Utility of a molecular prescreening program in advanced colorectal cancer for enrollment on biomarker-selected clinical trials.

BACKGROUND: Incorporation of multiple enrichment biomarkers into prospective clinical trials is an active area of investigation, but the factors that determine clinical trial enrollment following a molecular prescreening program have not been assessed. PATIENTS AND METHODS: Patients with 5-fluorouracil-refractory metastatic colorectal cancer at the MD Anderson Cancer Center were offered screening in the Assessment of Targeted Therapies Against Colorectal Cancer (ATTACC) program to identify eligibility for companion phase I or II clinical trials with a therapy targeted to an aberration detected in the patient, based on testing by immunohistochemistry, targeted gene sequencing panels, and CpG island methylation phenotype assays. RESULTS: Between August 2010 and December 2013, 484 patients were enrolled, 458 (95%) had a biomarker result, and 157 (32%) were enrolled on a clinical trial (92 on biomarker-selected and 65 on nonbiomarker selected). Of the 458 patients with a biomarker result, enrollment on biomarker-selected clinical trials was ninefold higher for predefined ATTACC-companion clinical trials as opposed to nonpredefined biomarker-selected clinical trials, 17.9% versus 2%, P < 0.001. Factors that correlated positively with trial enrollment in multivariate analysis were higher performance status, older age, lack of standard of care therapy, established patient at MD Anderson, and the presence of an eligible biomarker for an ATTACC-companion study. Early molecular screening did result in a higher rate of patients with remaining standard of care therapy enrolling on ATTACC-companion clinical trials, 45.1%, in contrast to nonpredefined clinical trials, 22.7%; odds ratio 3.1, P = 0.002. CONCLUSIONS: Though early molecular prescreening for predefined clinical trials resulted in an increase rate of trial enrollment of nonrefractory patients, the majority of patients enrolled on clinical trials were refractory to standard of care therapy. Within molecular prescreening programs, tailoring screening for preidentified and open clinical trials, temporally linking screening to treatment and optimizing both patient and physician engagement are efforts likely to improve enrollment on biomarker-selected clinical trials. CLINICAL TRIALS NUMBER: The study NCT number is NCT01196130.

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.