When new treatments are compared with existing therapies in clinical care, population-adjustment techniques need to ensure that the populations in the compared treatments are similar. Key to this process is the identification of effect modifiers: that is, factors, like demographic characteristics or genetics, that can alter the effect of a treatment on a clinical outcome and impact relative effects. Ensuring an unbiased selection of effect modifiers can translate to more confident downstream decision-making throughout the process of obtaining payer approval.
Indirect treatment comparisons and the challenge of effect modification
Methods for indirect treatment comparisons (ITCs) enable statistical experts to compare the results of two different therapies even when head-to-head trial comparisons are not available. Rather, if treatments A and B are compared to the same comparator, then assuming similar trial populations, treatments A and B can be compared to each other.
According to Dr. Andreas Freitag, in his recent webinar “Effect Modifiers in Indirect Treatment Comparisons: Steps to Ensure Unbiased Identification to Inform Decision-Making,” the entire scheme of ITCs rests on the assumption of transitivity or exchangeability. This means that there are no systematic differences across the trials that have an effect on the analysis results other than the treatment being compared. Imbalances in effect modifying variables between trials may lead to bias in the ITC results and lower confidence in the subsequent healthcare decision-making.
Undoubtedly, there are often many population differences between the trials undergoing ITC that violate the transitivity assumption. Population-adjustment ITC methods are therefore required to combat the bias in the comparisons due to evidence for effect modifiers that are differently distributed between the compared trials. Note that such biases would also exist within a traditional network meta-analysis if the distribution of a specific effect modifier differed from trial to trial. As a result, network meta-analyses often begin with unrealistic assumptions that the relative treatment effects between two treatments found in one trial would be the same across the populations in the other trials included in the network. Using population-adjusted methods, imbalances in effect modifiers can be adjusted for to allow for these assumptions to hold.
The population-adjusted ITC methods used to correct for such bias are varied and often depend on the type and quality of the data available. An overview of methods available to sponsors (adapted from the National Institute for Health and Care Excellence, or NICE, Decision Support Unit recommendations and including new methods proposed by Cytel experts) and when to use each of them is provided in Figure 1 below.
How to choose which effect modifiers to examine
HTA agencies like NICE and the French National Authority for Health have a number of guidelines on how to implement statistical methods for correcting such bias. However, these agencies leave open the question of how effect modifiers should be selected in the first place. In many instances, effect modifiers are not well established and the choice of which variables to adjust for in ITCs is based on insufficient information. This risks introducing bias in the analysis results by not adjusting for all relevant factors or a loss of precision by over-adjusting for factors that are not effect modifiers.
According to Dr. Freitag, multiple sources of consideration should affect whether to incorporate a specific effect modifier into the analyses. A sponsor should review the literature, consult a clinical or subject-matter expert, examine concerns documented in previous HTA submissions and network meta-analyses/ITCs, and consider statistical analysis of available individual patient-level data. Unfortunately the need for such evaluation is not always a streamlined process as the considerations can arise in ad hoc ways before an analysis is conducted by the sponsor.
In order to understand exactly which effect modifiers might be problematic, working with a quantitative strategist can help.
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About the Author of the Blog:
Dr. Esha Senchaudhuri is a research and communications specialist, committed to helping scholars and scientists translate their research findings to public and private sector executives. At Cytel Esha leads content strategy and content production across the company's five business units. She received a doctorate from the London School of Economics in philosophy, and is a former early-career policy fellow of the American Academy of Arts and Sciences. She has taught medical ethics at the Harvard School of Public Health (TH Chan School), and sits on the Steering Committee of the Society for Women in Philosophy's Eastern Division, which is responsible for awarding the Distinguished Woman in Philosophy Award.