On Demand Webinar Series:
HEOR & RWE
Effect Modifiers In Indirect Treatment Comparisons; Steps To Ensure the Unbiased Identification to Inform Decision Making
Population-adjusted indirect treatment comparisons (PAICs) are commonly used in health technology appraisals to estimate relative treatment effects in presence of bias due to effect modification. Bias due to effect modification occurs when there are distributional differences in the effect modifiers (EMs) across the trials in a network. Standard network meta-analyses (NMAs) are based on aggregated data (AgD) and assume no EMs or that the distributions of EMs are balanced between trials. However, when this is not the case, different methods of PAICs such as matching-adjusted indirect comparison [MAIC], simulated treatment comparison [STC] and the newest method of Multi-Level Network Meta-Regression (ML-NMR) have been proposed to "correct" for the role of EMs by using available individual patient data (IPD) to adjust for differences in EMs through reweighting or regression adjustment.
Key Learning Points:
- How should EMs be identified from the literature following evidence-based principles? How to ensure the unbiased selection of EMs for inclusion in PAICs?
- How best to navigate across the different methodologies for adjusting for EMs in PAICs and to handle the nuances around each method such as multiple endpoints, sample size considerations and evidence sources?
- What is the potential of a novel indirect treatment comparison approach, called Network Meta-Interpolation which challenges the strong assumption of shared effect modification in NMAs and offer an alternative solution?
Join us for the first webinar in the series, Effect Modifiers In Indirect Treatment Comparisons; Steps To Ensure the Unbiased Identification to Inform Decision Making. One of the main reasons driving the development and use of population-adjusted indirect treatment comparisons (PAIC) is the presence of some evidence for effect modification across the trials in the network and that, each of these EMs is distributed differently in the included study populations. Therefore, ensuring the unbiased identification of effect modifiers (EMs) for use in these comparisons is key to ensure the validity of analytical results. So far, much less emphasis has been placed on how these EMs are identified from the literature and selected for incorporation in these analyses.
Key learning points:
During this first webinar in the series our speakers will address the following questions, which will be answered based on the available methodologies and guidance from key organizations and Cytel research, but also takes into account recent methodological trends in decision-making:
- What is the current guidance from key organizations for the identification of EMs for use in PAICs?
- How often do researchers provide justification for the selection of EMs in their analyses?
- What are the key steps to confidently justify the unbiased identification of EMs for use in PAICs?
- Grammati Sarri, Senior Research Principal, Head of RWAA External Research Partnerships, Cytel
- Andreas Freitag, Associate Director, Research Principal, Health Economics