A data-driven approach to quality risk management
Aim: An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in re...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2013-01-01
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| Series: | Perspectives in Clinical Research |
| Subjects: | |
| Online Access: | http://www.picronline.org/article.asp?issn=2229-3485;year=2013;volume=4;issue=4;spage=221;epage=226;aulast=Alemayehu |
| _version_ | 1818266343442481152 |
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| author | Demissie Alemayehu Jose Alvir Marcia Levenstein David Nickerson |
| author_facet | Demissie Alemayehu Jose Alvir Marcia Levenstein David Nickerson |
| author_sort | Demissie Alemayehu |
| collection | DOAJ |
| description | Aim: An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials. Materials and Methods: Several statistical methods were employed, including the Wilcoxon rank-sum test and logistic regression, to identify the presence of association between risk factors and the occurrence of quality issues, applied to data on quality of clinical trials sponsored by Pfizer. Results: Only a subset of the risk factors had a significant association with quality issues, and included: Whether study used Placebo, whether an agent was a biologic, unusual packaging label, complex dosing, and over 25 planned procedures. Conclusion: Proper implementation of the strategy can help to optimize resource utilization without compromising trial integrity and patient safety. |
| first_indexed | 2024-12-12T20:05:12Z |
| format | Article |
| id | doaj.art-ba6cbde823b04a359bef4fe6683df17a |
| institution | Directory Open Access Journal |
| issn | 2229-3485 |
| language | English |
| last_indexed | 2024-12-12T20:05:12Z |
| publishDate | 2013-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Perspectives in Clinical Research |
| spelling | doaj.art-ba6cbde823b04a359bef4fe6683df17a2022-12-22T00:13:39ZengWolters Kluwer Medknow PublicationsPerspectives in Clinical Research2229-34852013-01-014422122610.4103/2229-3485.120171A data-driven approach to quality risk managementDemissie AlemayehuJose AlvirMarcia LevensteinDavid NickersonAim: An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials. Materials and Methods: Several statistical methods were employed, including the Wilcoxon rank-sum test and logistic regression, to identify the presence of association between risk factors and the occurrence of quality issues, applied to data on quality of clinical trials sponsored by Pfizer. Results: Only a subset of the risk factors had a significant association with quality issues, and included: Whether study used Placebo, whether an agent was a biologic, unusual packaging label, complex dosing, and over 25 planned procedures. Conclusion: Proper implementation of the strategy can help to optimize resource utilization without compromising trial integrity and patient safety.http://www.picronline.org/article.asp?issn=2229-3485;year=2013;volume=4;issue=4;spage=221;epage=226;aulast=AlemayehuClinical trialcompliancequality risk managementrisk assessment and mitigation |
| spellingShingle | Demissie Alemayehu Jose Alvir Marcia Levenstein David Nickerson A data-driven approach to quality risk management Perspectives in Clinical Research Clinical trial compliance quality risk management risk assessment and mitigation |
| title | A data-driven approach to quality risk management |
| title_full | A data-driven approach to quality risk management |
| title_fullStr | A data-driven approach to quality risk management |
| title_full_unstemmed | A data-driven approach to quality risk management |
| title_short | A data-driven approach to quality risk management |
| title_sort | data driven approach to quality risk management |
| topic | Clinical trial compliance quality risk management risk assessment and mitigation |
| url | http://www.picronline.org/article.asp?issn=2229-3485;year=2013;volume=4;issue=4;spage=221;epage=226;aulast=Alemayehu |
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