Understanding the Science in Collaborative Research

David M. Vock, Ph.D.

My Background

• Third-year at University of Minnesota• Worked on a variety of applications

including hepatitis C, lung transplantation, heart failure, tobacco cessation, Alzheimer’s disease, primary prevention of CVD, influenza

What Does “Understanding the Science” Entail

• Should be able to give an “elevator talk” to another subject area expert

• Know major objectives • Understand protocol for data collection • Read the major recent papers• Comprehend how study fits within the

larger research agenda of discipline

Not a Revolutionary Idea, But . . .

• Academic departments teach a certain set of skills amenable to solving varied problems

• “Real-world” problems usually require lots of tools to solve them interdisciplinary teams

• Too often statisticians think of themselves as separate from the team

Why is Understanding Science Important?

• Builds credibility with investigators• Improve the research agenda• Guide appropriate analysis• Strengthen manuscript for publication and

anticipate problems with review• Troubleshoot problems

Builds Credibility• Statisticians too-often viewed as another

hoop in research process• To be part of interdisciplinary team have to be

able to speak common language• Stats not universally known: must learn

scientific language and thought process• Forthcoming: value to the team is increased

by understanding science• Think of yourself as scientist with purview

over entire research process

Improve Research Agenda

• If you know the science . . .• Focus research question – no fishing

expeditions• Help prioritize scientific hypotheses• Ensure that the question can be answered

from the data collected

Guide appropriate analysis • Anticipate appropriate confounders to

account for • Prediction versus estimations problem• Avoid analyses not scientifically interesting• Move from associational analyses to causal

treatment analyses• Not going to “win” every disagreement, want

to fight hardest for those points that will affect scientific conclusions

Anticipate Problems in Review

• Extreme resistance to “different” analytical methods

• Must be able to justify departures from standard analysis

• Statistical articles written in medical journals are immensely valuable

• Want to ensure that subject-area conclusions match analysis performed (cannot be too speculative, either)

Troubleshoot Problems• Example: quality of life (QOL) study part of

VALGAN trial• Pre-specified secondary analysis of a

randomized trial of CMV prophylaxis for lung transplant recipients

• Goal was to characterize QOL changes over first year post-transplant using SF-36

• Preliminary analyses showed extremely small gain in QOL even in physical domains

Questions or Comments