Methods for multiple treatment comparisons – Annual Meeting Short Course

Half-day course: Sept 30, 2015, 12:00 -4:00 pm

Target audience: MS- and PhD-level quantitative researchers

Registration is now open – Course is FREE to all


Laura Hatfield, PhD
Department of Health Care Policy – Harvard Medical School

Sherri Rose, PhD
Department of Health Care Policy – Harvard Medical School


Randomized 2-arms trials remain the primary source of information establishing medical device effectiveness. Yet in many clinical settings, clinicians and patients choose among multiple possible medical devices. Comparative effectiveness research addresses this gap. To use observational data, we apply causal inference techniques to address confounding. In this course, we present modern statistical techniques that produce consistent evidence to support clinical decision making from among multiple medical device treatment options.

Learning objectives

At the end of the course, participants will be able to

  1. Describe a clinical decision process in terms of the applicable patient population, set of treatment options, treatment assignment mechanism (including all relevant confounders, both observed and unobserved), and clinical outcomes
  2. Identify inferential targets relevant to the desired clinical decision process
  3. Specify causal and statistical assumptions required for the inferential target to be valid
  4. Choose an estimation method that is feasible given the available data, decision process, and required assumptions


  • Essential methods elements of clinical decision problems
  • Two clinical examples
    • claims data for implantable cardiac devices
    • registry data for stents
  • Inferential targets
    • all pairwise comparisons (device effect on “treated” or average device effect)
    • posterior rankings of device effects
    • marginal structural model
  • Causal and statistical assumptions
  • Estimation methods
    • balancing scores
    • device effect estimates

Instructor Bios

Laura Hatfield, PhD is an Assistant Professor of Health Care Policy, with a specialty in Biostatistics. Dr. Hatfield received her BS in genetics from Iowa State University and her PhD in biostatistics from the University of Minnesota. Her research focuses on trade-offs and relationships among health outcomes. In particular, she develops and applies statistical methods that incorporate multiple sources of information and relationships among outcomes. Dr. Hatfield has particular expertise in Bayesian hierarchical modeling and has taught short courses in industry, government, and professional society meetings.

Sherri Rose, PhD is an Assistant Professor in the Department of Health Care Policy at Harvard Medical School. Dr. Rose received her BS in statistics from The George Washington University and her PhD in biostatistics from the University of California, Berkeley. Broadly, Dr. Rose’s methodological research focuses on nonparametric estimation in causal inference and machine learning for prediction.  Within health policy, Dr. Rose works on risk adjustment, health care program impact evaluation, and comparative effectiveness research. Dr. Rose has taught short courses on robust estimation in causal inference and comparative effectiveness for varied audiences.

Methodology Forum March 4, 2015

March 4, 2015 (2:00-4:00pm EST)

Meeting Contact:


2:00 – 2:15 Objectives/Logistics

2:15 – 2:30 Introductions

2:30 – 2:45 Case Study 1: Matthew Brennan (Duke)

2:45 – 3:00 Discussion

3:00 – 3:15 Methods Study: Laura Hatfield (Harvard)

3:15 – 3:30 Discussion

3:30 – 3:45 Next Steps


  • Forum meetings planned on a quarterly basis
  • Summary of meeting posted on public website
  • Form collaborations
  • What can this group do:
    • Write white papers/participate in public forms
    • Identify common problems and propose solutions
      • Prioritize gaps in key methodological areas
    • 3 problems identified by Dr. Brennan:
      • multiple treatments,
      • missing data,
      • multiple comparisons
    • Dr. Hatfield:
      • Learning curve issues: how handled in post-market setting?
      • Make better use of realistic loss functions (enumerate actions that industry may face, that patients may face, that a regulatory agency may face)