Related Experiment Video
Updated: Jun 14, 2026
06:37
Using Reference Reagents to Confirm Robustness of Cytokine Release Assays for the Prediction of Monoclonal Antibody Safety
Published on: September 15, 2023
Negative controls: a tool for detecting confounding and bias in observational studies
Marc Lipsitch1, Eric Tchetgen Tchetgen, Ted Cohen
1Department of Epidemiology, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA 02115, USA. mlipsitc@hsph.harvard.edu
Epidemiology (Cambridge, Mass.)
|March 26, 2010
Summary
Negative controls are crucial for identifying noncausal associations in observational studies. Employing these controls can help resolve confounding and other errors, improving the validity of causal inference.
Area of Science:
- Epidemiology
- Biostatistics
- Observational Studies
Background:
- Noncausal associations threaten causal inference in observational studies.
- Experimental studies typically control for noncausal associations through standardization and randomization.
- Negative controls are a common practice in biologic laboratory experiments to detect spurious causal inference.
Purpose of the Study:
- To highlight the utility of negative controls in observational studies for detecting noncausal associations.
- To distinguish between exposure and outcome negative controls.
- To identify conditions under which negative controls effectively detect confounding and other errors.
Main Methods:
- Review of epidemiologic literature for examples of negative controls.
- Distinction between two types of negative controls: exposure controls and outcome controls.
- Analysis of conditions for effective use of negative controls in detecting confounding.
Main Results:
- Negative controls, analogous to those in laboratory experiments, can identify and resolve confounding in epidemiology.
- Two types of negative controls (exposure and outcome) are identified and exemplified.
- Conditions for using negative controls to detect confounding are specified.
Conclusions:
- Negative controls should be more widely adopted in observational studies.
- Further research is needed to optimize the use of negative controls for detecting various sources of error.
- Increased use of negative controls can enhance the validity of causal inference in observational research.
Related Concept Videos
01:13Controls in Experiments
When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:27Confounding in Epidemiological Studies
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...
01:25Strategies for Assessing and Addressing Confounding
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
01:11Blinding
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
01:29Bias in Epidemiological Studies
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is: