Date of Graduation

12-2018

Document Type

Dissertation

Degree Name

Doctor of Education in Human Resource and Workforce Development (EdD)

Degree Level

Graduate

Department

Rehabilitation, Human Resources and Communication Disorders

Advisor

Carsten Schmidtke

Committee Member

Vicki Dieffenderfer

Second Committee Member

Kevin Roessger

Abstract

The training and competency development of individuals who manage data from a clinical trial is in an international concern. Clinical trials are designed to test the safeness and efficacy of drugs, biologics, and devices including the frequency of adverse drug reactions that pose a potential threat to human subjects. Given that clinical data managers hold the responsible for managing data on a human subject’s adverse reactions to a drug in a clinical trial, there is a major need to ensure that clinical data managers are effectively trained in the evidence-based data management practices of the profession. The Society for Clinical Data Management’s Certified Clinical Data Manager ExamTM has clearly articulated the evidence-based data management practices through its competencies. The use of evidence-based data management practices may reduce the number of errors in clinical trial data and help ensure that a harmful drug is not approved for use in patients; potentially reducing the significant amount of deaths that occur annually from an individual adverse reaction to a drug. This study uses a quantitative descriptive research design to examine the frequency of correct responses to questions in the competency domains of the Certified Clinical Data Manager ExamTM to identify exam preparation needs. In alignment with classical test theory, descriptive statistics, point-biserial correlation values, and p-values were calculated to discriminate between questions that could potentially be written poorly and questions that require clinical data managers to prepare better. The analysis revealed clinical data managers needed additional exam preparation in coordinating data discrepancy identification and resolution, entering data, implementing data standards, specifying edit checks, designing data collection forms, and programming data extracts.

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