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DATABASICS
A PUBLICATION SUPPORTED BY AND FOR THE MEMBERS OFTHE SOCIETY FOR CLINICAL DATA MANAGEMENT, INC
TO ADVANCE EXCELLENCE
IN THE MANAGEMENT
OF CLINICAL DATA
Volume 24 | Issue 1 / 2018 Spring
This Issue2Letter From the Chair
3Co-editors’ Letter
4Systematic Review Process for GCDMP by Meredith Zozus
6Book Review - Remember Who You Are by Sanet Olivier
8Moving away from 100% Onsite SDV by Nimita Limaye
13Leveraging Digital Technology in Clinical Trials by Ashish Bagde
2 DATA BASICS 2018 Spring
LetterFrom the Chair
Shannon Labout
Dear SCDM Community,
As we finish the first quarter of 2018, I’d like to take this opportunity to thank you for your support of SCDM and your dedication to the discipline of data science, and to invite you to become more deeply involved in the activities of the Society this year.
These are exciting times in our industry, with technological advances coming at us from all directions, new sources and higher volumes of data, and a growing checklist of regulatory requirements to manage eSource, mobile health data and much more.
This year at SCDM we are continuing to move forward with significant changes in the way we operate as a community and in the way we deliver support to our community. These
changes include:
• Evolving the development of GCDMP from a community-based document to an evidence-based document
• Ensuring tight alignment across GCDMP, educational opportunities and the CCDM Certification exam
• Making the CCDM exam more globally accessible with a new online platform
• Working with regional Steering Committees around the world to offer local opportunities for networking,
education and events in Europe, India, China, and Japan
We don’t know what the future holds for data science, but as we evolve and grow as a profession and as a Society, remember that we are stronger when we all
contribute and work together. That is why I am asking every one of you to find an activity with SCDM that will allow you to contribute your knowledge and expertise to the
community. I also hope that you will encourage a colleague who has perhaps never been involved in SCDM, or someone who used to be involved and hasn’t been lately, to join you in
getting involved.
Please join me and let’s continue to work together to make SCDM the best it can be.
Shannon
Shannon Labout Chair Interim Chief Standards Officer & Vice President of Education CDISC
Linda King Vice Chair
Jaime Baldner Past Chair Manager, Clinical Data Management Genentech
Jonathan R. Andrus Treasurer COO & CDO Clinical Ink
Jennifer Price Secretary Director, Clinical Solutions BioClinica
Carrie Zhang Trustee CEO eClinwise, Panacea Technology Co.
Michael Goedde Trustee Vice President Clinical Database and Statistical Programming PAREXEL International
Arshad Mohammed Trustee Senior Director, CDM IQVIA
Peter Stokman Trustee Global Clinical Data Sciences Lead Bayer
Reza Rostami Trustee Assistant Director, Quality Assurance and Regulatory Compliance Duke Clinical Research Institute
Sanjay Bhardwaj Trustee Global Head, Data & Analytics Management Biogen
Deepak Kallubundi Trustee Clinical Functional Service Provider and Analytics – Associate Director Chiltern
2018 SCDM Board of Trustees
Letter from the Editors
3 DATA BASICS 2018 Spring
Dear Colleagues,
With this issue of Data Basics we welcome Spring in North America - and with it the freshness and new ideas that Spring can bring.
Our first article by Meredith Zozus takes us up to speed on the fresh approach currently being used to update SCDM’s Good Clinical Data Management Practice (GCDMP). The systematic approach promises to keep content current and provide support to data management professionals in their work for a long time to come.
Both Nimita Limaye’s article, ‘Moving away from 100% SDV’ and Ashish Bagde’s article ‘Leveraging Digital Technology in Clinical Trials’ support the use of new strategies and emerging technologies to freshen the CDM approach to both SDV and data collection, despite the challenges individuals and organizations wrestle with when implementing new methods.
And finally, we are adding a fresh feature to Data Basics – periodically we find a book we think is worth sharing with our global CDM community. This time, our Publication’s Committee Co-chair Sanet Olivier reviews Paula Brown Stafford and Lisa T Grime’s new book, “Remember Who You Are”.
We hope you find this issue refreshing and that it provides a spark to fuel continued forward momentum into the future CDM landscape!
Janet Welsh & Sanet Olivier
Editorial BoardStacie T. Grinnon, MS, [email protected]
Lynda L. Hunter, CCDMPRA Health [email protected]
Elizabeth [email protected]
Nadia [email protected]
Arshad MohammedPublications Committee Board LiaisonIQVIA [email protected]
Claudine Moore, CCDMAdvance Research Associates, [email protected]
Michelle Nusser-Meany, CCDMNovartis [email protected]
Sanet Olivier, CCDMCo-Editor & Publication Committee [email protected]
Derek Petersen, [email protected]
Margarita Strand, CCDMGilead Sciences, [email protected]
Vadim Tantsyura, DrPHTarget Health, [email protected]
Janet Welsh, CCDMCo-EditorBoehringer-Ingelheimjanet.welsh@boehringer-ingelheim.com
4 DATA BASICS 2018 Spring
In 1998, to provide a basis for clinical data management practice the Society for Clinical Data Management (SCDM) convened the Good Clinical Data Management Practices committee and
published the first version of the GCDMP in the year 2000. 1 Until that date there had been little guidance on data collection and management methodology. The first synthesis of evidence relevant to clinical research data management, the Greenberg Report, was published in 1967 and concentrated on organization, review, and administration of cooperative studies with little detail of clinical research
data management methods.2 In 1990, Dr. Morris Collen presented a review of data collection and processing methods
for existing clinical research databases.3 This paper was followed in 1995 by a special issue of Controlled Clinical
Trials (now Clinical Trials) containing a collection of five review papers documenting current practice of the day.4-9 The
over forty contributors to the issue were largely from academic-based clinical trials10, 11 and many of the methods did not extend to clinical studies intended for marketing authorization and falling under the regulatory authority of the United States Food and Drug Administration (FDA). Thus, the SCDM GCDMP filled a major gap in guidance for clinical data managers. Later, in 2006 the most significant survey of clinical data management practice was reported as part of the Inventory and Evaluation of Clinical Research Networks (IECRN) under the National Institutes of Health (NIH) Reengineering the Clinical Research Enterprise program. Though the results are limited to clinical research networks, many of which are academic in nature, they do focus on organizational infrastructure to manage data for programs of multiple studies and are informative of practice. The IECRN report however, is no longer available. Thus, the GCDMP remains the most significant source of practice guidance for Clinical Data Managers. As such, it has been translated into multiple languages, was awarded the 2007 Clinical Research Excellence Award for “Most Successful Company or Programme of the Year in Raising GCP Standards”, and has been downloaded over one hundred thousand times.
