A Practical and Efficient Approach to Database Quality Audit in Clinical Trials
- PDF / 929,610 Bytes
- 9 Pages / 612 x 792 pts (letter) Page_size
- 79 Downloads / 142 Views
STATISTICS
385
A Practical and Efficient Approach to Database Quality Audit in Clinical Trials
Larry Z.Shen, PhD, Jay Zhou, MS
Biometrics, Amylin Pharmaceuticals, Inc., San Diego, California
Key Words
Clinical database; Quality audit; Confidence interval; Acceptance criterion; Quality control; Sample size
Correspondence Address
Larry Z. Shen, PhD, Amylin Pharmaceuticals, Inc., 9360 Towne Centre Drive, San Diego, CA 92121 (e-mail: [email protected]).
-
In clinical trials, data quality is one of the key factors in determining thesuccess or failure of a drug development program. Data audit is an important step in assessing and ensuring the quality of clinical databases. However, despite theavailability of many sophisticated statistical methodologies in analyzing clinical data, there are few data audit methods that are both efficient andstatistically sound. A frequent method is auditing data for a fixed percentage of the
INTRODUCTION The International Conference on Harmonisation E6 Guidelines for Good Clinical Practice (1) indicates that quality control should be applied to each stage of data handling to ensure that all data are reliable and have been processed correctly. To satisfy this requirement, it is a common practice to conduct a quality audit of a database before it is accepted for "lock" for analyses and reporting. It is a key step in quantifying and ensuring the integrity and quality of data transcribed from trial subjects to a database in clinical trials, even though federal regulations and guidelines do not address the minimally acceptable data quality standards. Auditing an entire database, especially in a large clinical trial, could be daunting as it involves great effort and resources. It is also unnecessary from the perspective of statistical quality control. The audit practice in the pharmaceutical industry has been ad hoc. There have been few systematic and defined ways to define the sample size, the sampling unit, error rate, data listing generation, and follow-up action plan. The most often used approach is to sample a fixed percentage (eg, 10%) of the subjects in the database and to audit all of the data for these subjects. This may either lead to over sampling and wasted resources for a large phase
subjects ina clinical study. Such a method either wastes too many resources or lacks statistical rigor indetermining thequality status ofa database. In this article, we give a short review of some recent developmentsand provide an example of database audit strategy. The methodology was developed for, and applied to, a real clinical study. Such a method is both statisticallysound and efficient in saving resources neededfor theconduct of theaudit.
III trial or result in insufficient sample size for a small phase I study. Recognizing this possible deficiency, a few articles provided guidance on how a database audit should be conducted. Sullivan et al. (2) introduced a statistical method in the audit of a clinical database. They also clarified several definitions and concepts related to qua
Data Loading...
