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Data abstraction is a key step in conducting systematic reviews because data collected from study reports form the basis of appropriate conclusions. Recent methodological standards and expectations highlight several principles for data collection. To support implementation of these standards, this article provides a step-by-step tutorial for selecting data collection tools; constructing data collection forms; and abstracting, managing, and archiving data for systematic reviews. Examples are drawn from recent experience using the Systematic Review Data Repository for data collection and management. If it is done well, data collection for systematic reviews only needs to be done by 1 team and placed into a publicly accessible database for future use. Technological innovations, such as the Systematic Review Data Repository, will contribute to finding trustworthy answers for many health and health care questions.

The authors developed and tested a framework for identifying evidence gaps and prioritizing comparative effectiveness research by using a combination of clinical practice guidelines and systematic reviews. In phase 1 of the project, reported elsewhere, 45 clinical questions on the management of primary open-angle glaucoma were derived from practice guidelines and prioritized by using a 2-round Delphi survey of clinicians. On the basis of the clinicians' responses, 9 questions were classified as high-priority. In phase 2, reported here, systematic reviews that addressed the 45 clinical questions were identified. The reviews were classified as at low, high, or unclear risk of bias, and evidence gaps (in which no systematic review was at low risk of bias) were identified. The following comparative effectiveness research agenda is proposed: Two of the 9 high-priority questions require new primary research (such as a randomized, controlled trial) and 4 require a new systematic review. The utility and limitations of the framework and future adaptations are discussed.