Glossary

4.1 Types of bias: selection, information, and confounding

5 min read•august 14, 2024

Bias can seriously skew study results, leading to incorrect conclusions. , , and are three main types that can distort the true relationship between exposure and outcome in epidemiological research.

Understanding these biases is crucial for critically evaluating studies. Researchers must carefully design studies and analyze data to minimize bias and accurately interpret findings. Recognizing potential sources of bias helps assess the validity and reliability of epidemiological research.

Types of Bias in Epidemiology

Selection Bias

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  • Selection bias occurs when the study population does not accurately represent the target population, leading to a of the association between exposure and outcome
  • Selection bias can arise from non-random sampling, differential participation rates, or loss to follow-up, leading to an over- or of the true association between exposure and outcome
    • is a type of selection bias that occurs when the exposure and outcome are both associated with the likelihood of being included in the study population (hospital-based studies)
  • Selection bias example: In a study of the association between cell phone use and brain cancer, participants who agree to participate may be more health-conscious and have lower cell phone use than the general population, leading to an underestimation of the true association

Information Bias

  • Information bias arises from systematic differences in the accuracy or completeness of data collected from study participants, which can lead to misclassification of exposure or outcome status
    • is a type of information bias that occurs when participants' ability to accurately recall past exposures or events is influenced by their outcome status
    • is another type of information bias that occurs when the observer's knowledge of the exposure or outcome status influences the measurement or recording of data
  • Information bias can result from differences in the accuracy or completeness of exposure or outcome data, leading to a distortion of the observed association
    • Recall bias can lead to an of the association if cases are more likely to recall past exposures than controls
    • Observer bias can lead to an overestimation of the association if the observer's knowledge of the exposure influences the measurement of the outcome (or vice versa)
  • Information bias example: In a case-control study of the association between a certain medication and birth defects, mothers of babies with birth defects may be more likely to recall taking the medication during pregnancy than mothers of healthy babies, leading to an overestimation of the true association

Confounding

  • Confounding is a mixing of effects that occurs when a third variable is associated with both the exposure and the outcome, leading to a distortion of the true relationship between the exposure and the outcome
    • Confounding can be addressed through study design (, restriction, ) or statistical analysis (, multivariate regression)
  • Confounding can lead to an apparent association between the exposure and outcome, even when no causal relationship exists, or can mask a true association
    • occurs when the confounder is positively associated with both the exposure and the outcome, leading to an overestimation of the true association
    • occurs when the confounder is positively associated with the exposure but negatively associated with the outcome (or vice versa), leading to an underestimation of the true association
  • Confounding example: In a study of the association between coffee consumption and heart disease, smoking may be a confounder if it is associated with both coffee consumption and heart disease. Failing to account for smoking could lead to an overestimation of the true association between coffee and heart disease

Sources and Impact of Bias

Magnitude and Direction of Bias

  • The impact of bias on study results depends on the magnitude and direction of the bias, as well as the strength of the true association between the exposure and outcome
  • Selection bias and information bias can lead to either an overestimation or underestimation of the true association, depending on the specific nature of the bias and the study design
  • Confounding can lead to an apparent association when no causal relationship exists, or can mask a true association, depending on the direction of the associations between the confounder, exposure, and outcome

Assessing the Likelihood of Bias

  • The potential for bias should be carefully considered when interpreting study findings, and the likely direction and magnitude of any bias should be assessed
  • Sensitivity analyses can be conducted to evaluate the robustness of study findings to different assumptions about the presence and impact of bias
  • Triangulation of findings from studies with different designs and populations can help to assess the consistency of results and the likelihood of bias

Recognizing Bias in Studies

Selection Bias Examples

  • Non-random sampling: A study of the prevalence of hypertension in a city that only includes participants from a single, high-income neighborhood may underestimate the true prevalence in the general population
  • Differential participation rates: In a study of the association between physical activity and obesity, individuals who are more physically active may be more likely to participate, leading to an overestimation of the true association

Information Bias Examples

  • Recall bias: In a case-control study of the association between childhood infections and the development of asthma, parents of children with asthma may be more likely to recall past infections than parents of healthy children
  • Observer bias: In a study of the effectiveness of a new surgical technique, surgeons who are aware of which patients received the new technique may be more likely to rate their outcomes favorably

Confounding Examples

  • Age confounding: In a study of the association between alcohol consumption and heart disease, age may be a confounder if it is associated with both alcohol consumption and heart disease risk
  • Socioeconomic status confounding: In a study of the association between air pollution and respiratory disease, socioeconomic status may be a confounder if it is associated with both exposure to air pollution and risk of respiratory disease

