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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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my background
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
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 i dea but
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 s cience i mportant
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
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
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
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
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
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