The evolving role of data & safety monitoring boards for real-world clinical trials

Bryan J Bunning^{1

2}, Haley Hedlin¹, Jonathan H Chen³, Jody D Ciolino⁴, Johannes Opsahl Ferstad⁵, Emily Fox^{6

7}, Ariadna Garcia¹, Alan Go⁸, Ramesh Johari⁵, Justin Lee¹, David M Maahs⁹, Kenneth W Mahaffey¹⁰, Krista Opsahl-Ong⁹, Marco Perez¹¹, Kaylin Rochford⁵, David Scheinker¹², Heidi Spratt¹³, Mintu P Turakhia¹⁰, Manisha Desai¹

Affiliations

¹ Quantitative Sciences Unit, Stanford University, Stanford, CA, USA.
² Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
³ Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
⁴ Department of Preventative Medicine - Biostatistics, Northwestern University, Chicago, IL, USA.
⁵ Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.
⁶ Department of Statistics, Stanford University, Stanford, CA, USA.
⁷ Kaiser Permanente Northern California Division of Research, Kaiser Permanente, Oakland, CA, USA.
⁸ Department of Computer Science, Stanford University, Stanford, CA, USA.
⁹ Department of Pediatrics, Stanford Medicine Children's Hospital, Stanford, CA, USA.
¹⁰ Stanford Center for Clinical Research, Stanford University, Stanford, CA, USA.
¹¹ Department of Medicine, Cardiovascular Medicine, Stanford Medicine, Stanford, CA, USA.
¹² Systems Design and Collaborative Research, Stanford Medicine Children's Hospital, Stanford, CA, USA.
¹³ Department of Preventative Medicine & Community Health, University of Texas Medical Branch, Galveston, TX, USA.

PMID: 37745930
PMCID: PMC10514684
DOI: 10.1017/cts.2023.582

The evolving role of data & safety monitoring boards for real-world clinical trials

Bryan J Bunning et al. J Clin Transl Sci. 2023.

. 2023 Aug 2;7(1):e179.

doi: 10.1017/cts.2023.582. eCollection 2023.

Authors

Affiliations

¹ Quantitative Sciences Unit, Stanford University, Stanford, CA, USA.
² Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
³ Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
⁴ Department of Preventative Medicine - Biostatistics, Northwestern University, Chicago, IL, USA.
⁵ Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.
⁶ Department of Statistics, Stanford University, Stanford, CA, USA.
⁷ Kaiser Permanente Northern California Division of Research, Kaiser Permanente, Oakland, CA, USA.
⁸ Department of Computer Science, Stanford University, Stanford, CA, USA.
⁹ Department of Pediatrics, Stanford Medicine Children's Hospital, Stanford, CA, USA.
¹⁰ Stanford Center for Clinical Research, Stanford University, Stanford, CA, USA.
¹¹ Department of Medicine, Cardiovascular Medicine, Stanford Medicine, Stanford, CA, USA.
¹² Systems Design and Collaborative Research, Stanford Medicine Children's Hospital, Stanford, CA, USA.
¹³ Department of Preventative Medicine & Community Health, University of Texas Medical Branch, Galveston, TX, USA.

PMID: 37745930
PMCID: PMC10514684
DOI: 10.1017/cts.2023.582

Abstract

Introduction: Clinical trials provide the "gold standard" evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources - data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor.

Methods: Three examples of real-world trials that leverage different types of data sources - wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived.

Results: Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity.

Conclusions: Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.

Keywords: Clinical trials; DSMB; RWD; data and safety monitoring board; data integrity; data quality; decentralized; digital; pragmatic; real world data; real-world data; real-world evidence.

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Conflict of interest statement

