PHUSE Clinical Data Scientists Guide to Studies in COVID-19

1 | PHUSE Deliverables

PHUSE Clinical Data Scientists Guide to Studies

in COVID-19

phuse.eu

PHUSE Clinical Data Scientists Guide to Studies in COVID-19

2 | PHUSE Deliverables

Contents

Introduction 1

Regulatory Background 1

Data Collection and Tabulation 1

Data Analysis 2

Data Sharing 3

Conclusion 3

Disclaimer 3

References 4

PHUSE Clinical Data Scientists Guide to Studies in COVID-19

PHUSE Clinical Data Scientists Guide to Studies in COVID-19

1 | PHUSE Deliverables

Introduction

As the COVID-19 pandemic severely impacts many facets of human activity around the world, the pharmaceutical industry is being presented with significant challenges related to clinical trial research and development activities Regulatory authorities have released guidance documents to support sponsors in establishing clinical trials for vaccine, therapeutics and diagnostic tests in COVID-19

Clinical trials are an essential component of the response to the global health emergency in ensuring an evidence-based approach to identify which vaccines, therapeutics and diagnostics tests are both safe and effective Given the circumstances, there is a clear need for urgency in designing, executing and reporting clinical studies This guide offers perspectives from industry experts on what Clinical Data Scientists [1] (i e those who analyse data collected in clinical trials) can expect in studies in COVID-19 and strategies to address some of the challenges they may encounter It is important that the reader considers the specific circumstances of each trial and consults with their specific sponsor company’s procedures and work instructions It is essential to collaborate with all relevant team members to ensure all updates are managed appropriately It will also be beneficial to discuss proposed approaches with health authorities and individual review divisions as part of taking decisions for individual scenarios

“Out of clutter, find simplicity From discord, find harmony In the middle of difficulty, lies opportunity” is a quote from Albert Einstein Clinical Data Scientists find themselves in the pandemic needing to readdress their approach to designing clinical trials, collecting patient data and analysing that data Tasks that were previously done sequentially need to be done in parallel and some tasks may be deprioritised to meet accelerated timelines so that insights can be reported and shared with the global community To achieve this, each activity needs to be approached by the Clinical Data Scientist with an agile mindset

The variety of scenarios that a Clinical Data Scientist faces is a feature of the research community’s response to the pandemic Clinical studies are being initiated in both therapeutics and vaccines, as well as for diagnostic tests For therapeutics, in particular, there are significant resources being deployed to repurpose existing treatments to establish which may be effective in treating COVID-19, either as monotherapies or as part of a combination This has led to significant heterogeneity in the studies that are being initiated, in terms of the setting (e g home vs hospital), disease severity (e g mild, moderate, severe) and patient populations An additional complication is that the Standard of Care for patients with COVID-19 is evolving continuously Studies need to be managed carefully so that advances in medical knowledge are appropriately accounted for and that appropriate adjustments are made

Regulatory Background

To address the ongoing COVID-19 global pandemic, both the FDA (Drug Product [2], Vaccine [3]) and EMA [4] have issued guidance for the pharmaceutical industry interested in developing treatments or vaccines for COVID-19 Both agencies have accelerated pathways to help speed up the regulatory processes while ensuring patient safety and data quality This, however, has also meant the sponsors should match the speed and flexibility of the regulators Materials that had ‘months’ to prepare before have to be prepared and submitted in ‘days’ Nor do the deliverables follow the traditional timelines or order Many of the review materials that are not normally required until later will be required at Pre-IND consultations in final or near final drafts Resources both internal and external must be allocated, at risk, even before the regulatory approvals are in place Also, a very quick turnaround on any feedback from regulators is needed in order to take advantage of ‘warp speed’ that governments/agencies around the world have launched It is therefore essential that sponsors engage with regulatory agencies very early on all aspects, and commit resources, while the plan for COVID-19 trials is being worked on, in order to take advantage of the expedited regulatory review and decision This would enable clinical data scientists to get early feedback on the data collection and analyses strategies that are in line with regulatory expectations for COVID-19 and provide lead time for development of required sponsor documents to support the trials

Data Collection and Tabulation

Since the emergence of the novel COVID-19 virus, the pharmaceutical industry has rallied to tackle the pandemic, but clinical development in the area poses many challenges, not least for data scientists With the treatment and clinical trial landscape evolving continually, we need to embrace agility to ensure the best possible data collection and analysis outcomes

