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Spring 2018 | PhUSE News | 1

C O N N E C T – S H A R E – A D VA N C E

PhUSE NewsSpring 2018

All the regular features...SDE recaps, Kirk’s Korner, and ten questions with Scott Bahlavooni

Looking backA brief history of our beginnings as a society

CSS 2018All the vital information about our upcoming US event

2 | PhUSE News | Spring 2018 Spring 2018 | PhUSE News | 3

Has spring sprung? Here in South East England, it’s certainly thinking about it. In contrast to last year, the weather is appropriately frosty and the skies are blue. Whilst the weather is inspiring positivity, less can be said for the merry-go-round that is politics, which lately has been giving me a feeling of déjà vu thanks to the reporting of what seems to be the same discussions over and over. Thank goodness for the ever-evolving world of science! PhUSE undeniably plays its part, inspiring clinical data scientists to look ahead, as evidenced in the conference themes this year: the US Connect 2018 – Transformative Current and Emerging Best Practices, and the EU Connect 2018 – Future Forward. Not forgetting the Computational Science Symposium, which encourages us to Advance. You can find out more about these events in this edition of the newsletter – March brings us the CSS in Maryland, which you can read about on pages 6 to 8, followed in June by the US Connect conference in Raleigh (pages 10 and 11).

Elsewhere in the first newsletter of 2018, the success of PhUSE Single Day Events continues with expansion into new geographical areas. On pages 14 to 19, you can catch up with reports from Shanghai, Durham, Bloemfontein, and Mumbai. Not to be outdone, we have Kirk’s Korner starting on page 20 along with creating GIF file tips from Anik Chatterjee. Let’s not forget our regular 10 questions spot – get to know Scott Bahlavooni, PhUSE Working Groups Director on page 4. And whilst we’re meeting people, we introduce a few of our new members on page 27.

Don’t forget that the Call for Papers tool for the EU Connect in Frankfurt opens 5th March, so now is the time to submit your abstracts for this hugely popular conference.

As always, if you have something to say or contribute, please feel free to get in touch at [email protected].

Diana Stuart

From the Editor

Q1/2018

Contact us PhUSE Office, Kent Innovation Centre, Millennium Way, Broadstairs, Kent CT10 2QQ, England, UK

Comms channels UK +44 1843 609600 USA +1 609 514 5105 +44 1843 265850 [email protected] phuse.eu

Social @phusetwitta @phusesnappa /phusebook /phusetube /phuseoffice

N E W S

4Starter for tenWith Scott Bahlavooni

5A brief historyLooking back at PhUSE membership

6CSS US 2018Info on our upcoming US event

10US Connect 2018All about the event and our host city

13PSI 2018Five reasons to attend

R E V I E W S

14–19SDE recapsReports from Shanghai, Durham, Bloemfontein, and Mumbai

T E C H

20Kirk’s KornerTech tips

24Next gen data vis via GIFA case study

I N F O

26New membersIntroducing our latest members

28Dates for your diaryEvents in the year ahead

30Advertising and membershipGet your message out to our membership

In this issue

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1 | PhUSE News | Summer 2017

June 3rd–6thRaleigh Convention Center

Raleigh, North Carolina, USA

The Clinical Data Science Conference

CONNECT SHARE ADVANCE

CONNECT

US 2018


Chair Jim Johnson, Summit Analytical Co-Chairs Jennifer True, GSK & Terek Peterson, Covance

phuse.eu

#15633 PhUSE Connect Development June 2017 Covers A4 v5.indd 1 08/02/2018 16:22

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PhUSE News is published quarterly. For advertising and other enquiries, please email [email protected] or call +44 (0)1843 609603. See page 30 for more details.

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HAT ATTRACTED YOU TO your current position? I wanted to be part of an organisation with corporate values and

individuals committed to improving and changing the clinical research and regulatory science paradigm. What’s the most enjoyable part of your job? Interacting with clients, colleagues, and co-workers.And the least enjoyable? Travel.If you could only eat one food for the rest of your life, what would it be? Two Roads’ Roadsmary's Baby.

What – to you – is the most irritating industry buzzword? Metadata. Life science companies’ unhealthy fixation on ‘metadata’ has caused us to lose sight of the scientific meaning of our information (data), and new ways of working. Life science companies believe metadata will enable the automation of 20+ year-old business processes. The sooner we realise this is a fallacy, the sooner we can truly evolve and revolutionise clinical research and regulatory science data management and analytics.If you weren’t doing this job, what would you be doing? Walking dogs. If you could give your 18-year-old self one piece of advice, what would it be? It’s always the quiet ones… always.Can you describe yourself in three words? Introvert, dork, dog-lover.What is the one thing you can’t live without?My wife.What is your musical guilty pleasure? Vince Guaraldi Trio: A Charlie Brown Christmas. Scott Bahlavooni is a Senior Consultant at d-Wise

Q & A

“ Life science companies’ fixation on metadata has caused us to lose sight of the scientific meaning of our information and new ways of working”

Looking backGetting to know you

Starter for ten

Clockwise from top left Scott and his dog Olivia; Scott in profile; Scott walking his three dogs, Missy, Olivia, and Mollie; a cool glass of Scott’s favourite drink

Facing the quickfire questions this issue is PhUSE Working Groups Director, Scott Bahlavooni

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just one year after the first meeting. With over 270 registered delegates from 12 countries, drawn from over 50 organisations in attendance, the initial goal of advertising and promoting PhUSE within the industry had been achieved, and was an overwhelming success.

Stephen Bamford, when working at Pfizer, noticed that statisticians and data managers each had their own community and conferences – but there was nothing for programmers. Following several attempts to get the DIA to have a programming stream, Stephen contacted James McDermott with his idea for a new European conference for programming in the healthcare arena and, together, they contacted a small number of volunteers to brainstorm and then organise the first conference. The name ‘PhUSE’ came out of a lunch meeting that was held at the UK SAS Forum. Prior to that, PhUSE’s working title was EPSUG – European Programmers’ Software User Group.

Since that first Conference, PhUSE has created a membership society, hosted Single Day Events and FDA/CSS conferences and set up a range of Working Groups – innovations that have catapulted PhUSE into a global not-for-profit, passionate-about-data-science membership society run by dedicated volunteers. As membership continues to grow, likewise PhUSE continues to expand according to its motto: connect–share–advance.