GCDMP REVISION
As practitioners of a science-based discipline, Data Managers should be able to explain the advantages and disadvantages of different approaches to CDM. To do this, Data Managers should have access to that science applied in the context of clinical research data collection and management. In 2016 SCDM initiated a major revision of the GCDMP to do just that - transition the practice standard to an evidence-based document. In recognition of shared principles and methods across all of clinical research, an additional goal of the revision is to broaden the scope of the standard to cover data collection and management practices across industry, academia and the full spectrum of the National Institutes of Health (NIH) definition of clinical research.
PRIORITIZATION OF CHAPTERS FOR REVISION
To prioritize chapters for revision, each of the twenty-eight GCDMP chapters was reviewed by members of the Content Alignment Task Force and the GCDMP Executive Committee. Chapters with greater extent of outdated material, higher number of unsupported assertions and chapters covering content also on the CCDMTM (Certified Clinical Data Manager) exam were prioritized. This is the first attempt to broadly review and synthesize the literature supporting the practice of data collection and management for clinical studies. Given the importance of data collection and management to the ability of data to support research conclusions, such a review is long overdue. The ten chapters currently under revision based on this prioritization are listed in Table 1.
METHODS To start the revision SCDM sought CDM expertise relevant to one or more of the ten prioritized chapters. These volunteers were publically solicited from individuals with contact with SCDM, through the annual meeting of the society and through social media. Credentials were reviewed to document appropriate expertise and over sixty volunteers from around the world were invited to participate.
Systematic Review Process for GCDMPBy Meredith N. Zozus, PhD 1
1 University of Arkansas for Medical Sciences, Little Rock, AR, USA
Table 1: GCDMP Chapters in Revision
CRF Completion Guidelines
Data Management Plan
Edit Check Design Principles
Electronic Data Capture—Concepts and Study Start-up
Electronic Data Capture—Study Conduct
Electronic Data Capture—Study Closeout
External Data Transfers
Measuring Data Quality
Safety Data Management and Reporting
Vendor Selection and Management
5 DATA BASICS 2018 Spring
Systematic Review Process for GCDMP
Continued
This revision is different from earlier GCDMP revisions in that a systematic literature review has been completed for each of the ten prioritized chapters. The literature reviews had the following goals :
1. Identification of published research results and reports of evaluation of new methods2. Summarizing evidence capable of informing the practice of clinical research data management.
Search queries were customized for the databases searched (PubMed, CINAHL, EMBASE, Science Citation Index/Web of Science, PsychINFO, ACM Guide to the Computing Literature, IEEE) and executed. Search results were consolidated to obtain a list of distinct articles. The searches were not restricted to any time range. Two reviewers used inclusion criteria to screen all abstracts. Disagreements were adjudicated by the writing groups. Selected articles were reviewed by two individuals each of which read the article for mention of explicit practice recommendations or research results informing practice. This work provides a synthesis of the literature relevant to each chapter and is supporting the GCDMP’s transition to an evidence-based guideline.
CURRENT STATUS OF THE REVISIONS
In 2017, across all chapters, over 8,600 articles were screened. Four hundred and twelve calls were held with over sixty volunteers. In addition, over seventy draft items for the certification exam were written. All chapters in revision have completed extensive literature reviews. Seven of the ten chapters had outlines approved by the GCDMP Executive Committee – that means that the chapter working groups are busily working on updates now. The groups expect to have drafts of the revised chapters out for public comment in 2018.
REFERENCES
1 Society for Clinical Data Management, Good Clinical Data Management Practices (GCDMP), October 2013. Available from http://www.scdm.org
2 Organization, Review, and Administration of Cooperative Studies (Greenberg Report): A Report from the Heart Special Project Committee to the National Advisory Heart Council, May 1967. Controlled Clinical Trials 9:137-148, 1988.
3 Collen MF, Clinical Research Databases – A Historical Review. Journal of Medical Systems, 14:06, 1990.
4 McBride R and Singer SW, Introduction [to the 1995 Clinical Data Management Special Issue of Controlled Clinical Trials]. Controlled Clinical Trials 16:1S-3s, 1995.
5 McBride R and Singer SW, Interim Reports, Participant Closeout, and Study Archives. Controlled Clinical Trials 16:1S-3s (1995).
