly because extensive searching and recordkeeping are required to find matches. -When one matches subjects on a potential confounder that particular exposure variable can no longer be evaluated with respect to its contribution to risk.