Quantifying the Value of Research: Identifying and Measuring Treatment Effect Modifiers

• Activity Number: 162 - SPEED: Government Statistics, Health Policy, and Marketing
• Type: Contributed
• Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Katherine Lofgren* and Daniel Kramer and Joshua Salomon and Laura Anne Hatfield
• Companies: Harvard University and Beth Israel Deaconess Medical Center and Harvard T.H. Chan School of Public Health and Harvard Medical School
• Keywords: value of information ; effect modification ; proxy effect modifier ; clinical effect modifier ; heart failure ; simulation

Abstract

Clinicians use patient specific characteristics to guide treatment choice towards the option with greatest expected benefit. Factors that influence the expected outcomes of treatment are called effect modifiers. The body of evidence for proxies, like sex, will be larger, with evidence from randomized control trials (RCTs), meta-analyses, and observational studies. The effect of clinical traits will be less precisely known, with few studies powered to report clinical sub-population treatment effects. Using RCT data on multiple treatment options for heart failure patients, we will apply value of sample information methods to quantify the costs and benefits of conducting additional research to precisely and accurately estimate clinical effect modifiers previously identified through biased or imprecise sources. We will generalize our findings using simulation models to explore different underlying correlations between clinical traits and their proxies. This research studies the tradeoffs between relying on an imperfect proxy which may mask important within-group clinical variation, with the additional effort of identifying and accurately measuring clinical traits.