LONG, LONG TIME AGO, clinical trials were paper-based: tons of paper, valuable beyond compare, leading

the advancement of data science in the pursuit of human health and welfare. This was followed by computer-assisted NDAs, with data that were databased in computer systems – structured and documented – delivered to the FDA.

Electronic data capture systems came next, replacing the traditional paper-based data collection methodology, streamlining data collection and expediting the time to market for drugs and medical devices.

In 1997, CDISC was founded, as the pharmaceutical industry moved towards standards such as SDTM and ADaM. Since then, CDISC has grown into a standards organisation dedicated to developing global standards. Regulatory agencies, notably the FDA, enthusiastically supported the advent of a different standardisation approach based on hardware and software advancements.

With the discontinuation of SEUGI conferences following the 2003 21st SEUGI conference in Paris, France, a determined group of European volunteers met in September 2004 to create a new type of conference, one organised by a not-for-profit organisation – PhUSE – to continue the advancement of data science. The inaugural PhUSE Conference was held in Heidelberg, Germany in October 2005,

M E M B E R S H I P

A brief historyA look at how we began as a membership society

By FRANK SENK, PhUSE Membership Director

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“ PhUSE’s innovations have made it into a global society of almost 9,000 members”


Sunday, March 4th16:00–17:00 On-site registration opens17:00–19:00 Opening Session: Welcome and Working Group Overview19:30–21:00 Linked Data for Clinical Trials: An Interactive Hands-on Workshop

Monday, March 5th08:30–10:30 Be an FDA Reviewer for the Day10:30–11:00 Break and networking11:00–12:00 Working Group Overview Session12:00–13:00 Lunch and networking13:00–16:30 Working Group Breakout Sessions17:00–18:30 Poster Session18:30–20:00 GitHub Environment: From White Paper Targets

(TFL) to Shareable Scripts Workshop18:30–20:00 Maintaining the Clinical & Nonclinical Study Data

Reviewer's Guides Workshop

Tuesday, March 6th08:30–12:00 Working Group Breakout Sessions12:00–13:00 Lunch and networking13:00–14:00 Working Group Wrap-up14:00–14:30 Break and networking14:30–16:00 Defining the CSS Mission Statement and Principles:

Ideation and Collaboration16:00–16:30 Poster Awards and Closing Session

RENE BROWN SAID, “Courage starts with showing up and letting ourselves be seen.” Seven years ago, 250

courageous people showed up to the first Computational Science Symposium to embark on a new kind of collaboration. At the time, these representatives from life sciences companies, the FDA, technology providers and academia were uncertain if the journey would end on a mild Silver Spring afternoon or blossom into a transformational global collaboration. As we approach the 7th annual CSS, we are pleased that the PhUSE Working Groups have indeed matured into a transformational collaboration between industry, global regulators, technology providers, standards development organisations and academia.

The 7th annual CSS will once again bring together courageous clinical research and regulatory science stakeholders from around the world to collaborate and incubate. Although the CSS is primarily a working conference, it also includes traditional elements such as plenary and closing sessions, posters and networking. However, at the heart of the conference is the collaboration on PhUSE Working Group projects. The CSS also provides unparalleled opportunities to engage with regulators including representatives from the FDA’s CDER, CBER and CDRH, the PMDA and the EMA. FDA/CDER is a co-sponsor of the CSS, an indicator that the Agency officially recognises your work, accomplishments and commitment that are demonstrated in this unique open collaboration; this work is designed to promote the ‘public good’. CDER co-sponsored events are considered by the Center and Agency to be opportunities “to share CDER’s vision, policies, current thinking, and ideas”.

If you are courageous enough to attend the 2018 CSS, take heed: this is not a meeting where you sit comfortably

in a chair and listen to a series of recycled presentations while monitoring your email, Facebook and Instagram. This is an opportunity to sit as peers whether you’re a biostatistician just out of university or an FDA Reviewer with 20 years of experience and collaboration. You will listen, connect and share your thoughts and ideas in a welcoming, non-competitive, transparent forum focusing on developing tangible work packages that improve clinical research and regulatory science and, ultimately, benefit patients needing the therapies.

The CSS will kick off at 5pm on Sunday, March 4th with the opening session, and end at 4:30pm on Tuesday, March 6th. The event will feature three workshops, a session where you can learn what it’s like to be an FDA Reviewer, and an interactive panel discussion featuring senior leadership and thought leaders from the FDA, PMDA, CDISC, and PhUSE.

Please visit the PhUSE website for a description of all PhUSE Working Groups – attendance is limited; therefore, please register early. The PhUSE Working Groups have matured into a transformative global collaboration because of the courageous stakeholders that volunteer their time. We understand that not everyone can volunteer their time, but we would encourage you to find a way to contribute if you can. Simply raising awareness about the PhUSE Working Groups makes a big difference. Be courageous; collaborate; connect; advance; share. See you at the 2018 CSS!

Turn the page to read more about our keynote speakers

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Around the worldAround the world

U S E V E N T S

CSS US 2018Our annual US symposium is almost here4th–6th March 2018

“ We’re pleased that the Working Groups have matured into a transformational collaboration”

By SCOTT BAHLAVOONI, PhUSE Working Groups Director, and CHRIS DECKER, PhUSE/FDA

Below Mingling and networking at last year’s CSS


Around the world

Helena Sviglin Helena works at the FDA CDER Office of Computational Science as an epidemiologist. She joined the FDA in 2010. Helena is currently contributing to the development of the Technical Conformance Guide (TCG), FDA Business Rules, FDA Validator Rules, TAUG evaluations, FDA Data Standards Catalog and Core DataFit (self-serve data validation pilot).

Helena’s past contributions include the Development of Technical Rejection Criteria, an internal process to evaluate PhUSE deliverables such as the SDRG development, FDA legacy data conversion, FDA Amendment 1, CDISC Trial Summary overhaul, OCS JumpStart service development and the DataFit development.

Alan Shapiro Alan is a Pediatrician Infectious Diseases Specialist and Medical Officer working in the Office of Computational Science (OCS) at the Center for Drug Evaluation and Research

(CDER) at the FDA. He received his MD and PhD from the University of California, San Francisco.

