Factors to Consider When Evaluating Lines of Evidence (Text Version)

Data Quality and Adequacy

When evaluating lines of evidence, determine whether:

• Enough data were generated to satisfy the analyses chosen.
• The analyses were sensitive and robust enough to identify stressor-caused perturbations.

Specific concerns to consider include whether:

• Experiments were designed to answer the questions posed in studies.
• Data quality objectives were clear and adhered to.

Degree and Type of Uncertainty

Extrapolations are a major source of uncertainty. The greater the number of extrapolations, the more uncertainty they introduce into the study. When evaluating lines of evidence, consider whether effects were extrapolated from:

• One species to another.
• One temporal or spatial scale to another.
• The laboratory to the field.
• Chemical structure-activity relationships to the field.

Also consider whether no-effect or low-effect levels were used to address the likelihood of effects.

Relationship to Risk Assessment Questions

How directly lines of evidence relate to the questions asked in the risk assessment may determine their relative importance in terms of the assessment endpoints. Lines of evidence directly related to the risk questions, and those that establish a cause-and-effect relationship based on a definitive mechanism (rather than associations alone), are likely to be most important.