6 Gassman JJ, Owen WW, Kuntz TE, Martin JP, Amoroso WP, Data Quality Assurance, Monitoring, and Reporting. Controlled Clinical Trials 16:1S-3s (1995).
7 Hosking JD, Newhouse MM, Bagniewska A, Hawkins BS, Data Collection and Transcription. Controlled Clinical Trials 16:1S-3s (1995).
8 McFadden ET, LoPresti F, Bailey LR, Clarke E, Wilkins PC, Approaches to Data Management. Controlled Clinical Trials 16:1S-3s (1995).
9 Blumenstein BA, James KE, Lind BK, Mitchell HE, Functions and Organization of Coordinating Centers for Multicenter Studies. Controlled Clinical Trials 16:1S-3s (1995).
10 1995 Clinical Data Management Special Issue of Controlled Clinical Trials, Appendix C: Reviewers for This Issue. Controlled Clinical Trials 16:1S-3s (1995).
11 1995 Clinical Data Management Special Issue of Controlled Clinical Trials, Appendix B: Studies Cited and Sources of Information. Controlled Clinical Trials 16:1S-3s (1995).
ABOUT THE AUTHOR:
Meredith N. Zozus, Ph.D., CCDM
Associate Professor and Vice Chair for Academic Programs, Department of Biomedical Informatics in the UAMS College of Medicine.
Dr. Zozus joined UAMS in 2016 after an 18 year career at Duke University where she served as the Director of Clinical Data Management at the Duke Clinical Research Institute, the Associate Director for Clinical Research Informatics in the Duke Translational Medicine Institute. Her research focuses on data quality in health care and health-related research including collection and management of data for clinical studies and assessment and use of EHR data. In addition to published research findings, her work includes editorship of the GCDMP, six international data standards, and The Data Book: Collection and Management of Research Data.
6 DATA BASICS 2018 Spring
Immediately, when I saw the book title I went down memory lane. Thinking about a movie very close to my African heart: ‘Lion King’. The scene when Mufasa talks to Simba about life, looking up at the stars and promise Simba that he will be always be next to him. Mufasa dies and
Rafiki takes Simba to the water and he hears his father’s voice…. ‘remember… remember who you are…’
Paula Brown Stafford and Lisa T. Grimes are two award-winning, c-suite executives with immense work experience. They are both married with four successful children and successfully manage their careers. Looking at this, I sometimes wonder how do these superwomen achieve all of this? I certainly feel myself trying and it is so easy to get absorbed in the moment and in our environment. Does not matter if you are a clinical data manager or in management or even in the c-suites of your company.
The book is written from a woman’s perspective for (wo)men. The writing style is transparent and reliable. Concepts are clearly defined and can easily be followed by any person with a basic understanding of English (this is not my first language). I had no need to have a dictionary next to me to understand the authors intent and the message they wanted to convey. The thread is also lined through with letters from successful female executive women who experienced a similar situation and sharing that moment: what she would like to tell her younger self.
Throughout the book, Paula and Lisa share their own experiences (from childhood, aspiring career woman, climbing the ladder in the big, large pharmaceutical world). It was easy to relate in many instances to what happened in my own life. The advice, lessons learned and tips are clear and useful to women and men (yes, you can read the entire book without the ‘wo’ in woman).
In summary: if you want to read a great book in 2018 – this is it! Personally, I would like to give each of my team members a copy of this book as a gift on their birthdays as this might help them to achieve success, create balance and hopefully experience fulfilment in their careers.
ABOUT THE AUTHORS
Paula Brown Stafford is a clinical researcher, business leader, and lecturer. She is a distinguished alumna at the University of North Carolina at Chapel Hill, where she is also an Adjunct Professor. Previously, she was president of clinical development at QuintilesIMS, a Fortune 500 company. She lives in Chapel Hill, NC with her husband and has two adult children.
Lisa T. Grimes is a business leader, coach and speaker. She has spent most of the last 30 years in healthcare and lifestyle start-ups where she has served as CEO of PurThread Technologies, InSite Clinical Trials and AcSentient. She loves to connect people and often does so through her work with non-profit organizations. She lives in Cary, NC with her husband and has two adult sons.
Together, Paula and Lisa founded Habergeon to help women achieve success, create balance, and experience fulfillment.
They are dedicated to giving back and are donating a portion of all proceeds from book sales to some of their favorite charities.
Book Review : Remember who you areBy Sanet Olivier
SCDM COMMITTEES
- Board Of Trustees
- Executive Committee
- Finance Committee
- China Steering Committee
- EMEA Steering Committee
- India Steering Committee
- Content Alignment Taskforce
- Innovation Committee
- Academic Relations Taskforce
- eSource Implementation
Consortium
- Certification Committee
- Online Course Committee
- Webinar Committee
- Annual Conference Taskforce
- GCDMP Committee
- Publications Committee
- Marketing Advisory Board
- Membership Committee
7 DATA BASICS 2018 Spring
The book was just released on 6th March 2018 and available in Kindle and paperback formats.
You can order your copy through one of the following online retailers:
• Amazon
• Barnes & Noble
• Powells City of Books
• Indie Bound Org - A Community of Independant Local Bookstores
• !ndigo
PRODUCT DETAILS
ISBN: 9781683506478
Binding: Trade Paperback
Publication date: 06 March 2018
Publisher: Morgan James Publishing
Language: English
Pages: 162
Author: Lisa T. Grimes
Author: Paula Brown Stafford
Book Review : Remember who you are
Continued
ABOUT THE AUTHOR:
Sanet Olivier (CCDM) is a Director at IQVIA. She has 21 years’ experience in Clinical Research with 18 years in Data Management.
