Value-based assessment of pharmacodiagnostic testing from early stage development to real-world use

Tools for the Economic Evaluation of Precision Medicine: A Scoping Review of Frameworks for Valuing Heterogeneity-Informed Decisions

BACKGROUND AND OBJECTIVE

Precision medicine highlights the importance of exploring heterogeneity in the effectiveness and costs of interventions. Our objective was to identify and compare frameworks for valuing heterogeneity-informed decisions, and consider their strengths and weaknesses for application to precision medicine.

METHODS

We conducted a scoping review to identify papers that proposed an analytical framework to place a value, in terms of costs and health benefits, on using heterogeneity to inform treatment selection. The search included English-language papers indexed in MEDLINE, Embase or EconLit, and a manual review of references and citations. We compared the frameworks qualitatively considering: the purpose and setting of the analysis; the types of precision medicine interventions where the framework could be applied; and the framework's ability to address the methodological challenges of evaluating precision medicine.

RESULTS

Four analytical frameworks were identified: value of stratification, value of heterogeneity, expected value of individualised care and loss with respect to efficient diffusion. Each framework is suited to slightly different settings and research questions. All focus on maximising net benefit, and quantify the opportunity cost of ignoring heterogeneity by comparing individualised or stratified decisions to a means-based population-wide decision. Where the frameworks differ is in their approaches to uncertainty, and in the additional metrics they consider.

CONCLUSIONS

Identifying and utilising heterogeneity is at the core of precision medicine, and the ability to quantify the value of heterogeneity-informed decisions is critical. Using an analytical framework to value heterogeneity will help provide evidence to inform investment in precision medicine interventions, appropriately capturing the value of targeted health interventions.

Towards a systems approach for chronic diseases, based on health state modeling

Rising pressure from chronic diseases means that we need to learn how to deal with challenges at a different level, including the use of systems approaches that better connect across fragments, such as disciplines, stakeholders, institutions, and technologies. By learning from progress in leading areas of health innovation (including oncology and AIDS), as well as complementary indications (Alzheimer's disease), I try to extract the most enabling innovation paradigms, and discuss their extension to additional areas of application within a systems approach. To facilitate such work, a Precision, P4 or Systems Medicine platform is proposed, which is centered on the representation of health states that enable the definition of time in the vision to provide the right intervention for the right patient at the right time and dose. Modeling of such health states should allow iterative optimization, as longitudinal human data accumulate. This platform is designed to facilitate the discovery of links between opportunities related to a) the modernization of diagnosis, including the increased use of omics profiling, b) patient-centric approaches enabled by technology convergence, including digital health and connected devices, c) increasing understanding of the pathobiological, clinical and health economic aspects of disease progression stages, d) design of new interventions, including therapies as well as preventive measures, including sequential intervention approaches. Probabilistic Markov models of health states, e.g. those used for health economic analysis, are discussed as a simple starting point for the platform. A path towards extension into other indications, data types and uses is discussed, with a focus on regenerative medicine and relevant pathobiology.

Is risk stratification ever the same as 'profiling'?

Physicians engage in risk stratification as a normative part of their professional duties. Risk stratification has the potential to be beneficial in many ways, and implicit recognition of this potential benefit underlies its acceptance as a cornerstone of the medical profession. However, risk stratification also has the potential to be harmful. We argue that 'profiling' is a term that corresponds to risk stratification strategies in which there is concern that ethical harms exceed likely or proven benefits. In the case of risk stratification for health goals, this would occur most frequently if benefits were obtained by threats to justice, autonomy or privacy. We discuss implications of the potential overlap between risk stratification and profiling for researchers and for clinicians, and we consider whether there are salient characteristics that make a particular risk stratification algorithm more or less likely to overlap with profiling, such as whether the risk stratification algorithm is based on voluntary versus non-voluntary characteristics, based on causal versus non-causal characteristics, or based on signifiers of historical disadvantage. We also discuss the ethical challenges created when a risk stratification scheme helps all subgroups but some more than others, or when risk stratification harms some subgroups but benefits the aggregate group.

Dilemmas of the causality assessment tools in the diagnosis of adverse drug reactions

Importance: Basic essence of Pharmacovigilance is prevention of ADRs and its precise diagnosis is crucially a primary step, which still remains a challenge among clinicians. Objective: This study is undertaken with the objective to scrutinize and offer a notion of commonly used as well as recently developed methods of causality assessment tools for the diagnosis of adverse drug reactions and discuss their pros and cons. Evidence review: Overall 49 studies were recognized for all assessment methods with five major decisive factors of causality evaluation, all the information regarding reasons allocating causality, the advantages and limitations of the appraisal methods were extracted and scrutinized. Findings: From epidemiological information a past prospect is designed and subsequent possibility merged this background information with a clue in the individual case to crop up with an approximation of causation. Expert judgment is typically based on the decisive factor on which algorithms are based, nevertheless in imprecise manner. The probabilistic methods use the similar principle; however connect probabilities to each measure. Such approaches are quite skeptical and liable to generate cloudy causation results. Causation is quite intricate to ascertain than correlation in Pharmacovigilance due to numerous inherent shortcomings in causality assessment tools. Conclusions and relevance: We suggest that there is a need to develop a high quality assessment tool which can meticulously establish suitable diagnostic criteria for ADRs with universal acceptance to improvise the fundamental aspect of drug safety and evade the impending ADRs with the motive to convert Pharmacovigilance into a state of art.

Economic incentives for evidence generation: promoting an efficient path to personalized medicine

The preceding articles in this volume have identified and discussed a wide range of methodological and practical issues in the development of personalized medicine. This concluding article uses the resulting insights to identify implications for the economic incentives for evidence generation. It argues that promoting an efficient path to personalized medicine is going to require appropriate incentives for evidence generation including: 1) a greater willingness on the part of payers to accept prices that reflect value; 2) consideration of some form of intellectual property protection (e.g., data exclusivity) for diagnostics to incentivize generation of evidence of clinical utility; 3) realistic expectations around the standards for evidence; and 4) public investment in evidence collection to complement the efforts of payers and manufacturers. It concludes that such incentives could build and maintain a balance among: 1) realistic thresholds for evidence and the need for payers to have confidence in the clinical utility of the drugs and tests they use; 2) payment for value, with prices that ensure cost-effectiveness for health systems; and 3) levels of intellectual property protection for evidence generation that provide a return for those financing research and development, while encouraging competition to produce both better and more efficient tests.