<img alt="" src="https://secure.lote1otto.com/219869.png" style="display:none;">
Skip to content

5 Skills Needed by All Highly Effective Statisticians

The Head of the DIA’s Adaptive Design Working Group Asks Us to Consider 5 ‘Soft-Skills’ All Effective Statisticians Should Cultivate


The advent of adaptive designs has meant that statisticians have a new role to play in the drug development process. Not only are they responsible for tackling the precise statistical issues that can arise during the course of a study, but their knowledge and vision can result in innovations that substantially alter study design, strategic decision-making, probability of trial success and level of revenue.  

Given this new role played by statisticians, what skills do statisticians now need to cultivate to be as effective as possible in the boardroom and on their project teams?

Earlier this year, Zoran Antonijevic, the chair of the DIA’s Working Group on Adaptive Design gave a talk on the future of adaptive clinical trials. During this talk he highlighted five soft-skills that statisticians can develop to contribute to the broader objectives of a trial. While commending statisticians for their focus and determination in statistical analysis and problem-solving, Zoran encouraged them to take a wider view of their responsibilities towards a clinical trial's goals.

Zoran argued that statisticians wanting to make the transition from statistical problem-solver to strategic team member should consider cultivating the following skills.

Communication: Young statisticians spend years learning a very technical language in which lots of information is summarized in very few words. This makes communication amongst statisticians rather efficient. Concepts which capture complex ideas that everyone understands, ultimately result in short, crisp, direct sentences. However, explaining these same insights to the non-statisticians (i.e. in plain English), will take more time while also requiring more precision. Words that can be thrown about imprecisely amongst a group of statisticians sometimes demand greater explanation when speaking with a non-statistician. Learning to communicate statistical concepts to the non-statistician might be a frustrating challenge, but it means that statistical insights will eventually play a more vital role in the strategic decision-making which occurs during a trial.

Teamwork: The image of the lone statistician working on a clinical study team is a common trope within the biopharmaceutical industry. Such a vision often relies on an outdated idea of what role the statistician is meant to play. More and more, study leaders, feasibility planners and financial decision-makers are relying on statistical guidance to plan and employ the best strategy for a trial. Cultivating responsibility to a clinical trial at large means being accountable not just for solving a narrow range of highly technical problems, but being accountable to the team as well .

Influence: What objectives are statisticians angling to obtain and how does this resonate with the objectives of the decision-makers on a study? Do you feel confident approaching your CMO or CFO with insights into how to improve the trial? Do they trust and respect your judgment? How do they respond? Taking a more strategic role means taking the time to cultivate trust, accountability and influence.

Confidence: Zoran points out that leading on from teamwork and influence, some statisticians might struggle with a lack of confidence when approaching decision-makers. Sticking to narrow statistical problem solving makes a statistician feel like an expert, while working on a team with CMOs, CFOs and others raises the risk of being the non-expert in the room. On the flip side, statisticians often bring a unique expertise leading others to recognize and expect more from them. Barriers to confidence can be crucial in leaving the savvy statistician behind.  

Goals: Driving a clinical trial to success means giving a new drug the best chance it has to prove its worth. Both pre-trial planning and interim decision-making require statisticians to take on new responsibilities. Zoran encourages enlarging the vision and expectation of what statisticians hope to achieve. Yes, statisticians will still be responsible for much of the technical statistical problems that arise in a trial. However, there is no need to limit their vision to this.

Liked this article ?  Join our global audience of biopharmaceutical innovators and click the button below to receive Cytel blog notifications direct to your inbox ( choose from instant or weekly notifications).


Related Items of Interest

Seamless Adaptive Clinical Trials: Now that we get the statistics, what’s really at stake?

Relative Clinical Efficiency and Phase 2 Biomarker Studies

Monte Carlo Simulations II: Reassessing Strategic Options During an Interim Look

 Data-Driven Trial Planning: An Interview with Pfizer's Chris Conklin

contact iconSubscribe back to top