Alan has been at the FDA since 2003. He has worked as a paediatric consultant in the Division of Pediatric Development and as a primary clinical reviewer in the Division of Antiviral Products in the Office of New Drugs. Alan joined the OCS in November 2015 and has been involved in managing and supporting OCS services, tools and training. He applies his experience in clinical review and analysis to guide reviewers in CDER’s JumpStart service and the utilisation of OCS tools.

Lilliam Rosario Lilliam is the Director of the Office of Computational Science (OCS) in the Office of Translational Sciences, CDER. The OCS leads and directly supports the modernisation of CDER’s scientific computing capabilities by providing services across the spectrum of innovation. This

includes supporting the submission and use of high-quality data and providing access to high-end analytical tools and training, thus enabling overall regulatory review processes.

Lilliam has also served as Associate Director in the FDA’s Office of Regulatory Science and Innovation, Office of the Chief Scientist. In this position, she provided strategic leadership and support for innovation in FDA science. She has also worked in the Office of New Drugs in the FDA’s Center for Drug Evaluation and Research, leading the development and implementation of a new labelling policy including Structure Product Labelling.

Lilliam joined the FDA as a Pharmacology/Toxicology Reviewer in the Office of Oncology Drug Products. She holds a PhD in Neuroscience from Rutgers University and a BA in Chemistry from the University of Puerto Rico. She has been with the FDA since 2000.

Crystal Allard Crystal leads the Data Quality Team in the Office of Computational Science (OCS) at the FDA/CDER. She is also the programme manager for all collaborations with the OCS. Crystal is an FDA liaison to the PhUSE Computational Science Steering and Planning Committees, and a co-lead for the Design Thinking project in the Educating for the Future Working Group. Crystal has worked at the FDA since 2008 and at the OCS since 2012.

Our keynote speakers

We are delighted to announce four keynote presentations for CSS 2018 – read on to find out about our speakers’ backgrounds, and what you can expect to hear from them

Clockwise from top left Helena Sviglin; Alan Shapiro; Lilliam Rosario; Crystal Allard

Share and learn at PhUSE SDEsFree to PhUSE members and held globally, our year-round Single Day Events cover a range of topics and provide a chance to network with colleagues...

• Generally scheduled between 9am and 5pm, with lunch and refreshments included

• Includes a brochure with information on speakers and presentations• All presentations are available to view on the PhUSE

website archive – visit phuse.eu/archive

For the full schedule and to register, visit phuse.eu/single-day-events-2018


Around the worldAround the world

U S E V E N T S

U S E V E N T S

US Connect 2018

Welcome to Raleigh

Transformative Current and Emerging Best PracticesRaleigh, NC 3rd-6th June 2018

In preparation for the Connect, here’s a brief introduction to the ‘City of Oaks’

By JIM JOHNSON, US Connect Chair

By CHRIS HURLEY, PhUSE Americas Director

E ARE JUST THREE months away from the inaugural PhUSE US Connect conference! The call for

papers went out on 9th October 2017 and closed on 1st December 2017, and we had a record 265 papers to review. Your Connect Committee has completed the paper review meeting and selected an outstanding set of 135 papers to be presented, along with 30 posters at the opening session on Sunday 3rd June. We are excited about this meeting and all the interest generated by our PhUSE family and community. Abstracts came from 73 different organisations, including many from industry, regulatory agencies, contract research organisations and technology & software organisations. The high quality of the abstracts made the selection of papers very difficult for the Stream Chairs, Co-Chairs and Organizing Committee. The Organizing Committee would like to thank all who submitted abstracts.

The organisation of the PhUSE US Connect will be similar to that of the much-acclaimed European Annual Conference just completed in Edinburgh, UK. The streams for the Connect will include: Standards Implementation; Data Visualization; Management; Data Handling; Regulatory; Trends & Technology; Analytics, Big Data & Statistics; Data Standards & Governance; Coding Tips & Tricks; and a Poster Session. We have added

ERE ARE SOME interesting things to consider about Raleigh, as you ponder your visit.

The city was incorporated in 1792 as the state capital of North Carolina. It is named after Sir Walter Raleigh, who in 1585, established the lost Roanoke Colony in present-day Dare County. What happened to that colony was a mystery – the colonists disappeared after the Anglo-Spanish War, giving this place the Lost Colony moniker.

What didn’t disappear during another war (the American Civil War, over 260 years later) was the city of Raleigh. General Robert E. Lee surrendered his Army of Northern Virginia on April 9th 1865. Four days later, Union general William Tecumseh Sherman marched his army into Raleigh – the forces there surrendered without a shot. In response, Sherman forbade foraging and destruction in the city. Raleigh was the only city in the Carolinas during Sherman’s scorched-earth campaign to avoid intentional destruction – many of the buildings are still standing today.

You can get a flavour of the Greek Revival architectural style during the antebellum years. There are some

a twist to the Connect in that we have received a selection of papers that will be put into a new stream entitled Strategy in Programming. This stream will have papers presented that deal with issues and solutions for managing the challenging work we do by addressing changing skill sets, working practices, and areas of emerging research and technology, while focusing on the soft skills we really need to complete high-quality statistical programming work in the pharmaceutical and biotechnology industries. Check out the Connect stream web page for this and all the streams in the meeting at phuse.eu/us-conference-streams-2018.

We have expanded the Regulatory, Data Standards & Governance, Standards Implementation, and Trends & Technology streams to provide for additional space for diverse presentations by industry and regulatory agency colleagues. These expanded sessions will provide a greater opportunity to dialogue, learn and broaden all our horizons on best and emerging practices for delivering high-quality research data.

Now is the time to register and book your flights and hotel for this inaugural conference. Please visit the Connect web page for more details at phuse.eu/us-connect18. I very much look forward to seeing you all at the PhUSE 2018 US Connect!

wonderful tours of the city where you can walk, ride, or even enjoy your favourite beverage on a rolling pedal-powered pub – which is rated the number one tour in the city. There are plenty of other things you can do in the area, many of which are within walking distance of the hotel and convention centre.

Here are a few facts for travellers visiting for the first time... • The co-ordinates for the Raleigh

Convention Center are 35.7735° N, 78.6414° W.

• The average high temperature on June 3rd in Raleigh is 84°F (29°C).