The programming and data science community has undoubtedly moved quickly: CDISC established a therapeutic area user group for COVID-19 [5] and has released standards in record time The WHO has also issued guidance on best practices for working in the area, with the release of the ISARIC-WHO CRF forms [6] As data scientists, working in this disease area means getting up to speed with the demands of this new field quickly

Here, we’ll tackle some of the key considerations and factors to bear in mind:

• Urgency: Like other clinical trial professionals working in COVID-19, data scientists need to be willing to work under intense pressure With trials starting quickly, Protocols, eCRFs, vendor data specifications and analysis plans all need to be set up and reviewed in record time

• Data Collection Considerations: We must be conscious of the situation at investigational sites For certain studies (hospitalised/critical care studies in particular), there may be limited resources available at sites for data entry and dealing with data cleaning activity For others (e g early treatment studies in outpatients), there may be other considerations around how best to design and collect Patient Reported

Doc ID: WP-053 Version: 1.0 Working Group: COVID-19 Date: 11-SEP-2020

PHUSE Clinical Data Scientists Guide to Studies in COVID-19

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Outcomes (PROs) and/or the mechanisms for data collection (e g telephone visits) As mitigation, data scientists should use their knowledge and experience to help influence: lean protocols, lean data collection, clarity of endpoints, and a data collection design that ensures analysis needs can be met easily and quickly downstream [7]

• Concomitant Medications: Data scientists need to be acutely aware of the importance of accurate and timely collection of concomitant medications for studies in COVID-19 The treatment environment and standard of care for COVID-19 is changing quickly, so we should be aware of changes to background medications and how this may affect programming and analysis activity (including estimands)

• Non-visit-based Collection of Data: In COVID-19 trials, it is quite common to collect data such as hospitalisation and ventilation status, continuously using log forms as opposed to specifically at defined visits As these data are likely to contribute towards primary or secondary efficacy analyses, data scientists should pay special attention to the design considerations, derivations and data integrity checks applied to these data Due to the timing-independent nature of log form entries, data scientists will need to thoroughly investigate these data points and consider how this will impact the structure of analysis datasets This will be particularly important where the same data contribute to both point in time analyses (e g NEWS2) and continuous analyses (e g ventilator-free days)

• Agility: With COVID-19 being a new phenomenon, it is likely that studies in this disease area may be subject to changes instream Whether it’s sample size changes, squeezed timelines, eCRF updates (new forms, changed forms), changes to estimands, ad hoc safety review meetings or IDMCs, data scientists need to be agile and prepared to adapt within studies as new information emerges Data Scientists should also consider ways in which they can provide rapid data insights to the clinical study team members (e g through the use of flexible data review tools for fast interrogation of potential signals)

scientists to stay abreast of development of new endpoints across the industry, for example by keeping abreast of CDISC developments Also, it is important to stay aware of the ever-changing set of information relating to this virus For example, the new ordinal endpoints are focused on the main symptom of this disease of respiratory failure and organ shutdown There is evidence of other types of impacts of the virus on the body which may lead to long-term consequences for the patients It is possible that these longer term conditions will become a focus and there will be a need for studies to extend to long-term follow-up and further new endpoints based on emerging conditions

Although there are challenges related to endpoints for COVID-19 clinical trials, existing CDISC ADaM standards can be utilised to derive specific endpoints For example, the ADaM time to event BDS dataset (ADTTE) could be used to derive parameters for time to event analysis Some clinical trials are using 6–10 point ordinal scales for COVID-19 clinical trials, which would be contained in an efficacy-specific ADaM dataset, with appropriate parameter and results variables – general details for rating scales are provided by CDISC [9] Additional subject-level flags could be added in ADSL for subgroup analysis as required For safety assessments, standard ADaM BDS or OCCDS datasets can be used but may require additional variables or record-level flags for specific analysis It would be useful to check the latest developments with industry standards for COVID-19 trials as there are already some efforts to collate the data across all current research efforts (e g the International COVID-19 Data Research Alliance [10])

Any change to the planned study outcome should ideally be done with reference to the estimand framework in the ICH E9 addendum [11] An estimand is a precise definition of the treatment effect to be estimated There may be some changes that may not affect the estimand (e g a change in sample size) A potential change to an estimand may be that the comparison treatment changes – as the standard of care evolves over time, with the discovery of new or improved treatments These changes in the standard of care should be anticipated, and planned for, by the clinical study teams Standard-of-care flags which change over time may be required in datasets Other factors that may affect the estimand are the population, variable, handling of intercurrent events and a population summary Missing data and other issues that may affect the analysis should be explored using sensitivity analyses

