Science Strategy
Epidemiologic studies aim to conduct research in a scientific manner, but they can be susceptible to errors and biases. Errors, in this context, refer to inaccuracies in the way something is being measured or observed, which can lead to concerns about a study's validity. Meanwhile, bias is a systematic error that results in an inaccurate measure of association between exposure and disease or other outcomes. The scientific community is particularly concerned about bias because it can distort the truth by overestimating or underestimating a measure of effect.
There are three key types of bias: selection bias, information bias, and confounding. Selection bias occurs from systematic errors in the way study participants are chosen. Information bias is a systematic error in gathering data or measuring a study variable, which can lead to misclassification bias. Misclassification can either be non-differential or differential, with the latter including recall bias, interviewer bias, and surveillance bias. Identifying and addressing these types of error and bias is crucial to ensuring accurate and reliable study results.
Lesson Outline
<ul> <li>Epidemiologic studies and the presence of error and bias</li> <ul> <li>Errors: inaccuracies in measurement or observation</li> <li>Bias: systematic error that leads to inaccurate measure of association</li> <ul> <li>Three types of bias: selection bias, information bias, and confounding</li> </ul> </ul> <li>Selection bias: systematic error in study participant selection</li> <ul> <li>Examples: losing participants to follow-up in cohort studies</li> </ul> <li>Information bias: systematic error in gathering information or measuring variables</li> <ul> <li>Misclassification bias: misclassifying disease or exposure</li> <ul> <li>Non-differential misclassification: same error for all study participants</li> <li>Differential misclassification: error varies based on disease status</li> <ul> <li>Recall bias: diseased participants more likely to remember past exposures</li> <li>Interviewer bias: interviewers more thorough with cases vs. controls</li> <li>Surveillance bias: one study group more likely to undergo testing</li> </ul> </ul> </ul> <li>Confounding: unintentional third variable in play</li> </ul>
Don't stop here!
Get access to 19 more MCAT Science Strategy lessons & 8 more full MCAT courses with one subscription!
FAQs
Random errors are errors that occur by chance and are unpredictable. They can be seen in data variability and result from factors such as sampling errors or measurement variability. In contrast, systematic errors are consistent errors that result from a flaw in the study's design, data collection, or analysis. Systematic errors can lead to biased results where the observed association between exposure and disease deviates systematically from the true association. Common types of systematic errors include selection bias, information bias, and confounding.
Selection bias occurs when there is a systematic difference in the inclusion of participants or the retention of study subjects, leading to a flawed sample that doesn't accurately represent the target population. This can ultimately distort the observed association between exposure and outcome. Information bias, on the other hand, arises from inaccurate or incomplete measurement of exposure or outcome variables among the study participants. This can compromise the validity of the study results by misrepresenting the true associations. Both selection and information biases can cause study conclusions to be inaccurate and misleading.
Both non-differential and differential misclassifications are types of information bias. Non-differential misclassification occurs when the errors in measuring exposure or outcome variables are unrelated to the study variables or the outcome. In this case, the measurement errors are made equally across all exposure or outcome categories. Non-differential misclassification tends to bias the measure of association towards the null, attenuating the observed effect. Differential misclassification occurs when the errors in measuring exposure or outcome variables are related to the study variables or the outcome. The pattern of error differs for different exposure or outcome categories, potentially leading to an overestimation or underestimation of the true association.
Recall bias, interviewer bias, and surveillance bias are all examples of information biases. Recall bias occurs when study participants, particularly in case-control studies, have differential accuracy or completeness in recollecting past exposures or events based on their disease status. This can lead to inaccurate reporting of exposure histories and biased results. Interviewer bias occurs when interviewers inadvertently influence the responses of study participants based on their preconceptions or expectations, leading to systematic errors in data collection. Surveillance bias, also known as detection bias, occurs when individuals in certain exposure or outcome groups are more likely to be monitored or tested for a disease, leading to differences in disease detection unrelated to the true association between exposure and disease.
