Understanding the Science in Collaborative Research

1. Understanding the Science in Collaborative Research David M. Vock, Ph.D.

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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)

10. 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

11. Questions or Comments