1. The Analyst Mindset in Statistical Programming
Ross Farrugia & Ryan Copping,
Roche Products Ltd

2. We’d like to start by showing the results of a short survey
We’d like to start by showing the results of a short survey. We asked 100 statisticians, data managers and clinical scientists to give us an artistic impression of the statistical programmer. The results were unanimous…

4. However, we all know that we can project the image of…

6. Agenda Introduction What do we mean by the “Analyst Mindset”?
Key Skills
Example Behaviours
How can this help us?
Conclusions
Questions

7. Introduction (1)
Nowadays in the industry Statistical Programmers are being asked to produce deliverables to tighter timelines for reduced cost with the same accuracy
At Roche, we had the added challenge of our Biostatistics department splitting into two separate global functions - Biostatistics and Statistical Programming
This gave the Statistical Programming group their own identity with an increased responsibility to manage, prioritize, negotiate and deliver
Programming teams have had to evolve to meet these demands and become an effective and innovative partner – Stepping out of our comfort zone!

8. Introduction (2)
We identified key behaviours and skills to encourage across the department to ensure programmers are not just a service provider but an equal partner to achieve the challenges of the project
We’ve labelled these skills and behaviours as the “analyst mindset”, and re-named our function as SPA (Statistical Programming & Analysis)
Initiative communicated and rolled out globally this year (taking into account cultural differences)

9. What do we want you to get out of this talk?
Real life examples of where the “analyst mindset” has been successfully implemented
Potential benefits for you and your drug projects:
Managers – Skills and competencies identified which should be looked for in interviewees and developed in current staff to ensure there is an element of these skills within each programming team
Programmers – Examples of how these skills can be applied to achieve the most efficient use of your time and resource, whilst also maximising your benefit to the drug projects
optimize the time we have and how we can become active contributing participants on the drug project

10. Agenda Introduction What do we mean by the “Analyst Mindset”?
Key Skills
Example Behaviours
How can this help us?
Conclusions
Questions

11. Key Skills
The “Analyst Mindset” involves a balance of many skills, but here are 4 we believe particularly important:
Communication
Decision Making
Planning
Problem Solving

12. Example Behaviours
See the "big picture" and understand our partners' perspectives: Viewing our business from various vantage points can put the best solutions into focus.
Understand the protocol, the science and the scope of the study work. What is the study team trying to achieve? Where will our analyses be used?
Benefits:
Programming accuracy, efficiency and decision making will be improved with greater understanding of the required deliverable
1.By being involved in early discussions between Stats and Science, I get understanding of the requirements, and then when come to programming results in less QC iterations or failures during Stats review or when Science see the output
2.Data management come to us asking for which validation checks, knowledge of the protocol helps us guide them to the key data points, etc. ****************

13. Example Behaviours Influence without authority: Use diplomacy.
Build strong relationships with your partners to be able to call on later to reach a consensus and an agreed solution
Benefits:
Open communication with key stakeholders can give the programming team a voice
By earning the respect of your drug project team and raising the profile of the programming team a trust will be built, so our recommendations are received with more confidence and hence more likely to be taken on board
Being honest and clear about programming resources available can help Clinical Science and Statistics to focus on the most important deliverables
Stats involved with the controversial new process
Educated Science on how extra deliverables would be produced and how long they would take (explaining complexities in non technical language), and explained knock on impacts that it would push other timelines out and then they decided the extra outputs weren’t so necessary.
Also we know the data better, another example where large analyses was needed and after we checked the data we realized there was going to be insufficient data the time AFTER WE EXTRAPOLATED THE DATA – didn’t justify the extra cost of the analyses – science then agreed. **********

14. Example Behaviours
Respectfully disagree and accept disagreement: Never personalize opposing viewpoints - different ideas are encouraged and can lead to better solutions.
Give freedom for people to express themselves within your working teams, whilst ensuring ideas are met with constructive feedback, and explain any disagreement with clear rationale
Benefits:
With so many collaborations that the programming team face it is inevitable disagreements will be seen. Good communication and negotiation skills should ensure that agreements can be established whilst still maintaining the respect of your collaborators
Work with an external company in the study, where they had access to our data but requested all analysis datasets be delivered in a totally different structure to how we had used. This would have caused a huge re-work so we sat down and educated on the benefits of having the datasets as they are. They did agree but also from them we picked up some ideas of how we could structure our datasets differently in future.