Evaluating Bias in Study Findings

Assessing the Impact of Bias

  • Consider the likely direction and magnitude of any potential bias when interpreting study results
  • Evaluate the sensitivity of study findings to different assumptions about the presence and impact of bias through sensitivity analyses
  • Compare results from studies with different designs and populations to assess the consistency of findings and the likelihood of bias

Strategies for Minimizing Bias

  • Use appropriate sampling methods and recruit a representative study population to minimize selection bias
  • Use standardized data collection methods and blind participants and observers to exposure and outcome status when possible to minimize information bias
  • Measure and adjust for potential confounders through study design (randomization, restriction, matching) or statistical analysis (stratification, multivariate regression)
  • Transparently report study methods and limitations to allow readers to assess the potential for bias in study findings

Key Terms to Review (28)

Alfred Sommer: Alfred Sommer is a prominent epidemiologist known for his groundbreaking research on vitamin A deficiency and its impact on public health, particularly in developing countries. His work has highlighted the importance of addressing nutritional deficiencies to prevent blindness and reduce child mortality, connecting his findings to various biases such as selection bias, information bias, and confounding in epidemiological studies.
Berkson's Bias: Berkson's Bias refers to a type of selection bias that occurs when individuals are selected for a study based on their presence in a hospital or clinic, potentially leading to an inaccurate representation of the general population. This bias often emerges in case-control studies where patients with certain conditions are overrepresented, skewing the results and making it difficult to generalize findings to a broader context. The bias highlights how the method of selecting participants can influence the observed associations between exposure and outcome.
Blinding: Blinding refers to a research technique used to prevent participants, researchers, or both from knowing which treatment or intervention participants are receiving. This method is crucial in reducing bias, ensuring that the results are not influenced by expectations or preconceived notions about the treatment effects. By implementing blinding, researchers aim to maintain the integrity of the study and enhance the reliability of the findings.
Causal inference: Causal inference is the process of determining whether a relationship between two variables is causal, meaning that one variable directly influences the other. This concept is crucial for understanding the underlying mechanisms of disease and the impact of exposures on health outcomes, helping researchers differentiate between correlation and causation.
Confidence Interval: A confidence interval is a statistical range that estimates the true value of a population parameter, calculated from sample data, and is associated with a specific level of confidence, usually expressed as a percentage. It provides a way to quantify the uncertainty of an estimate by indicating how much the estimate might vary if the study were repeated multiple times. This concept plays a crucial role in assessing the precision of estimates in various epidemiological contexts.
Confounding: Confounding occurs when the relationship between an exposure and an outcome is distorted by the presence of another variable that is related to both. This can lead to incorrect conclusions about the true nature of the relationship being studied, making it crucial to identify and control for confounders in research.
Distortion: Distortion refers to the alteration or misrepresentation of data or results in research, often leading to incorrect conclusions. This can happen due to various biases, such as selection bias, information bias, and confounding, affecting the reliability of study findings and the inferences drawn from them.
External validity: External validity refers to the extent to which the results of a study can be generalized to and have relevance for settings, people, times, and measures beyond the study itself. This concept is crucial as it helps determine whether the findings of a research study can be applied to broader populations and real-world scenarios, influencing how researchers interpret the significance of their results.
Information Bias: Information bias refers to systematic errors in collecting or interpreting data, leading to incorrect conclusions in research studies. This type of bias can arise from the way information is gathered, whether through questionnaires, interviews, or medical records, and it can significantly impact the validity of findings in observational studies, including cohort, case-control, and cross-sectional designs.
Internal Validity: Internal validity refers to the degree to which a study accurately establishes a causal relationship between the treatment or exposure and the outcome within the context of the study. It is crucial for determining whether observed effects are genuinely due to the intervention rather than other confounding factors. High internal validity means that the results are reliable and can be trusted, which is essential when assessing strengths and limitations of different study designs, understanding types of bias, and implementing strategies to minimize those biases.
Matching: Matching is a technique used in research to pair participants in a study based on specific characteristics to minimize bias and improve the validity of the results. By ensuring that groups are comparable in critical aspects such as age, sex, or socioeconomic status, matching helps control for confounding variables, leading to more accurate conclusions. This method is particularly relevant in observational studies where random assignment is not feasible.
Measurement Error: Measurement error refers to the difference between the true value of a variable and the value obtained through measurement. This error can arise from various sources, including inaccuracies in data collection instruments, participant reporting biases, or environmental factors affecting measurements. Understanding measurement error is crucial because it can lead to misleading conclusions in research, impacting bias types such as selection and information, as well as confounding variables that can obscure true relationships in data.