• Raleigh is considered a focus city for Delta Airlines and has service from many regional airlines.

• In 2016, 11,049,143 people passed through RDU airport – it is very easy to find a flight to Raleigh from virtually everywhere.

• If you want to venture out and experience other places in the Research Triangle, then the best way to travel is by car – you can get from Raleigh to Durham or Chapel Hill in less than an hour.

Raleigh is simply beautiful in June, so take time out to enjoy the attractions and history of this great city.

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“ We’ve added a twist to the Connect – we received a selection of papers that will be put into a new stream” Clockwise from top Downtown Raleigh; the Trolley

Pub on tour; an engraving of Sir Walter Raleigh

12 | PhUSE News | Spring 2018 Spring 2018 | PhUSE News | 132 | PhUSE News | Summer 2017

4th–7th November KAP Europa

Frankfurt, Germany

Chair Katja Glass, Bayer Pharma Co-Chair Michael Harris, Amgen



CONNECT

EU 2018

phuse.eu


E U E V E N T S

PSI 2018

Range of ContentThe PSI Conference will contain a range of plenary, parallel, poster and workshop sessions. This year we have additional parallel sessions and workshops on Monday and Tuesday, so you will have even more choice. We will have a diverse range of parallel sessions this year including Machine Learning, Missing Data, Data Science in Clinical Trials, Interactive Data Review Using Visual Analytics, Data Transparency, and many more. The Conference will finish at 3pm on the 6th, so don’t book your transport home too early! For updates, visit psiweb.org/psi-2018/scientific-programme.

NetworkingConferences are always a great place to catch up with old friends and meet new ones. Or to find that new job opportunity you have not found time to look into, whilst stuck at your desk. There will be plenty of time to mingle in the Exhibitors area during the breaks/lunch, over a drink at the Poster session or while you are dancing the night away at the Gala dinner!

Regulatory/reimbursement presenceWe are always excited with the engagement of different government organisations speaking at PSI, and this year is no exception. This year we will end the Conference with a regulatory townhall, where you will be able to ask any questions you have on any regulatory topic, in person or anonymously.

Poster sessionDue to the success of the Poster Quick Fire Round this year, we will be hosting this again in 2018 as a Plenary Session, directly before the formal Poster Session begins. With more than 50 posters last year, we hope to see as many submissions this year. The deadline for poster abstracts is 28th February 2018. For all the details and to download an abstract template, visit psiweb.org/psi-2018/psi-conference-2018.

Inspirational keynote speakersDr. Nupur Kohli and Dr. Steve Ruberg will focus on what the future holds for us, both in the world of healthcare overall and specifically for us and our role as statisticians. Both these keynote speakers are guaranteed to challenge our current thinking, and put us into an alternate mindset for the rest of the day. Full abstracts and biographies are available at psiweb.org/psi-2018/keynote-speakers.

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Fancy learning something new? Want to know more about statistics in the pharmaceutical industry? Here are five key reasons why you won’t be disappointed if you choose PSI

Around the world

Breaking boundaries in drug developmentAmsterdam, Netherlands 3rd–6th June 2018

If you want to know more about the PSI Conference 2018, please visit the website at psiweb.org, or to register, visit b-com.mci-group.com/registration. The early bird deadline is 21st March 2018. We hope to see you there!


ShanghaiBy PENG WAN, MSD

S D E R E C A P

HE PHUSE SHANGHAI Single Day Event took place on 3rd November at the Novartis campus in Shanghai. The

event was a great success, with over 160 attendees from 23 companies, including global and local pharma, CROs, consulting, and software vendors. The event focused on the understanding and discussion of agency requirements and expectations, submission packs, key deliverables, implementations of TA standards analysis and reporting and other forward-thinking considerations.

Victor Wu, Chair of the China CDISC Coordinating Committee (C3C), provided an overview of data submission requirements history, the current status and a look into the future, the challenges we face and thoughts on how to get ourselves well prepared. Anita Shen, Director of CDM from Janssen China R&D, focused on CFDA-required unique DMP & DMR for China NDA submission as well as how DM leaders and professionals

are positioning themselves in this high-demand market. Eason Yang, Principal Programmer from Novartis, gave a high-level summary of the electronic data submission requirements to health authorities in the US,

EU, Japan and China. In addition, several experienced professionals shared their knowledge and practice in the preparation of the eSub data package, statistical and programming consideration in TA-specific topics particularly in oncology studies, and SAS advanced programming in their daily operation. The day finished with closing remarks and future expectations from Wei Chen, site head of Novartis data science and programming, and Jingwei Gao and Peng Wan, SDE Co-Chairs.

Above SDE Chair Jingwei Gao, addressing the audience Below SDE Chairs and presenters

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DurhamBy KERRI MCCAUL CLAUS, PAREXEL

S D E R E C A P

HIS SDE WAS HOSTED BY PAREXEL on 9th November, 2017 in Durham, NC. Kerri McCaul Claus (PAREXEL) and

Karl Konrad (d-Wise) were the Co-Chairs and put together an exciting and varied agenda. Special thanks to Debbie Johnson, with help from Beth Neece, John Jordan and Leon Smith, who were involved behind the scenes, managing the logistics of the event and ensuring the guests were well taken care of.

The theme for the event was ‘Disruptive Changes in the Clinical Trial Data Handling and Analysis Space’, and we had over 70 attendees.

Jason Martin (PAREXEL) set the tone for the day with a great keynote touching on innovation within the industry and how historical innovations have helped to shape and drive the present and future of our business. Morning sessions followed the keynote and discussed in depth the concepts of linked data, metadata repository (MDR) and optimising technology to drive efficiency and cost savings in the healthcare industry. The morning also included a special presentation from FDA speaker Joe Meehan, who talked about tools and techniques that are being developed to help drive improvement in the regulatory review process.

Afternoon sessions kicked off with an update from Chris Hurley, on behalf of PhUSE, who shared news and upcoming events for the new year, including the 2018 annual conferences. Afternoon speakers continued with our theme for the day, with a focus on linking metadata and CDISC standards, as well as workflow automation as a means of driving efficiency. In addition, a macro tool offering a practical solution to managing text strings relative to regulatory requirements was demonstrated.

