Quantifying data quality for clinical trials using electronic data capture.

BACKGROUND: Historically, only partial assessments of data quality have been performed in clinical trials, for which the most common method of measuring database error rates has been to compare the case report form (CRF) to database entries and count discrepancies. Importantly, errors arising from medical record abstraction and transcription are rarely evaluated as part of such quality assessments. Electronic Data Capture (EDC) technology has had a further impact, as paper CRFs typically leveraged for quality measurement are not used in EDC processes. METHODS AND PRINCIPAL FINDINGS: The National Institute on Drug Abuse Treatment Clinical Trials Network has developed, implemented, and evaluated methodology for holistically assessing data quality on EDC trials. We characterize the average source-to-database error rate (14.3 errors per 10,000 fields) for the first year of use of the new evaluation method. This error rate was significantly lower than the average of published error rates for source-to-database audits, and was similar to CRF-to-database error rates reported in the published literature. We attribute this largely to an absence of medical record abstraction on the trials we examined, and to an outpatient setting characterized by less acute patient conditions. CONCLUSIONS: Historically, medical record abstraction is the most significant source of error by an order of magnitude, and should be measured and managed during the course of clinical trials. Source-to-database error rates are highly dependent on the amount of structured data collection in the clinical setting and on the complexity of the medical record, dependencies that should be considered when developing data quality benchmarks.

Design and Emergence in the Making of American Grand Strategy

The main research question of this thesis is how do grand strategies form. Grand strategy is defined as a state's coherent and consistent pattern of behavior over a long period of time in search of an overarching goal. The political science literature usually explains the formation of grand strategies by using a planning (or design) model. In this dissertation, I use primary sources, interviews with former government officials, and historical scholarship to show that the formation of grand strategy is better understood using a model of emergent learning imported from the business world. My two case studies examine the formation of American grand strategy during the Cold War and the post-Cold War eras. The dissertation concludes that in both these strategic eras the dominating grand strategies were formed primarily by emergent learning rather than flowing from advanced designs.