Understanding the science in collaborative research
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Understanding the Science in Collaborative Research. David M. Vock, Ph.D. My Background. Third -year at University of Minnesota

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Understanding the Science in Collaborative Research

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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

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