She has performed duties as Lead Data Manager (LDM) in large multi-national studies (Phase I, II and III) across Europe, South Africa, Australia, Asia and USA (5 years), as Group Manager, Associate Director (CDM), eData Technical Specialist (Global EDC) and as FSP Manager.
At present she oversees a specialist team with services in quality review, project delivery, customer satisfaction, innovation and other support for Data Management in a global delivery network.
HAVE YOU THOUGHT ABOUT JOINING AN SCDM COMMITTEE?
Contact us at [email protected] for more details
8 DATA BASICS 2018 Spring
Data has shown that on-site Source Data Verification (SDV) has added significantly to the costs of clinical trials, while the improvement in data quality has been less than proportionate 1.
In an effort to minimize the cost implications of on-site SDV, the pharmaceutical industry has explored options such as Remote SDV and directly extracting data from Electronic Medical Records (EMRs). And while SDV has long since been considered to be a “surrogate marker” for quality, this is a perspective that is rapidly changing. Regulators are increasingly recommending a strategic focus on high risk areas, rather than a tactical approach of 100% on-site
SDV 2. This article will briefly explore some of the strategies the pharmaceutical industry is exploring to meet these challenges.
THE COST OF POOR QUALITY (CPQ)
It is well known that the Cost of Poor Quality (CPQ) is the cost of not getting it right the first time, which really includes three costs: the cost of creating the defective product / service (this could result from an internal or an
external failure), the cost of detecting the issue, and the cost of rectifying the issue 3. In this paradigm, performing on-site SDV can be thought of as the step of detecting the issues and errors in the clinical trial data. It accounts for about 70% of the monitor’s time at the site, with monitoring accounting for 30% of the trial cost ($7.5 billion spent
annually) 2. Forty percent of the costs for Phase II and III clinical trials are attributed to SDV and these costs would only
escalate as trials grow increasingly global, multi-centric and complex 5. Results have shown that only 1.1% of the data
corrections in an electronic Case Report Form (eCRF) 6 and 2.4% of the queries related to critical data are attributed
to 100% on-site SDV 4.
SOURCE DATA VERIFICATION AND ITS VARIETIES
So what is on-site SDV anyway? It’s checking that the patient records are accurately transcribed to the Electronic Data Capture (EDC) database. And what is the cost of on-site SDV? Often there is a limited budget for site visits, with a fixed number of visits and fixed hours for each visit. So the total cost of on-site SDV includes not only the travel costs and the travel time of the monitor, but also the lack of time for the monitor to focus on more critical issues during the site visits.
Remote SDV (the validation of clinical data points by accessing secure off-site electronic health records remotely) could significantly save both time and money and, in fact, allow more real-time review of transcription accuracy. A study was carried out on five sites (four adult, one paediatric; using two NIH-sponsored clinical trial networks) with varying technology infrastructures and EMRs. Despite the varying technology set ups and diverse remote chart review policies, close to 100% of the data could be reviewed remotely across sites. Levels of satisfaction were high
with investigators and monitors 5.
The success of a remote SDV model depends on supportive institutional policies, regulatory requirements and technological infrastructure. Often, despite sponsors providing source document templates to sites, sites still feel
the burden of redacting, printing, and faxing paper work to enable a remote SDV model 7.
One variety of remote SDV is to provide monitors with remote access to Electronic Medical Records (EMRs) for performing remote SDV. However, it is important that monitors are able to access data only for their studies and view data that has been appropriately redacted or de-identified. Not all EMRs have the capability of selectively limiting access. While some sites may accept a signed agreement indicating that the monitors will access only data for their
studies, other sites depend upon audit trail data to monitor unauthorized access 7.
Furthermore, sites are covered entities under the Health Insurance Portability and Accountability Act 1996 (HIPAA). So even though the EDC system may be able to provide restricted remote access, they may be unwilling to allow
Moving away from 100% On-site SDVDr. Nimita Limaye, CEO, Nymro Clinical Consulting Services
9 DATA BASICS 2018 Spring
Moving away from 100% SDV
Continued
un-redacted Protected Health Information (PHI) to go off-site, even though study subjects may have signed a HIPAA
authorization permitting it. This remains a major barrier today 7.
ALTERNATIVE TO SDV
An alternative to on-site SDV is accessing electronic health records (EHRs) directly. EHRs include a patient’s longitudinal health records across multiple systems so interoperability becomes critical. If we could pull data from EHR systems directly into the EDC system, not only would we completely eliminate SDV, but this would also support
the real time assessment of risk 8.
It also supports the industry shift towards a real world evidence-based approach. The FDA, through its May 2016 draft guidance titled “Use of Electronic Health Record Data in Clinical Investigations,” endorses the use of EHR
data for clinical research and encourages the industry to use EHRs and EDC systems that are interoperable 10. However, significant challenges still exist in terms of driving seamless interoperability. Diverse, non-validated and out-dated EHR systems and the lack of a scalable standards-based integration model for EHRs, has long since been
a roadblock for EDC-EHR integration 10.
SOURCE DATA REVIEW AND TARGETED SDV
At the same time, there has been a shift in focus from SDV to source data review (SDR). The latter focuses on key issues related to protocol compliance, the logical reporting of data and the interpretation and significance of clinical data points, rather than on whether they were accurately transcribed on the eCRF or not.