Lunch and break sessions throughout the day allowed the opportunity for guests to mingle and network, discussing thoughts and perspectives from speaker presentations, as well as industry topics in general. Blue and orange cupcakes capped off the afternoon and were enjoyed by all in celebration of the year of the 100th PhUSE SDE!

The day concluded with a panel discussion and all the speakers were invited to field questions and comments from guests. Speakers responded to a number of questions and topics, and theorised on potential way-forward practices in our industry. Attendees actively participated and generated some thought-provoking discussions.

Thanks to all who helped to organise and participate in this exciting event!

T“ Guests mingled and networked, discussing thoughts and perspectives from speaker presentations, as well as industry topics in general”

Recaps and reviewsRecaps and reviews


BloemfonteinBy ALEX BRINK, SDE Chair

S D E R E C A P

S THE SUN SET ON A freezing mid-November Bloemfontein day, we were given the opportunity to reflect

on a hugely successful inaugural South Africa SDE. The fascinating topics around our ‘Shifting Pharmaceutical Programming Outside the Box’ theme ensured an engaged audience, and left our attendees looking forward to the next SDE.

Our 84 attendees, including 13 speakers, enjoyed excellent hospitality from Emoya Estate Private Game Reserve, who provided a wonderful lunch and snacks throughout

the day. We were graced with the presence of PhUSE founder, Stephen Bamford, who gave us opening and closing remarks. This inaugural event progressed without any concerns, and enabled colleagues from numerous organisations to meet and network.

Following the event, we hosted an open dinner, which provided further opportunity for attendees to network.

In conclusion, we had special weather for a special day, one that has ignited the PhUSE and left attendees looking forward to the next event.

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Clockwise from above Single Day Event presenters and Chairs; Leon Lombaard & Megan Carnell, IQVIA; SDE Chair Alex Brink and Co-Chair Dianne Weatherall; Stephen Bamford, PhUSE Global Events Director and Alex Brink, SDE Chair; attendees at the Emoya Estate Private Game Reserve

Recaps and reviews


Recaps and reviewsRecaps and reviews

MumbaiBy SUBHASH CHANDRA TADURI, ICON

S D E R E C A P “ The key to a healthy life is balancing between your mind, body and soul”

AMCHI MUMBAI! THE CITY of Mumbai, as it is belovedly called by its inhabitants, played host to the last PhUSE SDE in

India for 2017. On 25th November, the event was attended by 61 participants representing 16 different organisations, with the theme ‘Emerging Trends and Technologies’. A shoutout to Chiltern, Cytel, doLoop Technologies, Domain Clinical, Ephicacy, GCE Solutions, MMS, PPD, IQVIA, Sciformix and Symbiance for being the sponsors and making this event a grand success.

The day’s proceedings began with a welcome note by Sarvesh Singh, Asia Director, PhUSE, who introduced participants to the various roles each one of us can play in the diverse PhUSE Working Groups. It was then time for the first presentation of the day, by Godfrey Machado, Tata Consultancy Services, who spoke avidly on the advancements in precision medicine and how it has and will be shaping up study designs in the future. He went on to explain the importance of pharmacogenomics in clinical trials and provided a glimpse of the PGxIG1.0 SDTM domains. Next, Dr. Anandha Satheesh Chinnamuthu delved into the prospects of artificial intelligence in clinical SAS. He added that computers can be trained in processing large amounts of data and recognising patterns in the data, leading to faster data manipulation and more efficient and cost-effective outputs. Vikas Kumar Singh & Salil Sawant, inVentiv Health, then engrossed the audience by sharing their experience on using a project tracking tool named ‘JIRA’, which uses the Agile development life cycle. We then had Mousum Dutta & Pranam Vaishnav from doLoop Technologies as well as Milan Adesara from Cytel, who spoke in two separate presentations on the benefits of using the SAS Grid system in our clinical projects. Workload balancing, parallel processing and increased data growth are some of the salient features that were explained, with examples, which made it easier for the participants to understand the functionalities of the grid system. Tamilselvi Senthilkumar & Prakash Pichaimuthu from

Symbiance then followed up with their presentation discussing the ‘Smart aCRF Tool’, which, as the name suggests, deals with automating CRF annotation, drastically reducing the time taken from a week or more to just a day, or even a matter of hours. Sowmiya Kumarasamy & Vijayakumar Radhakrishnan, ICON, were next to present

on the topic of Adaptiv designs and the usage of the software ADDPLAN® to devise study designs which provide us with better insight and aid in better decision-making. Bhaskar Subramanian, Chiltern, was next to take to the floor, with his topic ‘Test Data Simulator’. A data simulator uses the protocol-defined data points and creates data fit for use, with all scenarios considered, which supports defensive programming of what-if data conditions.

‘Digital Health and Clinical Trials’ was the next presentation, by Pinakin Jani from IQVIA. In the era of real-world evidence and global availability of technology, it is certainly upon us, as clinical research professionals, to infer the benefits and challenges of using digital health data and devices in clinical trials. An interesting presentation came from Rajaram Venkatesan, Cognizant, entitled ‘Automated Generation of Statistical Reports – Literate Programming Approach’, a methodology that combines a programming language with a documentation language. The last presentation of the day came from Silvester Patil, which revolved around the topic of artificial intelligence being used in SDTM mapping and CSR writing.

After the presentations and a quick tea break, the participants regrouped for the panel discussion. The discussion was headed by Arghya Chattopadhyay, Tata Consultancy Services, Jitesh Nagaria, doLoop Technologies, and Bhaskar Subramanian, Chiltern. The discussions ranged from the OMOP Common Data Model, big data, digital health (collecting data through tears using Google Contact Lens) and the cheaper prices of genome sequencing and how everyone should take advantage of it. The session closed with a note stating, whatever the advancements in clinical research that reach out to patients, the key to a healthy life is always the basic ability of balancing between your mind, body and soul. Along with the presenters receiving their mementoes, we had our very own Jayapandian from Novartis, who was honoured with the Outstanding Contributors Award for his work with PhUSE.

