In the evolving landscape of health technology assessment (HTA), the inclusion of adverse events (AEs) in economic evaluations remains a critical yet underdeveloped area. Despite the rigorous benefit-risk assessments conducted by regulatory agencies like the European Medicines Agency and the Food and Drug Administration, standardised methods for incorporating AEs into economic evaluations are notably absent. This gap hinders effective healthcare decision-making, impacting everything from drug reimbursement to price negotiations.
The Unmet Need for Standardized Methods
Currently, there are no standardised methods for including AEs in economic evaluations of health technologies. This lack of standardisation can lead to significant variations in how AEs are handled, potentially skewing the results of economic evaluations. As highlighted by Ghabri, Dawoud, and Drummond, there’s a pressing need for improved methods to incorporate AEs in these evaluations to ensure accurate and reliable results.
Key Issues in Incorporating AEs
Several methodological challenges must be addressed to effectively integrate AEs into economic evaluations:
1. Incorporation into Decision Models: Integrating AEs into decision-analytic models is essential but often neglected. Recent evaluations, such as the assessment of new Alzheimer drugs by the Institute for Clinical and Economic Review, underscore the importance of considering AEs like amyloid-related imaging abnormalities in these models.
2. Terminology and Types of AEs: The lack of a standardised terminology for AEs complicates their inclusion in economic evaluations. Aligning with the terminology used by regulatory agencies could promote consistency and transparency.
3. Estimating QoL and Cost Consequences: Properly estimating the consequences of AEs in terms of quality of life (QoL) and costs is vital. Current methods often fail to capture the full impact of AEs, leading to an incomplete economic evaluation.
4. Exploring Uncertainty: Addressing the uncertainty associated with the impact of AEs on economic results is crucial. Robust sensitivity analyses and probabilistic modelling can help in understanding and mitigating these uncertainties.
Estimation of Quality of Life (QoL) and Cost Consequences
Importance of Accurate Estimation
Estimating the consequences of AEs in terms of Quality of Life (QoL) and costs is critical for a comprehensive economic evaluation of health technologies. This estimation directly impacts the incremental cost-effectiveness ratio (ICER), which is a key metric used in health technology assessments (HTA) to determine the value for money of medical interventions.
Challenges in Estimating QoL Impact
1. Diverse Nature of AEs: Adverse events vary widely in their severity, duration, and impact on patients’ lives. For instance, a mild rash has a different impact compared to a severe cardiovascular event. Capturing this diversity accurately is challenging but essential for a realistic estimation of QoL.
2. Lack of Standardized Measures: There is no universally accepted measure for capturing the QoL impact of AEs. While tools like the EQ5D and SF36 are commonly used, they may not capture all dimensions of the impact specific to certain AEs.
3. Patient-Reported Outcomes: QoL impact is often subjective and may vary significantly between patients. Incorporating patient-reported outcomes (PROs) can provide a more comprehensive understanding of the QoL impact but requires robust data collection and analysis methods.
Challenges in Estimating Cost Consequences
1. Direct Costs: These include the medical costs directly associated with managing AEs, such as hospitalisation, medications, and follow-up visits. Accurate estimation requires detailed data on healthcare resource utilisation.
2. Indirect Costs: Indirect costs, such as loss of productivity, long-term disability, and caregiver burden, are often harder to quantify but can be significant. These costs require economic models that account for broader societal impacts.
3. Data Availability: Reliable data on the costs associated with AEs are often sparse, especially for newer health technologies. Real-world evidence, such as that from electronic health records and insurance claims data, can help fill these gaps but may have limitations in terms of completeness and quality.
Improved Methods for Estimating QoL and Costs
1. Advanced Modelling Techniques: Techniques like Markov models and discrete event simulations can provide more nuanced estimates of the long-term impact of AEs on both QoL and costs. These models can incorporate various health states and transitions over time, reflecting the chronic and dynamic nature of many AEs.
2. Utility Weights: Developing and using disease-specific utility weights can improve the accuracy of QoL estimations. These weights can be derived from patient surveys and clinical studies that focus on the specific impacts of AEs.
3. Cost Databases: Utilizing comprehensive cost databases that capture both direct and indirect costs can enhance the accuracy of cost estimations. These databases should be regularly updated and validated to ensure reliability.
4. Sensitivity Analysis: Conducting extensive sensitivity analyses can help in understanding the uncertainty surrounding the estimates. Probabilistic sensitivity analysis, in particular, can account for the variability in both QoL and cost inputs, providing a range of possible outcomes rather than a single-point estimate.
Exploring Uncertainty
Importance of Addressing Uncertainty
Uncertainty is an inherent aspect of economic evaluations, especially when it comes to the impact of adverse events (AEs). Accurately capturing and exploring this uncertainty is crucial for providing reliable and robust economic evaluations of health technologies. This ensures that decision-makers are well-informed about the potential range of outcomes and the confidence they can place in these evaluations.