Note that a change in estimand may require a change to the trial design and/or conduct, so these discussions should take place within the larger clinical project team Further discussion on issues relating to estimands in COVID-19 trials can be found in Brennan C Kahan et al [12] As noted by the authors, handling outcomes truncated by death is of particular importance in COVID-19 trials Changes to any planned analyses should be labelled as post hoc in the trial publication and clinical study report

Traditional data analysis for clinical trial reporting relies on building an unchanging set of analysis to allow for rapid production of the pre-defined analysis at the end of the clinical trials Rapid production of results at the end of clinical trials is still an important part of COVID trials, but what the analysis will be is expected to evolve during the trial A risk-based approach to analysis can be followed:

Data Analysis

Due to the fact that this is a new disease under study, with an urgency that required rapid protocol development, there are cases where new information or understanding is leading to changes in the endpoints and outcomes under study, whilst the study is still running (for example, the RECOVERY trial changed primary outcome from in-hospital death to death within 28 days from randomisation after the trial was underway) This is unusual and requires more flexibility than is normally required from study programmers, statisticians and data scientists as a change to an outcome variable requires potential changes to analysis plan, both output and dataset specifications, and programming at SDTM, ADaM and outputs It would be useful for organisations to plan to have additional resources available at short notice where possible, to help if required

Examples of efficacy endpoints in COVID-19 trials are clinical status at an appropriate time point assessed using an ordinal scale, death within an appropriate timepoint, respiratory failure within an appropriate timepoint, duration of mechanical ventilation, ICU/hospitalisation with moderate to severe disease and time to clinical improvement/recovery within sustained duration [8] Further details on types of endpoint and statistical analysis can be found in [8] It is a good idea for data

PHUSE Clinical Data Scientists Guide to Studies in COVID-19

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• Identify data types more likely to have changes in analysis • Assign data analysis resource based on the risk of change and

requirement for further analysis For some data types, it may be necessary

• For some data types, resourcing will need to be similar to submission rapid response resourcing, where data analysis experts work closely with clinicians to clarify the final analysis through a series of ad hoc reporting

There is a significant risk that clinical trials may submit earlier than expected or have unexpected interim submission It should be the approach of study reporting to assess submission readiness continuously and from an early stage This can be achieved through:

• continuous validation of datasets against submission requirement, e g validation each time a dataset is produced

• producing Define-XML early in reporting and having a process in place for keeping this document up to date and validated

• a similar approach taken for SDTM aCRF• keeping the reviewers’ guides as live documents throughout the

study reporting cycle

Maintaining submission readiness throughout the study reporting facilitates the ability to react to changing timelines

It is also important to maintain close ties with study decision-makers to allow for early notification of change in timelines These types of relationships can help in formulating innovative solutions to meet accelerated reporting requirements, e g prioritisation of subset outputs, handling reporting requirements through ad hoc reporting rather than a formal study reporting stream

Data Sharing

In recent years, the broader value of individual patient data (IPD) collected during clinical trials has been more widely recognised Many sponsors have now defined voluntary mechanisms through which to share these data with qualified external researchers Researchers seek to pool data from multiple trials and multiple sponsors, which provides a rich data pool for novel analyses Some of the prominent platforms through which anonymised IPD and associated documents are Clinical Study Data Request (CSDR), Yale University Open Data Access Project (YODA), Supporting Open Access for Researchers, Project Data Sphere, and Vivli

Through the myriad of study changes and communications required during COVID-19, there are still safeguards to the sharing of personal data and implications for doing so improperly Many countries, including China and South Korea, are tracking COVID-19 infected individuals through mobile tracking apps to ensure the safety of others, where the design of such apps ensures ‘privacy-first’ (i e the personal data collected by the app is encrypted and remains secure on the phone until it is needed to ease medical intervention) These apps can also assist health authorities in finding and quarantining people who have encountered infected individuals

The EU General Data Protection Regulation (GDPR) is a broad piece of legislation and provides for rules that also apply to the

Conclusion

While the COVID-19 pandemic has been difficult, as a sector we have risen to the challenge Using our innate agility, dedication and ingenuity as data scientists, we have been able to contribute to an inspirational and coordinated global pandemic response