BJB, HH, JDC, JOF, AG, RJ, JL, KOO, MP, KR, HS, MT, & MD have no disclosures. JHC has received research grant support from NIH/National Institute on Drug Abuse Clinical Trials Network (UG1DA015815 - CTN-0136), Stanford Artificial Intelligence in Medicine and Imaging - Human-Centered Artificial Intelligence (AIMI-HAI) Partnership Grant, Doris Duke Charitable Foundation - Covid-19 Fund to Retain Clinical Scientists (20211260), Google, Inc. Research collaboration Co-I to leverage EHR data to predict a range of clinical outcomes, and American Heart Association - Strategically Focused Research Network - Diversity in Clinical Trials. JHC discloses: Co-founder of Reaction Explorer LLC that develops and licenses organic chemistry education software & Paid consulting fees from Sutton Pierce and Younker Hyde MacFarlane PLLC as a medical expert witness. EF is supported in part by AFOSR Grant FA9550-21-1-0397, ONR Grant N00014-22-1-2110, and the Stanford Institute for Human-Centered Artificial Intelligence (HAI). EBF is a Chan Zuckerberg Biohub – San Francisco Investigator. AGo has received research funding from the National Heart, Lung, and Blood Institute; National Institute of Diabetes, Digestive, and Kidney Diseases; and the National Institute on Aging. He has also received research grants through his institution from Novartis, Bristol Myers Squibb, Pfizer, Janssen Research & Development, CSL Behring, iRhythm Technologies, and Amarin Pharmaceuticals. RJ was partially supported by the National Science Foundation under grant 2205084, and by the Stanford Maternal and Child Health Research Institute under the Transdisciplinary Initiatives Program. DMM has had research support from the NIH, JDRF, NSF, and the Helmsley Charitable Trust and his institution has had research support from Medtronic, Dexcom, Insulet, Bigfoot Biomedical, Tandem, and Roche. Dr Maahs has consulted for Abbott, Aditxt, the Helmsley Charitable Trust, Lifescan, Mannkind, Sanofi, Novo Nordisk, Eli Lilly, Medtronic, Insulet, Dompe, Biospex, Provention Bio, and Bayer. DS discloses advising Carta Healthcare. MPT has received grants or personal fees from American Heart Association, Apple, Bayer, Bristol Myers Squibb, FDA, Gilead Sciences, Johnson & Johnson, Medtronic Inc., Myokardia, Pfizer, and Sanofi, is a shareholder of AliveCor, Connect America, Evidently, Forward, iRhythm, and PocketRN, and is an employee of iRhythm Technologies, Inc. KWM’s financial disclosures can be viewed at http://med.stanford.edu/profiles/kenneth-mahaffey.

Figures

**Figure 1.**
Data flow chart of the the statin therapy and global outcomes in older persons pragmatic clinical trial (STAGE PCT).

**Figure 2.**
Violin plots highlight the data distribution of the overall Teamwork, Targets, Technology, and Tight Control (4T) Study population across different metrics used as proxies for overall patient adherence, including CGM wear time, the open rate of messages sent from the patient’s clinical team, message response rate, message response time, and finally the average change in time in range one week following a message. This last metric is proposed as an indicator of whether a given patient is adhering to behaviors recommended by the care team. This illustrative visualization also allows for users to split data by factors including the patient’s race, gender, insurance type, age, and preferred langauge. The lower visualization shows population level adherence metrics with a split across insurance type selected. Note that the distributions presented are illustrative and are not necessarily representative of the 4T study.

**Figure 3.**
Proposed participant adherence monitoring diagram based on the protocol from the Apple Heart Study example. Each box represents a count of the number of participants who completed the given step in the protocol.

**Figure 4.**
Example visualization picturing average weekly time in range for a given patient over time, split by time of day. Messages sent by clinical team are also overlayed, and color coded by whether or not they were read by the patient. The purpose of this visualization is to provide a quick overview for whether or not a patient is responding to recommendations provided by the care team.

See this image and copyright information in PMC

References

1. Simon GE, Platt R, Watanabe JH, et al. When can we rely on real world evidence to evaluate new medical treatments? Clin Pharmacol Ther. 2022;111(1):30–34. - PMC - PubMed
1. Loudon K, Treweek S, Sullivan F, Donnan P, Thorpe KE, Zwarenstein M. The PRECIS-2 tool: designing trials that are fit for purpose. Bmj. 2015;350:h2147. - PubMed
1. Simon GE, Shortreed SM, Rossom RC, Penfold RB, Sperl-Hillen JAM. Principles and procedures for data and safety monitoring in pragmatic clinical trials. Trials. 2019;20(1):1–8. - PMC - PubMed
1. Food and Drug Administration. Guidance for clinical trial sponsors: Establishment and operation of clinical trial data monitoring committees (https://www.fda.gov/downloads/regulatoryinformation/guidances/ucm127073.pdf). Accessed August 15, 2023.
1. Evans SR. Independent oversight of clinical trials through data and safety monitoring boards. NEJM Evid. 2022;1(1):EVIDctw2100005. - PubMed

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The evolving role of data & safety monitoring boards for real-world clinical trials

Affiliations

The evolving role of data & safety monitoring boards for real-world clinical trials

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Grants and funding

LinkOut - more resources

Full Text Sources