Efficiency of the drug development process is a continuing concern for pharmaceutical companies, governments, regulatory authorities, and patients. While much time and effort have been spent on developments in genetics and on sophisticated statistical designs, there has been less concern about the processes that govern the running of clinical trials. In this article, I describe a statistical method for source data verification whose implementation can have a large impact on the workload of trial monitors. I investigate the consequences of a less stringent form of source data verification on the quality of data and the inferences that can be drawn from the data.
SheinerLB. Learning versus confirming in clinical drug development. Clin Pharmacol Ther. 1997;61:275-291.
4.
BeltangadyMBrownM. Enhanced clinical trial design: how it has transformed our late stage drug development. Paper presented at the PhRMA Adaptive Design Working Group KOL Lecture Series; December 12, 2008; Available at: http://www.biopharmnet.com/doc/doc12004-05.html. Accessed June 14, 2011.
5.
BarkerADSigmanCCKelloffGJHyltonNMBerryDAEssermanLJ. I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin Pharmacol Ther. 2009;86:97-100.
6.
FunningSGrahnénAErikssonKKettis-LinbladÅ. Quality assurance within the scope of Good Clinical Practice (GCP): what is the cost of GCP-related activities? A survey with the Swedish Association of the Pharmaceutical Industry (LIF)'s members. Qual Assur J. 2009;12:3-7.
7.
Society for Clinical Data Management. Good clinical data management practices. Washington DC; 2005.
8.
Clinical Trials Transformation Initiative. Effective and efficient monitoring as a component of quality in the conduct of clinical trials: final report of Workstream 2. Available at: https://www.trialstransformation.org/WS2%20Final%20Report.pdf. Accessed June 14, 2011.
9.
KoriethK. The high cost and questionable impact of 100% SDV. CenterWatch Mon. 2011;18:14-17.
TantsyuraVGrimesIMitchelJ. Risk-based source data verification approaches: pros and cons. Drug Inf J. 2010;44:745-756.
12.
British Standards Institute. Sampling procedures for inspection by attributes, part 1: sampling schemes indexed by acceptance quality limit (AQL) for lot-by-lot inspection, BS6001. London, UK: 1999.
13.
International Standards Organisation. Sampling procedures for inspection by attributes, part 1: sampling schemes indexed by acceptance quality limit (AQL) for lot-by-lot inspection, ISO2859. Geneva, Switzerland: 1999.
14.
BrossI. Misclassification in 2 x 2 tables. Biometrics. 1954;10:478-486.
15.
GoldbergJD. The effects of misclassification on the bias in the difference between two proportions and the relative odds in the fourfold table. J Am Stat Assoc. 1975;70:561-567.
16.
International Conference of Harmonisation. E6: guideline for Good Clinical Practice. Available at: http://www.ema.europa.eu/pdfs/human/ich/013595en.pdf. Geneva, Switzerland: International Standards Organisation. Accessed June 14, 2011.
17.
GrahnenAEKarlssonKBragazziF. Impact of transcription errors on the outcome of a clinical trial. Clin Pharmacol Ther. 2007;81:P1-29.
