Comparative Effectiveness: Methods and Techniques for Better Decision-Making
Health technology assessment (HTA) submissions require cost effectiveness analyses based on comparative effectiveness studies of survival benefits vs. standard-of-care options in each specific geography. Ideally, these submissions are based on large, randomized control trials (RCTs), however, most new drugs approved in the last five years, specifically in oncology and rare diseases, are being approved based on small clinical trials, often un-controlled or single arm. Often, these trials do not have overall survival as the primary efficacy parameter.
Thus, the dilemma: comparative evidence is still required for HTA submissions, but traditional investigative methods are no longer suitable to support value proposition. So where do we go from here?
There is a wide range of comparative effectiveness methods, and while some vendors tend to rely on the methods they’re most familiar with, they may not be fit for purpose. This issue is addressed in the first webinar of a four-part series on making better decisions using innovative comparative effectiveness methods, where Vice President, HEOR EU, Maria Rizzo and Vice President, HEOR Global, Bart Heeg discuss why you should use specific methods for specific cases. They also outline innovative methods that may be very helpful when data are insufficient.
The Maze of Methods and the Difficulty in Conducting an ITC Feasibility Assessment
When it comes to indirect treatment comparisons (ITCs) conducted to support HTA decision-making, explains Maria, there is a maze of ITC solutions available. The first critical step is to complete a feasibility assessment in order to navigate this maze.
There are four main considerations when conducting a feasibility assessment:
- Treatment pathway to define relevant populations and comparators
- Network availability and completeness
- Heterogeneity of patients and trial characteristics
- Observed treatment effects and how these are influenced by patients characteristics
However, based on the underlying trial data (whether the trial was an RCT, a single-arm trial, a comparison based on real-world evidence, and so on), this leads to further decisions, choices, and thus methods.
Navigating the Maze: Solutions
Cytel offers various unique solutions to solve and navigate this maze:
- As Bart outlines, if you have an RCT, and identify effect modifiers, you might need to consider an MAIC, an STC, or an ML-NMR, but these methods potentially result in bias. Cytel experts have developed a method called Network Meta-Interpolation, which uses subgroup analyses to adjust for effect modification, but does not assume shared effect modification, thus reducing the bias of other PAIC methods in HTAs.
- If you have a disconnected network, for example, colleagues have developed a methodology called Target Trial Emulation, with which you can do comparative effectiveness based on RWE.
- When comparing a single-arm trial against RWE, there may be missing data, which could influence your conclusions. Quantitative Bias Analysis is a technique to assess the impact of these unobserved variables on your outcome, therefore increasing the credibility of your analysis, and the likelihood that submissions will be accepted.
- Lastly, a difficult case of comparative effectiveness of a single-arm trial is a tumor-agnostic trial. Cytel experts have developed a technique based on Patient Hierarchical Modeling, in which a tumor-agnostic trial is compared with another set of data for comparative effectiveness for HTA purposes.
For more information on navigating comparative effectiveness methods, including a discussion of the evolution of this maze, a breakdown of feasibility assessments, and an explanation of these innovative methods, including a visual overview of the methods maze, watch the webinar “Comparative Effectiveness: Methods and Techniques for Better Decision-Making” now:
Read more from Perspectives on Enquiry & Evidence:
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Network Meta-Interpolation: Effect Modification Adjustment in Network Meta-Analysis Using Subgroup Analyses
Using Quantitative Bias Analysis in Real World Data Strategy
Can RWE Help Restore Decades of Health Inequalities? Yes, and Here’s How
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