Table 1.

Major sources of error in outcome assessment in radiation epidemiological studies

		Potential effect on dose response		Strategies to reduce error in risk
Source of error	Description	Nondifferential	Differential	assessment of outcomes
Loss to follow-up	Usually due to relocation and loss of contact, discontinued participation, or other reasons Date of loss to follow-up can be known or unknown	Should not result in bias if same data sources, methods, and level of effort used to trace and/or link in both exposure groups but may result in loss of statistical power	Bias may occur if loss substantial and associated with both exposure and outcome (eg, used different data sources for tracing or linkage, different methods or levels of effort in exposed compared with unexposed)	Use standardized methods and all available sources to target 100% follow-up. If active tracing of all patients not feasible (best approach), investigators should aim for linkage with high-quality vital status registries and data sources. Investigators should characterize level of completeness of vital status databases. High-quality nationwide mortality registries may be better for outcome assessment when nationwide high-quality cancer registries unavailable.
Under- or over ascertainment of outcomes	Underascertainment: cancer outcomes not identified or misclassified as another type of cancer or a noncancer disease Overascertainment: may occur if benign entity or lesion that rarely becomes malignant is designated as cancer. Clinical assessment and screening in absence of symptoms may result in overascertainment. Broad categories (eg, “all cancers”) may be less susceptible to over- or underascertainment	Should not result in bias if same data sources, methods, and classification system used	Bias may occur with use of different (by exposure status) sources of outcome data or methods for identifying cancer outcomes (eg, self-administered interviews, population-based cancer registry linkage, clinical assessment, cancer screening) or classification systems	Use of rigorous standardized methods, same data sources, and most recent classification systems (including reclassification where applicable or possible) should result in complete and accurate identification of all cancers. Cohort linkage with high-quality population-based cancer registries may still result in underascertainment for certain outcomes generally identified and treated in physicians’ practices or diagnosed solely by imaging. Use of multiple sources of data will likely yield high ascertainment. Screen-detected cancer outcomes should be excluded if identified in exposed groups only.
Misclassification, changes in classification over time, or indeterminate classification	Misclassification: nonmalignant condition classified as malignancy or vice versa; specific type of cancer classified as another type; or within neoplasm misclassification of a neoplasm that includes benign and malignant variants Changes in cancer classification over time: increased specificity of histologic or molecular subtypes; new ICD codes; reclassification from benign to malignant or vice versa; or one category to another Neoplasms of indeterminate classification	Could lead to under- or overestimation if substantial differences in outcome(s) misclassified between exposed vs unexposed populations	May result in biased cancer risk estimates. If efforts are made to reclassify cancer outcomes according to new criteria, bias could occur with usage of different sources of data, different approaches, or different levels of effort in exposed vs unexposed populations.	Undertake expert pathology review Comprehensive reclassification may not be feasible if tumor tissue, pathology, or medical records are not available; a modification of most recent classification may be the only possibility. Conduct sensitivity analyses using older classification schemes to compare findings of current study with those from earlier studies.