Does that mean no SDV is required at all? No, as long as a source exists, some degree of SDV would be important to ensure the integrity of data and hence ensure compliance. There are EDC systems that allow one to perform targeted
SDV, focusing on critical data points 11, 12. Organizations have different SDV strategies, escalating or reducing the percentage of SDV to be performed or changing the data points to be SDV’d based on the frequency of errors observed and the criticality of the data points.
It is important to note that ICH E6 guidelines have not defined upper or lower limits for the percentage of data that
needs to be SDV’d 9. Instead both the FDA and EMA have increasingly encouraged a shift to a risk based approach
(wherein visits are triggered based on thresholds being crossed for pre-defined critical data points) 9. While this is distinctly different from remote SDV, in both cases, the time spent at the site is significantly reduced. Risk based monitoring (RBM), which is the most logical path to follow, has been endorsed by regulators such as the FDA, MHRA
and EMA 13, 14, 15, 16, and by key industry consortiums such as TransCelerate BioPharma 17. The ICH E6 R2 guidance has stressed the following: the importance of technology as an enabler of RBM; the need to focus on systemic errors so as to optimize the return on investment (ROI); and the need to focus on a quality by design (QBD) approach, addressing
quality at the grass root level 18.
CONCLUSION
Recommendations made based on published statistical evidence suggest that the value of SDV decreases as the study size increases. Thus, less than 8% SDV has been recommended on an average to optimize data quality, with
an increase in the same for smaller studies and potentially zero SDV for large studies 18. Change is not easy. Even though the shift from 100 % SDV is being strongly endorsed, the industry is taking time to accept the change. In an attempt to ensure quality, while controlling costs, the industry is exploring multiple alternatives. It is employing a mix of some or all of the following strategies: remote SDV, eliminating SDV altogether by using eSource, partial SDV, and RBM. Each approach has its own advantages and disadvantages and there is no perfect solution at present.
10 DATA BASICS 2018 Spring
Moving away from 100% SDV
Continued
REFERENCES:
1 Miseta, E. “Source Data Verification: A quality control measure in clinical trials.” Clinical Leader (2015). Accessed March 14, 2018.
https://www.clinicalleader.com/doc/source-data-verification-a-quality-control-measure-in-clinical-trials-0001.
2 Torche, F. “The Practical Implementation of Risk Based Monitoring”. Applied Clinical Trials 22, no. 8. (2013). Accessed March 14, 2018.
http://www.appliedclinicaltrialsonline.com/practical-implementation-risk-based-monitoring.
3 Olivier, D and Seyedzadeh, J. “Using the cost of poor quality to drive process improvement.” (2006). Accessed March 14, 2018.
http://www.ehcca.com/presentations/devicecongress1/olivier_a.pdf
4 “Medidata RBM: The Fastest Way to Realize the Quality, Cost and Timeline Benefits of Risk-based Monitoring.” Medidata Fact Sheet (2015). Accessed March 14, 2018.
https://www.mdsol.com/sites/default/files/RBM_Risk-Based-Monitoring_20150717_Medidata_Fact-Sheet_0.pdf
5 Mealer, Meredith, J. Kittelson, B.T. Thompson, A.P. Wheeler, J.C. Magee, R.J. Sokol, M. Moss and M.G. Kahn.. “Remote Source Document Verification in Two National Clinical Trials Networks: A Pilot Study”. PLoS One 8, no. 12. (2013) Accessed March 14, 2018.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3857788/
6 Brennan Z. “Analysis finds 100% SDV has minimal impact on quality”. Outsourcing-Pharma.com (2014). Accessed March 28, 2018.
https://www.outsourcing-pharma.com/Article/2014/11/20/Analysis-finds-100-SDV-has-minimal-impact-on-overall-data-quality
7 Meehan, L.” Remote SDV/SDR: Alternatives to Redact/Fax” (2015) Accessed March 28, 2018.
http://polarisconsultants.blogspot.com/2015/04/remote-sdvsdr-alternatives-to-redactfax.html
8 Curran, A. “Emerging trends in EDC.” White paper by Aris-Global. Accessed March 28, 2018.
http://www.arisglobal.com/wp-content/uploads/2016/05/EDC-US-15WICR72.pdf
9 Barra, M. “Clinical trial monitoring – the present and the future.” Pharmupdates (2015). Accessed March 28, 2018.
https://pharmupdates.wordpress.com/2015/03/11/clinical-trial-monitoring-the-present-and-the-future/
10 US Dept of Health and Human Services Draft Guidance “Use of EHR Data in Clinical Investigations.” (2016). Accessed March 28, 2018.