Clockwise from left Presenter Bhaskar Subramanian, Chiltern; panel discussion with (left to right) Bhaskar Subramanian, Jitesh Nagaria, and Arghya Chattopadhyay; Jayapandian N receiving his Outstanding Contributor Award from Sarvesh Singh (left) and Arghya Chattopadhyay; attendees at the Single Day Event

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Tech tipsTech tips

P R O G R A M M I N G

Kirk’s Korner

SAS® Tip Emulating a transpose with SAS hash objects

By KIRK PAUL LAFLER,Software Intelligence Corporation

It’s frequently desirable to restructure a data set from a wide format to a long format, or perhaps a long format to a wide format, for the purpose of avoiding many-to-many merges, performing analysis, and an assortment of other tasks. Traditionally, users resort to restructuring a data set using the TRANSPOSE procedure. But, are there any other approaches for users to use when restructuring a data set? An effective alternative to the TRANSPOSE procedure is to use the SAS® Hash Object. The DATA step hash object code presented in this tip creates both long and wide data set structures from the SASHELP.SHOES data set.

Region Product Subsidiary Stores Sales Inventory Returns1 Africa Boot Addis Ababa 12 $29,761 $191,821 $7692 Africa Men’s casual Addis Ababa 4 $67,242 $118,036 $2,2843 Africa Men’s dress Addis Ababa 7 $76,793 $136,273 $2,4334 Africa Sandal Addis Ababa 10 $62,819 $204,284 $1,8615 Africa Slipper Addis Ababa 14 $68,641 $279,795 $1,7716 Africa Sport shoe Addis Ababa 4 $1,690 $16,634 $797 Africa Women’s casual Addis Ababa 2 $51,541 $98,641 $9408 Africa Women’s dress Addis Ababa 12 $108,942 $311,017 $3,2339 Africa Boot Algiers 21 $21,297 $73,737 $71010 Africa Men’s casual Algiers 4 $63,206 $100,982 $2,22111 Africa Men’s dress Algiers 13 $123,743 $428,575 $3,62112 Africa Sandal Algiers 25 $29,198 $84,447 $1,53013 Africa Slipper Algiers 17 $64,891 $248,198 $1,82314 Africa Sport shoe Algiers 9 $2,617 $9,372 $16815 Africa Women’s dress Algiers 12 $90,648 $266,805 $2,69016 Africa Boot Cairo 20 $4,846 $18,965 $22917 Africa Men’s casual Cairo 25 $360,209 $1,063,251 $9,42418 Africa Men’s dress Cairo 5 $4,051 $45,962 $9719 Africa Sandal Cairo 9 $10,532 $50,430 $59820 Africa Slipper Cairo 9 $13,732 $54,117 $1,21621 Africa Sport shoe Cairo 3 $2,259 $20,815 $4422 Africa Women’s casual Cairo 14 $328,474 $940,851 $10,124

Example data setThe data used in the examples in this paper originate from the SHOES data set in the SASHELP library. The SASHELP.SHOES data set, shown below, consists of 395 observations and seven variables where Region, Product, and Subsidiary are defined as character and Stores, Sales, Inventory, and Returns are defined as numeric.

The DATA step hash objectOne of the more exciting and relevant programming techniques available to SAS users today is the Hash object. Available as a DATA step construct, users are able to construct relatively simple code to perform match-merge and/or join operations. A hash object is a data structure that contains an array of items that are used to map identifying values, known as keys (e.g., Student_ID and Interval), to their associated values (e.g., Stores, Sales, Inventory, or Returns). As implemented, the DATA step Hash object is designed as a DATA step construct and is not available to SAS PROCedures. The behavior of a hash object is similar to that of a SAS array where the columns comprising it can be saved to a SAS table, but at the termination of the DATA step the in-memory hash object and all its contents disappear.

How does a hash object work?A hash object permits table lookup operations to be performed considerably faster than other available methods found in the SAS system. Unlike a DATA step merge or PROC SQL join where the SAS system repeatedly accesses the contents of a table stored on disk to perform table lookup operations, a hash object reads the contents of a table into memory once allowing the SAS system to repeatedly access it as necessary. Since memory-based operations are typically faster than disk-based, users experience faster and more efficient table lookup operations. Although one or more hash tables may be constructed in a single DATA step that reads data into memory, users may experience insufficient memory conditions, preventing larger tables from being processed. To alleviate this kind of issue, users may want to load the smaller tables as hash tables and continue to sequentially process larger tables containing lookup keys.

Hash object methodsADD Add data associated with key to hash object. CHECK Check whether key is stored in hash object. CLEAR Remove all items from a hash object without deleting hash object. DEFINEDATA Define data to be stored in hash object. DEFINEDONE Specify that all key and data definitions are complete. DEFINEKEY Define key variables to the hash object. DELETE Delete the hash or hash iterator object. EQUALS Determine whether two hash objects are equal. FIND Determine whether the key is stored in the hash object. FIND_NEXT The current list item in the key’s multiple item list is set to the next item. FIND_PREV The current list item in the key’s multiple item list is set to the previous item. FIRST Return the first value in the hash object. HAS_NEXT Determine whether another item is available in the current key’s list. HAS_PREV Determine whether a previous item is available in the current key’s list. LAST Return the last value in the hash object. NEXT Return the next value in the hash object. OUTPUT Create one or more data sets containing the data in the hash object. PREV Return the previous value in the hash object. REF Combine the FIND and ADD methods into a single method call. REMOVE Remove the data associated with a key from the hash object. REMOVEDUP Remove the data associated with a key’s current data item from the hash object. REPLACE Replace the data associated with a key with new data. REPLACEDUP Replace data associated with a key’s current data item with new data. SETCUR Specify a starting key item for iteration. SUM Retrieve a summary value for a given key from the hash table and stores the

value to a DATA step variable. SUMDUP Retrieve a summary value for the key’s current data item and stores the value

to a DATA step variable.

About KirkSenior SAS Consultant, Application Developer, Data Scientist, Trainer, and Author, Kirk Paul Lafler has been using SAS since 1979 and is consultant and founder of Software Intelligence Corporation. He is a SAS Certified Professional, application and tool developer, provider of IT consulting services, trainer to SAS users around the world, mentor, and sasCommunity.org emeritus Advisory Board member.