15. Example Behaviours
Negotiate for the "best" outcome: The best business outcome may not be "win-win".
Strive to look ahead for the most benefit of the drug project, even if this may not be the best outcome for your programming team
Benefits:
Although at the forefront of our minds is how can we ensure we achieve our deadlines as efficiently as possible, we should always keep the needs of the drug project in perspective. Maybe not in the short-term but this will lead to a better long-term outcome
Extra outputs may be needed for the filing. Sometimes it is better to accept this up front rather than receive these later in the day as possible FDA questions for example.
Analogy – cheap meal vs specials
Reduced VADs vs what may be needed at a later stage ************

16. Example Behaviours
Challenge without offending: Focus on the facts and objectively debate.
Use your programming knowledge and understanding of the study to encourage debate and ensure the key study objectives can be achieved as efficiently as possible, without damaging partnerships
Benefits:
By not just simply accepting work is required as requested, and taking the time to question requirements often this may result in a reduction of the deliverables asked for or an alternative more efficient method
Scatter plot example - By showing a different way of visualizing the data we were able to show that there was no pattern and many extra analyses were then no longer required.
Within Roche we have standard reporting macros. We often take the time to examine templates requested and see if we can persuade Stats to re-work the outputs so that they fit with the tools we have available. Or maybe we have previous programs that can be re-used. This proactive challenging can substantially reduce programming time.

17. Example Behaviours
Be curious & ask questions: Only after we fully understand a problem can we find a solution.
Ensure you fully understand the purpose behind the work we do. What is the rationale behind the analyses and what are they being used for?
Benefits:
We are able to question if the requested analyses does answer the original question in the best and most efficient and effective way
Suggest alternatives – MedDRA AEGT example, where many outputs combined to one as they only actually needed one count from each individual repeated table.

18. Example Behaviours
Encourage different ideas: Think about problems from all angles.
Be innovative and think outside the box!
Benefits:
By taking a second to think about what has been requested sometimes we can think of a more long-term and cost-effective approach, rather than just rushing straight into programming
I sometimes question to Science can the information you’ve requested be sourced from Table A and Table B. Often this can negate the need for an extra output.
If the output is needed, then think where will it be needed again in the future. On my project we’ve implemented project level standards and project level macros for reporting, so by planning up front we’ve been able to increase future re-usability of our work.

19. Example Behaviours
Understand and manage appropriate risk: Consider the probabilities and impacts of errors and strike the right balance.
Make the most efficient use of your time. What are my key deliverables? Where can I prioritize my work and where would a risk-based strategy be beneficial?
Benefits:
With good knowledge of the study and requirements we can risk manage to make the best use of our time, and avoid unnecessary time spent on elements of the analyses which are not focal
Decision making – Assign the most experienced programmers on your high risk work
QC strategy
Risk on prioritizing work

20. Summary of Behaviours
See the "big picture" and understand our partners' perspectives
Influence without authority
Respectfully disagree and accept disagreement
Negotiate for the "best" outcome
Challenge without offending
Be curious & ask questions
Encourage different ideas
Understand and manage appropriate risk

21. Agenda Introduction What do we mean by the “Analyst Mindset”?
Key Skills
Example Behaviours
How can this help us?
Conclusions
Questions

22. Conclusions
Implementing the “Analyst Mindset” can help ensure we make the most efficient use of time and resource, not only for us but for the sake of the drug projects
Recommend to have an element of these skills and behaviours across your programming teams whilst still maintaining a highly technical skillset
We hope the examples and ideas of how these skills have been applied can be taken back and implemented where possible
Next steps for Roche include additional training and development plus knowledge sharing to promote the initiative and encourage implementation
Training for programmers by the programmers (4 skills)….. Tracking examples…….. Being rolled out this year, next year we’ll build examples. Learn from cultural differences

23. Agenda Any Questions???? Introduction
What do we mean by the “Analyst Mindset”?
Key Skills
Example Behaviours
How can this help us?
Conclusions
Questions
Q: Don’t you think everyone is already doing this?
Q: Which skill is most important?
Q: Is technical skill still important?
Any Questions????