Disclaimer

The opinions expressed in this document are those of the authors and should not be construed to represent the opinions of PHUSE members’ respective companies or organisations or the FDA’s views or policies The content in this document should not be interpreted as a data standard and/or information required by regulatory authorities

processing of personal data in the context of COVID-19 The GDPR allows competent public health authorities and employers to process certain personal data in the context of an epidemic, in accordance with national law and within the conditions set therein Specifically, Articles 6 and 9 of the GDPR enable the processing of personal data, when it falls under the legal mandate of the public authority provided by national legislation and the conditions enshrined in the GDPR Further related guidance has been adopted recently by the European Data Protection Board (EDPB)

The EDPB guidance explains about lawfulness of the data collection and processing, and importance of adopting sufficient security methods and confidentiality policies to ensure legitimate data sharing, as needed [13] During reactivation of businesses, several governments issued guidance of collecting visitors’ personal data for contact tracing purposes in case of any untoward circumstances The ICO has advised businesses to collect minimal contact data of the customer with complete transparency, while storing the data carefully and without using it for any direct marketing [14]

More information on the impacts of COVID-19 on Clinical Trial Transparency and Document Disclosure is available on the PHUSE Blog [15]

PHUSE Clinical Data Scientists Guide to Studies in COVID-19

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References

[1] PHUSE Data to Knowledge – https://www.phuse.eu/d2k

[2] COVID-19: Developing Drugs and Biological Products for Treatment or Prevention – Guidance for Industry, FDA – https://www.fda.gov/media/137926/download

[3] Development and Licensure of Vaccines to Prevent COVID-19 – Guidance for Industry, FDA – https://www.fda.gov/media/139638/download

[4] EMA initiatives for acceleration of development support and evaluation procedures for COVID-19 treatments and vaccines, EMA – https://www.ema.europa.eu/en/documents/other/ema-initiatives-acceleration-development-support-evaluation-procedures-covid-19-treatments-vaccines_en.pdf

[5] Interim User Guide for COVID-19, CDISC – https://www.cdisc.org/system/files/members/standard/ta/Interim_User_Guide_for_COVID-19.pdf

[6] ISARIC-WHO Case Report Forms (CRFs) – https://isaric.tghn.org/COVID-19-CRF/

[7] End-to-End Data Flow Today: Current Processes and Challenges – Chris Price (Roche) & Praveen Garg (AstraZeneca) on behalf of the PHUSE Executive Summit (April 2019) – https://www.phusewiki.org/docs/Summits/End-to-End%20Data%20Flow%20Today%20Current%20Processes%20and%20Challenges.pdf

[8] ICH (2019), Addendum on Estimands and Sensitivity Analysis in Clinical Trials to The Guideline on Statistical Principles for Clinical Trials – https://database.ich.org/sites/default/files/E9-R1_Step4_Guideline_2019_1203.pdf

[9] CDISC Interim User Guide for COVID-19 – Questionnaires, Ratings, and Scales – https://wiki.cdisc.org/display/COVID19/Questionnaires%2C+Ratings%2C+and+Scales

[10] International COVID-19 Data Research Alliance and Workbench – https://www.hdruk.ac.uk/covid-19/international-covid-19-data-alliance/

[11] Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials E9(R1) – https://database.ich.org/sites/default/files/E9-R1_Step4_Guideline_2019_1203.pdf

[12] Treatment estimands in clinical trials of patients hospitalised for COVID-19: ensuring trials ask the right questions –

https://mfr.ca-1.osf.io/render?url=https://osf.io/ry7sf/?direct%26mode=render%26action=download%26mode=render

[13] Statement on the processing of personal data in the context of the COVID-19 outbreak – https://edpb.europa.eu/sites/edpb/files/files/file1/edpb_statement_2020_processingpersonaldataandcovid-19_en.pdf, accessed on 23 July 2020

[14] Data protection and coronavirus: what you need to know – https://www.shlegal.com/news/data-protection-and-coronavirus-what-you-need-to-know, accessed on 23 July 2020

[15] The Impacts of COVID-19 on Clinical Trial Transparency and Document Disclosure PHUSE CTT Project – https://www.phuse.eu/blog/the-impacts-of-covid-19-on-clinical-trial-transparency-and-document-disclosure-phuse-ctt-project

Acknowledgements

PHUSE would like to express our profound gratitude to the task force participants and their respective companies for their involvement in the development of this resource

Wendy Dobson (PHUSE), Shalini Dwivedi (Kinapse), Harivardhan Jampala (Covance), Matt Jones (Veramed), Kevin Kane (PHASTAR), Rakesh Kumar (GSK), Yvonne Moores (GSK), Chris Price (Roche), Nate Root (Ionis Pharma), Saloni Shah (Covance)