As the author of six books including Google® Search Complete (Odyssey Press. 2014); PROC SQL: Beyond the Basics Using SAS, Second Edition (SAS Press. 2013); and PROC SQL: Beyond the Basics Using SAS (SAS Press. 2004), Kirk has written more than five hundred papers and articles, been an invited speaker and trainer at four hundred-plus SAS International, regional, special-interest, local, and in-house user group conferences, educational forums, and meetings, and is the recipient of 23 ‘best’ contributed paper, hands-on workshop (HOW) and poster awards.

[email protected] linkedin.com/in/kirkpaullafler @sasnerd

Trademark citations: SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies.


Tech tipsTech tips

**************************************************************************** ;* CREATE LONG AND WIDE TRANSPOSED SHOES DATA SET FROM SASHELP.SHOES * ;**************************************************************************** ;data hash_long_shoes(keep=student_id interval name label amount) hash_wide_shoes(keep=student_id sales_1-sales_3 inventory_1-inventory_3 returns_1-returns_3) ; length student_id 4. interval 3. Name $9. Label $40. Amount 8. ; format Amount sales_1 inventory_1 returns_1 sales_2 inventory_2 returns_2 sales_3 inventory_3 returns_3 dollar12.0 ;

label sales_1 = "Interval 1 Total Sales" sales_2 = "Interval 2 Total Sales" sales_3 = "Interval 3 Total Sales" inventory_1 = "Interval 1 Total Inventory" inventory_2 = "Interval 2 Total Inventory" inventory_3 = "Interval 3 Total Inventory" returns_1 = "Interval 1 Total Returns" returns_2 = "Interval 2 Total Returns" returns_3 = "Interval 3 Total Returns" ; if 0 then set work.shoes ; 3 if _n_ = 1 then do ; 4 declare Hash Hash_shoes(dataset:'work.shoes', hashexp:10, ordered:'ascending') ; declare Hiter Hi_shoes ('Hash_shoes') ; Hash_shoes.DefineKey ('student_id','interval') ; Hash_shoes.DefineData (ALL:'Yes') ; Hash_shoes.DefineDone () ; end ; rc = Hi_shoes.first() ; 5 do while (rc = 0) ; 6 link outlong ; 7 rc = Hi_shoes.next() ; 8 end ; rc = Hi_shoes.first() ; 5 do while (rc = 0) ; 6 link outwide ; 7 rc = Hi_shoes.next() ; 8 end ; stop ; 9return ;outlong: ; 7 Name = 'Sales' ; Label = 'Total Sales' ; Amount = sales ; output hash_long_shoes ; Name = 'Inventory' ; Label = 'Total Inventory' ; Amount = inventory ; output hash_long_shoes ; Name = 'Returns' ; Label = 'Total Returns' ; Amount = returns ; output hash_long_shoes ;return ;outwide: ; 7 Sales_1 = sales ; Inventory_1 = inventory ; Returns_1 = returns ; Sales_2 = sales ; Inventory_2 = inventory ; Returns_2 = returns ; Sales_3 = sales ; Inventory_3 = inventory ; Returns_3 = returns ; if interval = 1 then output hash_wide_shoes ;return ;run ;

Code to create transposed data sets with the DATA step HASH objectAs with the code to create transposed data sets with the TRANSPOSE procedure presented earlier, we first show code that is used to streamline the SASHELP.SHOES data set structure by reducing the number of variables to two user-defined numeric variables (STUDENT_ID 1 and INTERVAL 2) and three existing variables (SALES, INVENTORY, and RETURNS).

In the next step a long (or thin) and a wide (or fat) data set structure is created using DATA step Hash methods and coding techniques to emulate the TRANSPOSE procedure results.

data work.shoes ; length student_id 4. interval 3. ; set sashelp.shoes(drop=region product subsidiary stores) ; student_id = _n_; 1 do interval = 1 to 3 ; 2 output ; end ;run ;

‘IF 0 THEN SET WORK.SHOES’ tells SAS to load variable properties into the hash object located in real memory. 3 The DO-group uses a DECLARE HASH statement to provide a name to the hash object in memory as ‘Hash_shoes’, the name of the data set, the internal size of the hash object (maximum value is 20), and how the data is ordered. The “DECLARE HITER” statement defines and initializes the hash object. The DEFINEKEY method identifies the variable (or variables) to use as the key. The DEFINEDATA method informs SAS what variables to read into the hash object in memory (in our case all variables not removed with the DROP= (or KEEP=) data set option). The DEFINEDONE method completes the hash table definition. 4

The FIRST() method tells SAS to return the first value stored in the defined hash object. 5 The DO WHILE loop iterates repeatedly as long as there is data stored in the hash object. 6 The LINK OUTLONG statement tells SAS to execute the OUTLONG subroutine and the LINK OUTWIDE statement tells SAS to execute the OUTWIDE subroutine. 7 The NEXT() method tells SAS to return the next value from the hash object in residing in memory. 8 The STOP statement tells SAS to terminate the DATA step. 9

The resulting HASH_LONG_SHOES data set is displayed in Figure 2. The columns have been transposed to observations creating a narrow (or thin) data set structure with 3,555 observations and five variables. Because of the thinness of this data set structure, the amount of computing resources (i.e., CPU, memory, and elapsed time) expended during processing is often much less than processing with wider (or fatter) data set structures.

The resulting transposed data set, HASH_WIDE_SHOES, as displayed below, represents a wider (or fatter) data structure with more variables, but fewer observations (reduced from 3,555 observations to 395 observations and ten variables). It contains one observation per BY-group (i.e., STUDENT_ID) with three interval columns for: SALES, INVENTORY, and RETURNS.