https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM501068.pdf
11 Medidata “Medidata solutions introduces RAVE targeted SDV.” (2010). Accessed March 28, 2018
https://www.mdsol.com/en/newsroom/press-release/medidata-solutions-introduces-rave-targeted-sdv
12 Levin, D. “SDV – Quality vs quantity – enough with 100% SDV.” (blog) (2011). Accessed March 28, 2018
https://www.clinipace.com/source-data-verification-quality-vs-quantity-enough-100-sdv/
13 FDA Guidance “Oversight of Clinical Investigation: A Risk-Based approach to Monitoring.” (2013). Accessed March 28, 2018
https://www.fda.gov/downloads/Drugs/Guidances/UCM269919.pdf
14 EMA Guidance “Reflection Paper on Risk-Based quality management in clinical trials.” (2013). Accessed March 28, 2018
http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2013/11/WC500155491.pdf
11 DATA BASICS 2018 Spring
15. MRC/DH/MHRA Joint Project “Risk adapted approaches to the management of clinical trials of investigational medicinal products.”. (2011). Accessed March 28, 2018
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/343677/Risk-adapted_approaches_to_the_management_of_clinical_trials_of_investigational_medicinal_products.pdf
16. Meely, K. “Risk adaptation in clinical trials of investigational medicinal products.” (blog) (2013). Accessed March 28, 2018 EMA Guidance
https://mhrainspectorate.blog.gov.uk/2017/11/16/risk-adaption-in-clinical-trials-of-investigational-medicinal-products-ctimps/
17. TansCelerate BioPharma
Position Paper: “Risk-Based Monitoring Methodology” (2013) Accessed March 28, 2018
http://www.transceleratebiopharmainc.com/wp-content/uploads/2013/10/TransCelerate-RBM-Position-Paper-FINAL-30MAY2013.pdf
18. ICH Harmonized Guideline “Integrated addendum to ICH E6 (R1): guideline for good clinical practice E6 (R2)” Accessed March 28, 2018
http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E6/E6_R2_Step_4.pdf
19. Tantsyura, Vadim, I.M. Dunn, K. Fendt, Y.J. Kim, J. Waters and J. Mitchell. “Risk-Based Monitoring: A Closer Statistical Look at Source Document Verification, Queries, Study Size Effects, and Data Quality” Therapeutic Innovation and Regulatory Science (2015). Accessed March 28, 2018
http://journals.sagepub.com/doi/abs/10.1177/2168479015586001
Moving away from 100% SDV
Continued
ABOUT THE AUTHOR:
Nimita Limaye – Dr. Nimita Limaye is an industry expert in Life Sciences with over 20 years of experience working across the global and local pharma, CRO, ITES industry. She has held key responsibilities for providing vision and direction in the clinical trial industry and has shaped organizational business strategy, driven operational excellence and managed large global operations. She currently runs her own consulting company, Nymro Clinical Consulting Services, which providing consulting, training and services to CROs, pharma companies, ITES, investment bankers, advisory firms etc on diverse topics related to clinical trials. She is the past chair of the board of SCDM and is on the steering committee of DIA India.
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MISSION OF Data Monitoring & Management
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13 DATA BASICS 2018 Spring
Leveraging Digital Technology in Clinical Trials By Ashish Bagde
Leveraging technology to optimize speed, quality and lower costs of clinical trials is a big hurdle for sponsors and CROs. Bringing drugs and medical devices to market faster is important for business success.
Sponsors and CROs are quickly realizing that in order to remain competitive, they need appropriate IT infrastructure as well as modern technology to accommodate an influx of clinical data that meets regulatory requirements. 1
There are multiple emerging technologies available to capture subject data. These technologies are making clinical trial subjects’ lives much more comfortable. They generate data quicker and more reliably. Many of the technologies are designed to be “Patient-centric” such as ePRO, wearable devices, digital pens, medical imaging, e-Consent, HER, mHealth, and even the adoption of virtual clinical trials.
Convergence of digital technologies is beginning to have a profound impact on the business models of many industries; they offer similar transformational potential when applied to the clinical trials process. For example, in the recruitment of patients and monitoring of clinical trials, digital technologies offer unprecedented opportunity to re-engineer the process entirely and do things in a truly transformative way. Moreover, this benefits all stakeholders involved in clinical development – the subject, the investigator, the clinical trial organization and the pharmaceutical company. 2
APPLICABILITY, ADVANTAGES AND CHALLENGES:
• APPLICABILITY & ADVANTAGES:
Digital data are original subject data that are digitally collected without having to first record the data on paper and then transcribe it into an EDC system. Digital data should not only be attributable, legible, contemporaneous, original, and accurate, but also complete, consistent, enduring and available when needed. An advantage of using digital data is that it makes it much more comfortable and easier for clinical trial participants. However, for elderly patients, using digitals may not be more comfortable for them. By using digitals in clinical trials, it drastically changes the conduct of the clinical trials, for example, subject data is sent securely to researchers who can immediately access information that would otherwise have to be collected by medical personnel through face-to-face interactions at trial centers. 3, 4
Paper-based record keeping in clinical trials has a long history and has served the community well for many years. It persists in part due to the interrelated factors of risk aversion, lack of knowledge, regulatory misperceptions, and perceived ambiguities. Varieties of digital technologies, as well as increasingly clear guidance from Western regulatory agencies present the opportunity for change. This change may have considerable positive ramifications in terms of a decreased burden on sites, sponsors and ultimately the community at large. It may cause the significant cost burden of the conduct of clinical trials to be potentially reduced or eliminated. This topic is addressed more in coming paragraphs. 5
Digital addresses one of the major challenges of EDC, the need to enter the clinical information from source documents into a CRF. Since for trials using digital technologies, clinical information flows directly into the study’s clinical trial database in real-time or near real-time, this allows earlier visibility and a faster start to data analysis. Additional benefits of using digital are: to eliminate unnecessary duplication of data, reduce transcription errors, promote the real-time entry of electronic source data during subject visits and to ensure the accuracy and completeness of data. 6
• CHALLENGES:
There are also challenges when using digital strategies in clinical trials, such as: authenticity and integrity of data, e-source product validation (IQ, OQ & PQ), user training, individual interest for use of e-source, data back up if a device is destroyed, maintenance of an audit trail of changes made by users and additional setup for e-source into EDC. In addition, various service providers develop eSource and the chances of compromising data privacy and protection are relatively high.
