Quality assurance and quality control in toxicology: how might these concepts apply to epidemiology?

Quality assurance and its distinction from quality control are defined in operational terms. The historical development of standards for scientific research supporting commercialization of materials leading to the current Good Laboratory Practice (GLP) standards is briefly outlined. The application of GLPs to toxicology studies and experience in the use of these standards over a broad range of activities is also presented. This background information provides a common starting point for consideration of the development and application of similar standards for epidemiological studies.

Quality assurance for epidemiologic studies.

Quality assurance (QA) has been successfully applied to scientific disciplines such as toxicology. Experience gained in the application of QA and quality control (QC) through a formal program of compliance with Good Laboratory Practice (GLPs) regulations has been beneficial in toxicology and can be used to develop and implement a similar program in epidemiology. We focus here on the specific ways in which GLP-mandated quality assurance can be adapted and used in epidemiology and on the consequent benefits of this effort.

Issues in data sharing and access: an industry perspective.

Epidemiologic data sharing and access, and information sharing and access, are complex issues with no consensus within the industrial community. The purpose of this paper is to provide an introduction, as well as some personal perspectives, to the issues of data access and sharing. These perspectives include a discussion of types of data sharing, advantages and barriers to openness, and alternatives that lower the need for sharing of the raw data.

Data audit as a management tool.

Management faces the three basic challenges of achieving cost efficiency, successfully applying new technology, and minimizing risk. This paper examines the ways in which data audits can help managers come to terms with each of these challenges. It emphasizes the role of data audits in providing reliable information on which to base decisions and stresses the importance of auditing data in process rather than retrospectively. The paper examines frequently encountered sources of error and discusses the ways audits benefit management by assuring the integrity of experimental design, by preventing misassignment of observations, and by preventing the loss of data.