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.

Hostile attributional bias and aggressive behavior in global context.

We tested a model that children's tendency to attribute hostile intent to others in response to provocation is a key psychological process that statistically accounts for individual differences in reactive aggressive behavior and that this mechanism contributes to global group differences in children's chronic aggressive behavior problems. Participants were 1,299 children (mean age at year 1 = 8.3 y; 51% girls) from 12 diverse ecological-context groups in nine countries worldwide, followed across 4 y. In year 3, each child was presented with each of 10 hypothetical vignettes depicting an ambiguous provocation toward the child and was asked to attribute the likely intent of the provocateur (coded as benign or hostile) and to predict his or her own behavioral response (coded as nonaggression or reactive aggression). Mothers and children independently rated the child's chronic aggressive behavior problems in years 2, 3, and 4. In every ecological group, in those situations in which a child attributed hostile intent to a peer, that child was more likely to report that he or she would respond with reactive aggression than in situations when that same child attributed benign intent. Across children, hostile attributional bias scores predicted higher mother- and child-rated chronic aggressive behavior problems, even controlling for prior aggression. Ecological group differences in the tendency for children to attribute hostile intent statistically accounted for a significant portion of group differences in chronic aggressive behavior problems. The findings suggest a psychological mechanism for group differences in aggressive behavior and point to potential interventions to reduce aggressive behavior.

Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues.

For optimal solutions in health care, decision makers inevitably must evaluate trade-offs, which call for multi-attribute valuation methods. Researchers have proposed using best-worst scaling (BWS) methods which seek to extract information from respondents by asking them to identify the best and worst items in each choice set. While a companion paper describes the different types of BWS, application and their advantages and downsides, this contribution expounds their relationships with microeconomic theory, which also have implications for statistical inference. This article devotes to the microeconomic foundations of preference measurement, also addressing issues such as scale invariance and scale heterogeneity. Furthermore the paper discusses the basics of preference measurement using rating, ranking and stated choice data in the light of the findings of the preceding section. Moreover the paper gives an introduction to the use of stated choice data and juxtaposes BWS with the microeconomic foundations.