14 DATA BASICS 2018 Spring
DATA FROM SUBJECTS FOR CLINICAL AND OBSERVATIONAL RESEARCH:
Recent advancements in consumer-directed personal computing technology have led to the patient driven data generation (PDDG) with potential health applications and devices. This has catalyzed interest in PDDG that is defined as
“health-related data created, recorded, gathered, or inferred by or from patients to help address a health concern.” It offers several opportunities to improve the efficiency and output of clinical trials as well as for observational research, particularly within oncology. 7
Bring Your Own Device (BYOD) and mHealth (mobile health) both support the PDDG concept. The BYOD model is when trial participants use their own devices to submit data at their convenience. The FDA is interested in the BYOD model being used in an increasing number of clinical investigations. The agency is seeking input from relevant stakeholders on how new technologies — including mHealth, telemedicine, and remote sensors — could be used to design and conduct better clinical trials regardless of study participants’ locations. 8
Not only does PDDG offer an opportunity to capture information needed for use during care, there are potential cost savings as well as improvements in quality of data, care coordination, and patient safety. PDDG can provide a more comprehensive picture of ongoing patient health in the following ways: provide important information about how patients are doing between medical visits; gather information on an ongoing basis, rather than only at one point in time; and provide information relevant to preventive and chronic care management. 9
Overall, the use of PDDG offers an opportunity to capture needed information for use during care, with potential cost savings and improvements in quality, care coordination, and patient safety. 10
DIGITAL DATA MANAGEMENT IN CLINICAL RESEARCH IN FUTURE:By introducing all of the above-mentioned technologies, it would be expected that the data management team would be able to control the data generated from various devices and apps (applications) efficiently. In the future, data management professionals may be expected to be much more involved in trials and may be dealing directly with patients. The data management team has to monitor the data that are generated digitally and stored in a central location from the patient’s own device. Now that trials are becoming patient-centric, the role of data management is to provide input for the design and set-up of eConsents, ePRO and various wearable devices. Using these tools should help the project team to make the trial more patient-centric. Data management will also be expected to play a vital role in the area of patient recruitment and engagement.
Recently some trials have recruited patients by advertising on social media such as Facebook. All the trial details are posted such as: who is the sponsor, sponsor contact details, trial indication, eligibility criteria and most important how to register in the trial online. Interested patients can enroll in the trial by logging into the mentioned website. Afterward, all the trial details are later shared with the subject. After eConsent, the investigational product is shipped by trial nurses to a patient’s address. Nurses also play a crucial role by visiting patients to help them further participate in the trial. This type of trial concept is very useful especially if there are patient mobility concerns or if patients are unable to visit to hospitals frequently.
During this type of trial as data are generated from a participant’s device, the data management team closely observes and reviews the data. If needed, the data management team can connect with the patient for more details. As data are generated in real-time, project team members can closely monitor patients and immediately direct care be given for emergency situations. In the future, most trials will be shifting to remote online trials by using digital technologies/devices such as mobile devices and wearable devices. This will save on costs, patient visits and the time it takes to launch new drug in the market. 11
ADVANTAGE OF EHR & CLAIMS DATA IN CLINICAL RESEARCH
Advances in health information technology, EHRs and claims data present a new opportunity to use data collected through the routine operation of a clinical practice to generate and test hypotheses about the relationships among
Leveraging Digital Technology in Clinical Trials Continued
15 DATA BASICS 2018 Spring
patients, diseases, practice styles, therapeutic modalities and clinical outcomes. Using these data can lead to tremendous advantages in future clinical trials by leveraging these combined data assets to include analysis and exploration of healthcare costs and utilization, episodes of care, effective treatment regimens, comorbidities, adverse events, patient cohorts, etc. As a majority of EHR and claims data are in various formats such as structured and unstructured, it will be necessary to use “big data” tools and other technology to perform advanced analysis of this structured and unstructured data. 12
Although there are various technologies that have come into the market to capture and manage the data, the industry is still in the development phase. Many wearable device companies are still working on FDA requirements such as data authentication, data privacy and protection, as well as disaster recovery of possible data loss. Most trial-generated data are available through various vendors and misuse of valuable data by competitors can be a serious concern for any innovative sponsor pharmaceutical.
WAY FORWARD TO DEAL WITH DATA PRIVACY AND SECURITY REQUIREMENTS:
Data collected from clinical trials are entitled to special legal protections in order to safeguard the confidentiality and privacy of the human involved in such research. In the United States, such protections are set forth in the laws as well as in FDA regulations, both of which contain additional safeguards for human subjects involved in research. 13
There are many apps for clinical trials approved by the FDA. The FDA expects sponsors and CROs to work with the only approved service providers, however, this area is relatively new and a lot of improvement is required. To meet regulatory requirements, service providers as well as experts from industry are coming forward to help develop solutions. Various apps/technologies have been used in trials that have saved trial execution time as well as improve the quality of data generated.
The following strategies can be useful to deal with Data Privacy and Security:
• Although most of the developed countries have data protection and privacy policies/rules in place (e.g., HIPPA in US), there are many countries who need to develop a robust policy/rule as well. Governments should form a body to review and upgrade the policies/rules periodically.
• All companies (Sponsors, CROs, etc.) who deal in health data collection, its storage and processing, should have data privacy and protection agreements with government authorities.
• All organizations who deal in healthcare records should have approval from government authorities before doing any business. In addition, service providers need to disclose/ensure how their business will not compromise in area of data protection and privacy.
