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Description
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Session Details
PM10: Introduction to Matching-Adjusted Indirect Comparisons: Leveraging Individual Participant Data
(
Event:
SMDM 40th Annual Meeting: Montreal, QC, Canada)
Oct 14, 2018 2:00PM - Oct 14, 2018 5:30PM
Session Type:
Short Course- PM 1/2 Day
Description
Background
Reimbursement and HTA bodies often require evidence on comparative efficacy between treatments that may not have been compared in head-to-head clinical trials. In sparse networks of treatments, indirect comparisons using aggregate data may not be feasible due to lack of a shared comparator or because trials differ on important patient characteristics. MAIC allows analysts to leverage individual participant data to match characteristics to aggregate data from a competitor’s treatment. Such individual participant data is often available to pharmaceutical manufacturers, and can also be requested by research groups through sources such as The Yale University Open Data Access (YODA) Project. In 2017, the National Institutes for Health and Care Excellence (NICE) decision support unit published guidelines for the conduct of MAICs for both the anchored (a common comparator exists) and unanchored (no common comparator) scenarios. In this workshop, participants will work through simulated examples of anchored and unanchored MAICs.
Course Type
Half Day
Course Level
Intermediate
Format Requirements
The course will consist of short presentations paired with hands-on exercises. Participants will be required to bring laptops with R and RStudio installed for participation in hands-on exercises. Installation instructions will be provided in advance of the course.
Overview
This course will provide an overview of methods for conducting matching-adjusted indirect comparisons (MAICs). These methods are appropriate when analysts have access to individual participant data on one treatment, and aggregate data for a comparator treatment. By conducting a MAIC, it is possible to match trials on important prognostic and effect modifying characteristics and reduce bias in comparisons from indirect estimates. This course will provide participants with hands-on experience conducting MAICs in line with best practices.
Description & Objectives
The course will begin with an overview of indirect comparisons with a focus on scenarios in which an MAIC may be desirable. We will discuss examples of MAICs that have been submitted to major HTA agencies around the globe. Participants will subsequently review the most recent best practices and work through a complete example using simulated data. This process will include:
Assessment of the feasibility and appropriateness of MAIC
Identification of important prognostic and treatment effect modifying variables
Complete analysis of anchored and unanchored MAIC
Exploration of assumptions
Visualization and communication of MAIC results
Objectives:
Following this short-course, participants will:
Understand the various indirect treatment comparison methodologies and when an MAIC should be considered;
Be familiar with best practices for MAIC and their rationale;
Be able to assess whether an MAIC is feasible;
Understand methods for identification of prognostic and treatment effect modifying variables
Have experience using code provided by the NICE TSD to run anchored and unanchored MAICs
Be familiar with best practices for exploration/visualization of assumptions and results
Course Director
Chris Cameron
Cornerstone Research Group
Course Faculty
Brian Hutton
School of Epidemiology and Public Health, University of Ottawa
Abhishek Varu
Cornerstone Research Group
Timothy Disher
Dalhousie University/IWK Health Centre
Register for this Session
Session Fees
Fee Type
Member Fee
Non-Member Fee
This session is free
Early:
$200.00
$325.00
Regular:
$245.00
$370.00
Late:
$245.00
$370.00
This session is free
Early:
$170.00
$170.00
Regular:
$215.00
$215.00
Late:
$215.00
$215.00