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Navigating Comparative Effectiveness in the Inflation Reduction Act: Methodological Approaches for Healthcare Challenges

The Inflation Reduction Act (IRA), passed in August 2022, marks a significant shift in the US healthcare landscape, particularly for Medicare. The IRA introduces reforms to Medicare's prescription drug program (Part D), inflationary caps in Medicare (Part B), and Medicare price negotiation. To effectively navigate these changes, pharmaceutical companies must develop robust evidence-generation programs that support evolving value requirements throughout a drug's life cycle.

One key aspect of the IRA is the negotiation of "maximum fair prices" (MFP) for selected brand name drugs. To establish added benefit, manufacturers will need to develop a holistic view of indications, lines, and formulations based on real-world evidence (RWE) and randomized controlled trials (RCTs) several years post-launch. The law requires the Health and Human Services (HHS) Secretary to consider evidence about alternative treatments and comparative effectiveness. This will require the inclusion of RWE and thus overcoming real-world data (RWD) limitations. The use of advanced epidemiologic techniques such as target trial emulation, quantitative bias analysis, and matching, weighting, or outcome regression techniques can help achieve this.

Target trial emulation

The IRA will require head-to-head comparisons of a therapy against alternative treatments. Target trial emulation (TTE) is a framework for estimating treatment effects by emulating a randomized controlled trial using observational data. TTE ensures the correct estimands are estimated for the causal contrast of interest and addresses issues arising from misaligned research goals or potential biases. By employing doubly robust methods, TTE allows researchers to obtain more accurate insights from RWD, improving the quality of evidence generated in clinical research.

Quantitative bias analysis

To address uncertainty in comparative effectiveness estimates derived from RWE, Quantitative bias analysis (QBA) can be used for issues with missing data, unmeasured confounders, and measurement error. By carefully selecting and applying QBA methods like E-values, external bias adjustment, and "tipping point" analyses, researchers can derive accurate and reliable insights from RWD, ultimately improving the quality of clinical research and informing the IRA negotiation process.

Matching, weighting, and outcome regression techniques

An important consideration for the life-cycle approach of evidence that the IRA will require is the regular updating of comparative effectiveness to a drug manufacturer’s pivotal trial data. Real-world data can also be used to generate external control arms for single-arms of trials. Several methods have been developed to facilitate valid comparisons between RWD sources and trial participants, such as matching and weighting methods, Bayesian synthesis and borrowing methods, and small sample size solutions. These methods help mitigate potential confounding and enable the integration of both aggregate-level information and individual-level patient data.

By embracing new developments in statistical methods, drug manufacturers will be able to effectively address the limitations inherent in real-world data and develop comprehensive evidence-generation programs that support the entire product life cycle. This will allow for a more informed IRA price negotiation process and ensure Medicare patients in the US receive the most effective and affordable treatments available.


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