Cross‐company collaboration to leverage analytics for clinical quality and accelerate drug development: The IMPALA industry group

Ménard, Timothé; Young, Kyle P. De; Siegel, Laura; Emerson, Jennifer; Studt, Robert; Sidor, Leslie

doi:10.1002/psp4.12677

Cited by 10 publications

(10 citation statements)

References 4 publications

(8 reference statements)

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“…Monitoring quality indicators and audit finding risk assessment could help to manage audit target selection and audit focus. To establish regulatory and industry trust, and to foster adoption of analytics-driven QA, we will continue to focus our effort on cross-company collaboration [ 9 ] and data sharing.…”

Section: Discussionmentioning

confidence: 99%

Statistical Modeling for Quality Risk Assessment of Clinical Trials: Follow-Up at the Era of Remote Auditing

Koneswarakantha

Ménard

2022

Ther Innov Regul Sci

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Background As investigator site audits have largely been conducted remotely during the COVID-19 pandemic, remote quality monitoring has gained some momentum. To further facilitate the conduct of remote quality assurance (QA) activities for clinical trials, we developed new quality indicators, building on a previously published statistical modeling methodology. Methods We modeled the risk of having an audit or inspection finding using historical audits and inspections data from 2011 to 2019. We used logistic regression to model finding risk for 4 clinical impact factor (CIF) categories: Safety Reporting, Data Integrity, Consent and Protecting Endpoints. Results We could identify 15 interpretable factors influencing audit finding risk of 4 out of 5 CIF categories. They can be used to realistically predict differences in risk between 25 and 43% for different sites which suffice to rank sites by audit and inspection finding risk. Conclusion Continuous surveillance of the identified risk factors and resulting risk estimates could be used to complement remote QA strategies for clinical trials and help to manage audit targets and audit focus also in post-pandemic times.

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Section: Discussionmentioning

confidence: 99%

Statistical Modeling for Quality Risk Assessment of Clinical Trials: Follow-Up at the Era of Remote Auditing

Koneswarakantha

Ménard

2022

Ther Innov Regul Sci

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“…Our strategy will continue to be improved and further tactics for quality checks can be performed by using additional variables/data, such as patient family history [24]. Finally, this project is part of a broader effort to deliver quality assurance effectively, by leveraging analytics and implementing quality by design to accelerate patient access to innovative healthcare solutions [25,26,27,28]. Application of a tailored data quality strategy to identify and address quality issues will aid researchers in reproducing results, demonstrating integrity of the data collected/generated, and ultimately giving stakeholders con dence in the research outcome.…”

Section: Discussionmentioning

confidence: 99%

Clinical Quality in Cancer Research: Strategy to Assess Data Integrity of Germline Variants Inferred from Tumor-Only Testing Sequencing Data

2021

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In the majority of cancers, pathogenic variants are only found at the level of the tumor; however, an unusual number of cancers and/or diagnoses at an early age in a single family may suggest a genetic predisposition. Predisposition plays a major role in about 5 to 10% of adult cancers and in certain childhood tumors. As access to genomic testing for cancer patients continues to expand, the identi cation of Potential Germline Pathogenic Variants (PGPV) through tumor-DNA sequencing is also increasing. Statistical methods have been developed to infer the presence of a PGPV without the need of a matched normal sample. These methods are mainly used for exploratory research, for example in realworld Clinico-Genomic Databases/platforms (CGDB). These databases are being developed to support many applications such as targeted drug development, clinical trial optimization and post marketing studies. To ensure the integrity of data used for research, a quality management system should be established, and quality oversight activities should be conducted to assess and mitigate clinical quality risks (for patient safety and data integrity). As opposed to well-de ned "good practice" quality guidelines (GxP) areas such as good clinical practice, there are no comprehensive instructions on how to assess the clinical quality of statistically derived variables from sequencing data such as PGPV. In this report, we aim to share our strategy and propose a possible set of tactics to assess the PGPV quality and to ensure data integrity in exploratory research.

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“…Monitoring quality indicators and audit finding risk assessment could help to manage audit target selection and audit focus. To establish regulatory and industry trust, and to foster adoption of analytics-driven QA , we will continue to focus our effort on the development of novel methods QA [10,11,12,13,14] and on cross-company collaboration and data sharing [15].…”

Section: Discussionmentioning

confidence: 99%

Statistical modeling for quality risk assessment of clinical trials: follow-up at the era of remote auditing

Koneswarakantha

Ménard

2021

Preprint

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Background - As investigator site audits have largely been conducted remotely during the COVID-19 pandemic, remote quality monitoring has gained some momentum. To further facilitate the conduct of remote Quality Assurance (QA) activities, we developed new quality indicators, building on a previously published statistical modelling methodology. Methods - We modeled the risk of having an audit or inspection finding using historical audits and inspections data from 2011 - 2019. We used logistic regression to model finding risk for 4 clinical impact factor (CIF) categories: Safety Reporting, Data Integrity, Consent and Protecting Endpoints. Results - Resulting Area Under the Receiver Operating Characteristic Curves were between 0.57 - 0.66 with calibrated predictive ranges of 27 - 41%. The combined and adjusted risk factors could be used to easily interpret risk estimates. Conclusion - Continuous surveillance of the identified risk factors and resulting risk estimates could be used to complement remote QA strategies and help to manage audit targets and audit focus also in post-pandemic times.

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Cross‐company collaboration to leverage analytics for clinical quality and accelerate drug development: The IMPALA industry group

Cited by 10 publications

References 4 publications

Statistical Modeling for Quality Risk Assessment of Clinical Trials: Follow-Up at the Era of Remote Auditing

Statistical Modeling for Quality Risk Assessment of Clinical Trials: Follow-Up at the Era of Remote Auditing

Clinical Quality in Cancer Research: Strategy to Assess Data Integrity of Germline Variants Inferred from Tumor-Only Testing Sequencing Data

Statistical modeling for quality risk assessment of clinical trials: follow-up at the era of remote auditing

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