Vol XX, December `05

Speaking at an elective session during the Fall Research Network Conference, Gravenstein suggested that inclusion/exclusion criteria need to be rigorous enough to ensure viable, representative, and accurate data but open enough to bring in the number of participants necessary to test the hypothesis.

Sample size is important, Gravenstein noted, and it can help determine the value of your hypothesis. "If you need thousands of people to show a difference, you need to consider that the difference is not important or significant enough to test," he observed.

In establishing inclusion criteria, Gravenstein noted, the goal is to be very specific. "Define the main characteristics of the target population. Consider what factors are important to the research question," he suggested, adding, "You want subjects that are easy to study and readily available. You don't want to use participants that are likely to be lost to follow-up."

Exclusion, Gravenstein explained, is influenced by many factors, such as "what kind of data you have access to. For example, the ability to collect and record data is different in facilities that use electronic medical records." Exclusion criteria also should consider subjects' ability to consent and participate. "You might want to exclude residents with dementia or language barriers," said Gravenstein. Those individuals for who it is unethical to withhold treatment also might be appropriately excluded.

While it is important to have exclusions that protect participants and ensure accurate, useable data, Gravenstein cautioned that "as you add exclusion criteria, you get fewer eligible participants." Additionally, more criteria mean more screening work to identify qualified participants.

Watch future issues of this newsletter for summaries of other Research Network Conference elective programs.