Sources of Uncertainty
1. Variability in AE Incidence: The incidence rates of AEs can vary significantly between different patient populations and settings. This variability can be due to factors such as demographic differences, comorbid conditions, and varying healthcare practices.
2. Severity and Duration of AEs: AEs can differ in their severity and duration, which in turn affects their impact on quality of life (QoL) and costs. Capturing this range of outcomes is essential for a comprehensive evaluation.
3. Data Limitations: Often, data on AEs, particularly long-term data, may be limited or incomplete. This lack of comprehensive data introduces uncertainty into the estimates of both QoL impact and costs.
4. Modelling Assumptions: Economic evaluations rely on various assumptions and parameters within their models. These assumptions, such as the discount rate or the utility values assigned to different health states, can significantly influence the results.
Methods to Address Uncertainty
1. Sensitivity Analysis: Sensitivity analysis is a key method to explore the impact of uncertainty in economic evaluations. This involves systematically varying the key parameters and assumptions to see how changes affect the outcomes. There are two main types:
Deterministic Sensitivity Analysis: This involves changing one parameter at a time to see its effect on the results.
Probabilistic Sensitivity Analysis (PSA): PSA involves simultaneously varying multiple parameters according to predefined probability distributions. This approach provides a more comprehensive view of the uncertainty by generating a distribution of possible outcomes.
2. Scenario Analysis: Scenario analysis involves evaluating the impact of different plausible scenarios on the outcomes. This can include best-case, worst-case, and most likely scenarios to understand the range of potential impacts of AEs.
3. Monte Carlo Simulation: Monte Carlo simulation is a technique used in PSA to generate numerous iterations of possible outcomes based on the probability distributions of input parameters. This method provides a robust way to capture the uncertainty and present it as a range of likely results with associated probabilities.
4. Bootstrap Methods: Bootstrap methods involve resampling the available data to generate multiple simulated samples. This technique can help in assessing the robustness of the results and providing confidence intervals for the estimates.
5. Bayesian Methods: Bayesian methods incorporate prior knowledge and data to update the probability estimates of different outcomes. This approach can be particularly useful when dealing with limited or uncertain data.
Real-World Application: Example from Cardiovascular Disease
An example from cardiovascular disease treatments can illustrate the practical application of these methods. In evaluating a new anticoagulant, the economic evaluation included various AEs such as bleeding events. By using PSA, the evaluation accounted for the uncertainty in the incidence and severity of these bleeding events. Monte Carlo simulations were employed to generate a range of possible ICERs, providing decision-makers with a probabilistic view of the cost-effectiveness of the anticoagulant.
Recommendations for Good Practice
To address these issues, several recommendations have been proposed:
Develop Standardized Frameworks: Establishing standardized frameworks for the inclusion of AEs in economic evaluations can facilitate more consistent and reliable assessments. These frameworks should document the identification and inclusion process of AEs transparently .
Harmonize Terminology: Adopting a common terminology for AEs, aligned with regulatory agencies, will ensure clarity and consistency across evaluations.
Comprehensive Data Collection: Encouraging the collection of real-world safety data can improve the robustness of economic evaluations. This includes data from clinical trials, registries, and AE reports.
Improved Estimation Methods: Enhancing methods to estimate the impact of AEs on QoL and costs will provide a more accurate picture of their economic implications. This can be achieved through better modelling techniques and comprehensive data analysis.
Multidisciplinary Collaboration: Fostering collaboration among HTA bodies, clinical researchers, and health economists will drive the development of best practices and innovative approaches to incorporate AEs in economic evaluations.
Moving Forward
The integration of AEs into economic evaluations is not just a methodological challenge but a necessary step towards more accurate and effective healthcare decision-making. As we advance, the proposed recommendations can serve as a starting point for developing robust practices in this field. By addressing these key issues and promoting standardised methods, we can ensure that economic evaluations of health technologies reflect the true impact of AEs, ultimately leading to better healthcare outcomes and more efficient resource allocation.
For further reading, the comprehensive discussion by Ghabri, Dawoud, and Drummond offers detailed insights and practical suggestions, paving the way for future advancements in this crucial area of health technology assessment.
References
1. Keeney E, et al. “Cost-effectiveness analysis of Smoking Cessation interventions in the United Kingdom accounting for major neuropsychiatric adverse events.” *Value Health*, 2021.
2. Gyllensten H, et al. “Comparing methods for estimating direct costs of adverse drug events.” *Value Health*, 2017.
3. Lam L, et al. “Inclusion of adverse events in the economic evaluation of health technologies: a review of the manufacturers’ submissions to the French National Authority for Health (HAS).” *Value Health*, 2019.
4. Brazier J, et al. “Identification, review, and use of health state utilities in cost-effectiveness models: an ISPOR Good Practices for Outcomes Research Task Force report.” *Value Health*, 2019.