Student_id Interval Name Label Amount1 1 1 Sales Total sales $29,7612 1 1 Inventory Total inventory $191,8213 1 1 Returns Total returns $7694 1 2 Sales Total sales $29,7615 1 2 Inventory Total inventory $191,8216 1 2 Returns Total returns $7697 1 3 Sales Total sales $29,7618 1 3 Inventory Total inventory $191,8219 1 3 Returns Total returns $76910 2 1 Sales Total sales $67,24211 2 1 Inventory Total inventory $118,03612 2 1 Returns Total returns $2,28413 2 2 Sales Total sales $67,24214 2 2 Inventory Total inventory $118,03615 2 2 Returns Total returns $2,28416 2 3 Sales Total sales $67,24217 2 3 Inventory Total inventory $118,03618 2 3 Returns Total returns $2,28419 3 1 Sales Total sales $76,79320 3 1 Inventory Total inventory $136,27321 3 1 Returns Total returns $2,43322 3 2 Sales Total sales $76,793

Student_id Interval 1 Interval 1 Interval 1 Interval 2 Interval 2 Interval 2 Interval 3 Interval 3 Interval 3 sales inventory returns sales inventory returns sales inventory returns1 1 $29,761 $191,821 $769 $29,761 $191,821 $769 $29,761 $191,821 $7692 2 $67,242 $118,036 $2,284 $67,242 $118,036 $2,284 $67,242 $118,036 $2,2843 3 $76,793 $136,273 $2,433 $76,793 $136,273 $2,433 $76,793 $136,273 $2,4334 4 $62,819 $204,284 $1,861 $62,819 $204,284 $1,861 $62,819 $204,284 $1,8615 5 $68,641 $279,795 $1,771 $68,641 $279,795 $1,771 $68,641 $279,795 $1,7716 6 $1,690 $16,634 $79 $1,690 $16,634 $79 $1,690 $16,634 $797 7 $51,541 $98,641 $940 $51,541 $98,641 $940 $51,541 $98,641 $9408 8 $108,942 $311,017 $3,233 $108,942 $311,017 $3,233 $108,942 $311,017 $3,2339 9 $21,297 $73,737 $710 $21,297 $73,737 $710 $21,297 $73,737 $71010 10 $63,206 $100,982 $2,221 $63,206 $100,982 $2,221 $63,206 $100,982 $2,22111 11 $123,743 $428,575 $3,621 $123,743 $428,575 $3,621 $123,743 $428,575 $3,62112 12 $29,198 $84,447 $1,530 $29,198 $84,447 $1,530 $29,198 $84,447 $1,53013 13 $64,891 $248,198 $1,823 $64,891 $248,198 $1,823 $64,891 $248,198 $1,82314 14 $2,617 $9,372 $168 $2,617 $9,372 $168 $2,617 $9,372 $16815 15 $90,648 $266,805 $2,690 $90,648 $266,805 $2,690 $90,648 $266,805 $2,69016 16 $4,846 $18,965 $229 $4,846 $18,965 $229 $4,846 $18,965 $22917 17 $360,209 $1,063,251 $9,424 $360,209 $1,063,251 $9,424 $360,209 $1,063,251 $9,42418 18 $4,051 $45,962 $97 $4,051 $45,962 $97 $4,051 $45,962 $9719 19 $10,532 $50,430 $598 $10,532 $50,430 $598 $10,532 $50,430 $59820 20 $13,732 $54,117 $1,216 $13,732 $54,117 $1,216 $13,732 $54,117 $1,21621 21 $2,259 $20,815 $44 $2,259 $20,815 $44 $2,259 $20,815 $4422 22 $328,474 $940,851 $10,124 $328,474 $940,851 $10,124 $328,474 $940,851 $10,124


Case studyCase study

IGURES PLAY A KEY ROLE IN EXPLAINING OUR DATA. Consider a situation where it is important to understand the relationship between two variables, X and Y, over time. A simple scatter plot to demonstrate the X vs Y relationship might help us out only for a particular time frame. How

can we see the changes in relationship over time? In this case, when a statistician and programmer try to find the tipping point (a particular penalty on comparator for which a p-value gets tipped), it is really important to visualise how that particular point is achieved in steps. The GIF (Graphics Interchange Format) is one such format that we may use to manage this kind of challenge, based on its properties: • a lossless compression with up to 8 bits per pixel,• a single image to refer to its own palette of up to 256 different colours,• supports animation.

Tipping point analysis It is usual to define three kinds of missing data:• Missing Completely At Random (MCAR): Missingness doesn’t depend on

observed/unobserved data,• Missing At Random (MAR): Missingness doesn’t depend on unobserved data,• Missing Not At Random (MNAR): Missingness depends on unobserved data.

Of the different types of missingness mechanism, Missing Not at Random (MNAR) becomes more challenging. In those situations, generally running a sensitivity analysis by introducing increasing penalties after performing Multiple Imputation (MI) and identifying the tipping point for which we have insignificant p-value is important.

A clinical SAS programmer mainly needs to pay attention when he/she finds an insignificant p value with respect to comparator drug. Then, a statistician’s approach must be to check if this is because of missingness in actual treatment arm. Hence, the concept of analysing missing data comes into picture and, consequently, the impact of adding penalty and change in p-value becomes important.

A case study on a simulated data is performed and, based on the same, the GIF-based visualisation is applied.

Let’s try this: create GIF file %macro crtgif; Count loops to run %do i = 1 %to &lp; subset the data for this particular loop read necessary macro variables such as penalty and p value to print

proc template; Write proc template code conditions to text attributes within graph drawtext textattrs=(size=12pt weight=bold %if &pval > 0.05 %then %do;color=black %end; %else %do; color=lightgrey %end;) Automatically changing attribute at tipped point "Penalty = &pen p-value = &pval"/ width=50 anchor=top borderattrs=(color=black) justify=center border=true x=70 y=96; end;run; Render the above template %end; ...... Loops ends %mend; ..... Macro ends

options printerpath=gif animation=start animduration=1 animloop=yes noanimoverlay nodate nonumber; ods printer file = “location/XXX.gif”; %crtgif; options printerpath=gif animation=stop; ods printer close;

F

P R O G R A M M I N G

Next gen data vis via GIF

A case study on sensitivity analysis of MI

By ANIK CHATTERJEE, Novartis, India

Tipping point infographic

Hence, it would be ideal if we had a small movie clip or animation that would help us in understanding how to come up with a decision based on observations from the data. Also, if we have the facility to refer the same a few years down the line, it would just take a minute to understand the whole story courtesy of the GIF clip.

Usual way: the panelled graph

Identification of MAR (PI + Act)

Imputation using MCMC

Is tipping observed?

YES NO

Getting estimateusing MAR (PI)

Derive p-value

Increase penalty

Identification of Act MNAR

Imputation assuming jumpto reference MNAR (Act)

Assign penalty

Pl – PlaceboAct – ActiveMCMC – Markov chain Monte Carlo

Checking p-values for increased penalties.The p-value tipped up at penalty = 1.45(Highlighted)


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