Related Experiment Video
Updated: May 17, 2026
06:46
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
False positives in cancer epidemiology
Joseph K McLaughlin1, Robert E Tarone
1International Epidemiology Institute, 1455 Research Boulevard, Suite 550, Rockville, MD 20850, USA. jkm@iei.us
Summary
Cancer epidemiology studies
Area of Science:
- Epidemiology
- Carcinogenesis
- Toxicology
Background:
- Critique of a recent false-positive rate estimation for cancer epidemiology studies.
- Focus on agents classified as IARC category 3 (not classifiable).
Purpose of the Study:
- To evaluate biases in a specific false-positive rate estimation method.
- To address the limitations of using IARC classification criteria.
Main Methods:
- Analysis of the International Agency for Research on Cancer (IARC) classification criteria.
- Critique of estimation methods relying on these criteria.
Main Results:
- The IARC criteria give epidemiologic studies a privileged position, influencing study outcomes.
- Using IARC category 3 agents (lowest carcinogenic potential) leads to spuriously low false-positive rates.
Conclusions:
- The estimation approach is methodologically flawed, producing misleading results.
- The reported false-positive rates are biased and lack substantive contribution to the field.
Related Concept Videos
02:59Cancer Prevention
Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...
Some...
02:59Cancer Prevention
Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...
Some...
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:
01:13Sensitivity, Specificity, and Predicted Value
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
01:26Introduction to Epidemiology
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
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...