Miscommunication Among Data Managers, Biostatisticians, and Programmers is Common, but Avoidable

Miscommunication is Common, but Avoidable

The quality of pharmaceutical research depends on many things, not least the biometrics of the study — that holy trinity of clinical data capture, manipulation, and reporting. When the interplay among those three functions goes smoothly, it’s a little like watching the graceful execution of a relay race. Communication is smooth; the baton handoffs are flawless; and each runner plays a different, yet critical, role in the journey to the finish line: the delivery of a clear, concise, and compelling New Drug Application to the FDA.

But too often the reality is quite different. The runners — the data managers, the biostatisticians, and the programmers— may not function as a well-oiled team and there are good reasons for that. Although they all have some familiarity with each other’s worlds, they come from very different backgrounds. They’re educated and trained differently and communicate in jargon that may be unfamiliar to one another. And in some companies, the three functions come under different organizational structures, exacerbating the disconnects.

Miscommunication Pitfalls

It’s no surprise that miscommunications often occur, resulting in lost time, needless spending, and lots of rework. And as drug trials have become increasingly complex and the race to market ever swifter, the smooth operation of the biometrics function is absolutely critical.

Here is just a sampling of some of the most common ways these communication breakdowns occur:

  • The statistician lives and breathes numbers; the data manager does not. Unfortunately, statisticians are not typically trained to communicate complex mathematical concepts to non-statisticians. Yet in order to make sure the data manager is capturing the right data points, the communication needs to be clear, jargon-less, and detailed. That being said, miscommunication is often a two-way street and data managers have to shoulder some of the responsibility. The best data managers are highly inquisitive: they ask the pointed questions that elicit the critical specifics from statisticians about their data needs.
  • When data is extracted from the electronic data capture (EDC) system and sent to the SAS programmers for analysis, it’s not uncommon for errors to occur.  The data may be in a format that’s unusable, key variables may be  mislabeled, or vital data points like the time samples were taken have gone missing. This typically occurs when programmers haven’t been consulted during the creation of the EDC, and the system hasn’t taken the needs of the programming function into account.
  • Expect the unexpected when it comes to clinical data: missing data points, protocol deviations, and incorrect data values. Without early and continual communication among the data managers, statisticians, and programmers about how to handle unclean data, lots of rework will be required. Organizations need a good remediation plan from the get-go.

Communication Best Practices

How does an organization avoid these communication pitfalls? Treximo’s clinical data science group has seen it all, and then some. Here are a few of the best practices we’ve developed during our years of working with sponsors:

  • Communication among data managers, statisticians, and programmers must occur early in the study and be continual. It’s incumbent on each to keep the others apprised of their needs in order to avoid incorrect assumptions that result in costly rework.
  • Keep the three functions in one division. If they all report to the same person, that person has a clear understanding of the data vision, the interplay among the three functions, and each person’s pain points. The supervisor can flag issues early and is able to easily facilitate communication among the three. This straightforward reporting structure improves accountability immensely.
  • Train all three team members to keep it simple: every meeting we conduct begins with the goal of the meeting, for example, the design of the EDC tool. Then each person states clearly what he or she needs to accomplish that goal. The side benefit of enforcing clarity is that it enables the data managers, statisticians, and programmers to learn a lot about the other person’s function, which makes them better at their own jobs.

Miscommunication happens to even the best of teams. It takes thoughtful planning to recognize where these breakdowns might happen and implement best practices to safeguard against them.  Once things start clicking, your NDA will be off to the races.


Robert Rachford
Senior Director, Clinical Data Sciences