• Periodically audits/inspections should be conducted by government-approved agencies and reports should be available publicly disclosing any violations for compromising data protection and privacy.
• Any new system/solution/app/product that deals with healthcare data should have approval from concerned authorities before it is used in humans.
• All the health records that are used in clinical trials should be shifted to electronic health records and the system used should comply with the 21-CFR Part 11 requirement.
• Hospitals/trial sites should have an access control system so that only authorized individuals can have access their online/digital health records.
Leveraging Digital Technology in Clinical Trials Continued
16 DATA BASICS 2018 Spring
CONCLUSION:
Pharmaceutical companies are leveraging digital health data more than ever before to improve clinical trials and the drug discovery process, ultimately allowing the efficient creation of game-changing drugs that could have a significant impact on subject lives. To get started, pharmaceutical companies can integrate digital health data to streamline four key areas of drug development: subject recruitment, remote patient monitoring during study conduct, post-marketing research and interaction with patient communities. 14
REFERENCES:
1 “New Trends in eClinical Systems Integration,” clinCapture, 21 September 2015, access date 21 March 2018,
http://www.clincapture.com/new/new-trends-in-eclinical-systems-integration/
2 Hans Poulsen, “Clinical Trials at a Digital Inflection Point”, wipro, access date 22 March 2018,
http://www.wipro.com/documents/clinical-trials-at-a-digital-inflection-point.pdf
3 Jules T. Mitchel, Jonathan Helfgott, Tom Haag, Silvana Cappi, Imogene McCanless, Yong Joong Kim, Joonhyuk Choi, Timothy Cho, Dean A. Gittleman, “eSource Records in Clinical Research” Applied Clinical Trials 16 April 2015, access date 22 March 2018,
http://www.appliedclinicaltrialsonline.com/esource-records-clinical-research
4 “Sanofi launches digital clinical trials to improve recruitment and reduce trial times” Centre Watch, 03 March 2017, access date 22 March 2018,
https://www.centerwatch.com/news-online/2017/03/03/sanofi-launches-digital-clinical-trials-improve-recruitment-reduce-trial-times/
5 Jules T. Mitchel, Jonathan Helfgott, Tom Haag, Silvana Cappi, Imogene McCanless, Yong Joong Kim, Joonhyuk Choi, Timothy Cho, Dean A. Gittleman, “eSource Records in Clinical Research” Applied Clinical Trials, 16 April 2015, access date 22 March 2018,
http://www.appliedclinicaltrialsonline.com/esource-records-clinical-research?pageID=3
6 Rick Morrison, “Technology’s Role in Clinical Trials” Applied Clinical Trials, 05 March 2015, access date 22 March 2018,
http://www.appliedclinicaltrialsonline.com/technology-s-role-clinical-trials
7 Wood WA, Bennett AV, Basch E, “Emerging uses of patient generated health data in clinical research” pubmed.gov, 27 August 2014, access date 22 March 2018,
https://www.ncbi.nlm.nih.gov/pubmed/25248998
8 Jof Enriquez, “FDA Solicits mHealth, Wearable Tech Info For Clinical Trials” Med device online, 03 November 2015, access date 22 March 2018,
https://www.meddeviceonline.com/doc/fda-solicits-mhealth-wearable-tech-info-for-clinical-trials-0001
9 “Patient-Generated Health Data” HealthIT.gov, access date 22 March 2018,
https://www.healthit.gov/policy-researchers-implementers/patient-generated-health-data
10 “Patient-Generated Health Data” HealthIT.gov, access date 22 March 2018,
https://www.healthit.gov/policy-researchers-implementers/patient-generated-health-data
11 “Science 37 Secures $31M Series B Funding to Expand Clinical Trial Access to Anyone, Anywhere, Anytime” Science 37, 18 October 2016, access date 22 March 2018,
https://www.science37.com/science-37-secures-31m-series-b-funding-expand-clinical-trial-access-anyone-anywhere-anytime/
Leveraging Digital Technology in Clinical Trials Continued
17 DATA BASICS 2018 Spring
Leveraging Digital Technology in Clinical Trials Continued
12 Weiner MG, Lyman JA, Murphy S, Weiner M, “Electronic health records: high-quality electronic data for higher-quality clinical research” pubmed.gov.in, 2007, access date 22 March 2018,
https://www.ncbi.nlm.nih.gov/pubmed/1787787414
13 Phillip DeFedele, “Data Protection in Clinical Trials: Adapting EU Solutions to US Research” 2015, access date 22 March 2018,
http://scholarship.shu.edu/cgi/viewcontent.cgi?article=1816&context=student_scholarship
14 “Advancing Drug Development with Digital Health” Validic, March 2016, access date 22 March 2018,
http://pages.validic.com/rs/521-GHL-511/images/eBook%20-%20Advancing%20Drug%20Development%20with%20Digital%20Health-4%20Key%20Ways%20to%20Integrate%20Patient-Generated%20Data%20into%20Trials.pdf
ABOUT THE AUTHOR:
Ashish Bagde – Working as a Manager in CDM with Dr. Reddy’s Laboratories, India, with over eleven years of experience. Ashish is responsible for overall data management deliverables. Ashish has presented at the SCDM 2016 Annual Conference on ‘Impact of Technology on Future Clinical Trials’. Ashish is an active member of SCDM and involved in the GCDMP book revision as co-author. Ashish has implemented DM systems as well as created all CDM SOPs in his current organization. In the past, Ashish has worked with Cognizant, InventivHealth and GVK Biosciences.
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DATA BASICS 2018 Spring
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