Negative confounding: Negative confounding occurs when the true association between an exposure and an outcome is underestimated due to the presence of a confounder that has an opposite effect on the relationship. This type of bias can distort the results of epidemiological studies, leading to incorrect conclusions about the strength or direction of an association. Understanding negative confounding is essential in identifying and adjusting for potential biases in research findings.
Observer Bias: Observer bias refers to the systematic distortion of study results caused by the expectations or beliefs of those conducting the research. This type of bias can influence how data is collected, interpreted, and reported, potentially leading to inaccurate conclusions. Recognizing observer bias is crucial in evaluating the strengths and limitations of various research designs, as it can affect the validity of findings and contribute to other types of bias.
Omitted variable bias: Omitted variable bias occurs when a model fails to include one or more relevant variables, leading to inaccurate estimates of the relationships between the included variables. This bias can distort findings and lead to misleading conclusions, especially when the omitted variables are correlated with both the dependent and independent variables. In epidemiological studies, this can significantly impact the validity of the results, making it crucial to identify and control for all relevant factors.
Overestimation: Overestimation refers to the act of inaccurately assessing a value, risk, or outcome as being greater than it actually is. This can lead to misinterpretations in research findings and has critical implications in the evaluation of data, particularly in the context of various biases that may skew results.
P-value: A p-value is a statistical measure that helps to determine the significance of results obtained in hypothesis testing. It represents the probability of observing the data, or something more extreme, given that the null hypothesis is true. A smaller p-value indicates stronger evidence against the null hypothesis, and is often used to infer whether the results are statistically significant.
Positive confounding: Positive confounding occurs when the presence of a confounder influences both the exposure and outcome in a way that exaggerates the true association between them. This type of bias can lead researchers to mistakenly conclude that there is a stronger relationship between the exposure and the outcome than actually exists, impacting the validity of study findings. Understanding positive confounding is crucial for accurately interpreting results and ensuring the reliability of epidemiological studies.
Randomization: Randomization is a process used in experimental studies to assign participants to different groups or interventions in a way that is entirely based on chance. This technique helps ensure that the groups being compared are similar in all respects except for the treatment being tested, thereby reducing the likelihood of bias. By using randomization, researchers can enhance the validity of their findings and better infer causal relationships between exposures and outcomes.
Recall Bias: Recall bias occurs when participants in a study have inaccurate memories of past events or experiences, leading to systematic differences in the information they provide. This bias can affect the validity of findings and is particularly relevant in studies relying on self-reported data, as it can skew results by overestimating or underestimating associations between exposure and outcome.
Risk Ratio: The risk ratio is a measure used in epidemiology to compare the risk of a certain event occurring (like disease development) between two groups. It provides insights into the strength of the association between exposure and outcome, making it crucial for understanding health risks and guiding public health interventions.
Selection Bias: Selection bias occurs when individuals included in a study are not representative of the larger population due to the method of selecting participants. This can lead to skewed results and conclusions, impacting the validity of both experimental and observational research designs.
Sensitivity Analysis: Sensitivity analysis is a method used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions. It helps assess the robustness of conclusions drawn from data, especially when potential biases such as selection bias, information bias, and confounding are present. By varying parameters systematically, sensitivity analysis reveals how sensitive the results are to changes in assumptions, which is crucial in understanding the reliability of study findings.
Sir Richard Doll: Sir Richard Doll was a prominent British epidemiologist known for his groundbreaking research linking smoking to lung cancer and other diseases. His work transformed public health perspectives, highlighting the importance of lifestyle factors in chronic diseases and the necessity of understanding biases, confounding factors, and risk assessments in epidemiologic studies.
Statistical Adjustment: Statistical adjustment is a method used in data analysis to control for confounding variables and reduce bias, ensuring that the relationship between exposure and outcome is more accurately assessed. This technique is crucial in observational studies, where variables can be intertwined, leading to misleading conclusions. By adjusting for these factors, researchers can isolate the effects of interest and provide a clearer picture of the underlying relationships.
Stratification: Stratification refers to the process of dividing a population into subgroups based on specific characteristics, such as age, gender, or socioeconomic status, to facilitate analysis and comparison. This technique helps in understanding variations in health outcomes and risk factors across different segments of the population, enabling researchers to control for confounding variables and assess the true effects of exposures.
Systematic Error: Systematic error refers to consistent, repeatable inaccuracies that occur in measurements or observations, leading to biased results in research studies. This type of error can skew data in a particular direction, making the findings unreliable and potentially invalid. It's crucial to identify and mitigate systematic errors to ensure the integrity of study outcomes, especially when evaluating causal relationships and associations.
Underestimation: Underestimation refers to the act of assessing a situation or outcome as being less significant, impactful, or severe than it truly is. This concept is crucial in understanding how biases can distort our perception of data, leading to flawed conclusions and potentially harmful implications in research and public health.
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