https://orcid.org/0000-0001-8964-1653
?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.1. Introduction
Precision medicine, a new paradigm in health-care systems, has been defined as ‘the use of genetic or other biomarker information to improve the safety, effectiveness and health outcomes of patients via more efficiently targeted risk stratification, prevention and tailored medication and treatment-management approaches.’ [Citation1]. Using precision medicine in pharmacotherapy causes improvement in outcomes and reduction in adverse effects [Citation2].
In spite of remarkable benefits, accessibility of precision medicine has been limited due to its high price [Citation2]. By considering maximizing health status of the society and according to limited budget of health-care systems, policy makers have to rationally and carefully decide about resource allocations of health-care system [Citation3]. They make their decision based on different evidence produced by various kinds of studies such as economic evaluations.
Economic evaluation studies assess the amount of costs spent on increasing specific outcome equal to one unit for specific intervention in comparison with its comparator and report it as a ratio named ICER which is calculated as below:
These studies help to evaluate the incremental cost to value of personalized medicines in comparison with the other interventions or standard of care [Citation3].
In the area of personalized medicine, selecting a treatment by biomarker-driven approach replaces disease-oriented approach. This issue has caused necessary topics such as inefficiency and imprecision of conventional clinical trials and their substitution with novel clinical trials such as basket trials in the field of PM [Citation4].
Economic evaluations are mostly designed and conducted based on clinical trials. However, according to the differences of novel clinical trial with conventional one, some substantial considerations in economic evaluations of precision medicine emerge. This paper will discuss the necessity of new methodology for ICER calculation in economic evaluation studies of precision treatments based on basket clinical trials.
2. Basket clinical trial; characteristics and differences with conventional trial
Conducting conventional clinical trials for PM suffers from a number of limitations, namely economic burdens of these trials and insufficient statistical power [Citation4]. These limitations cause the creation of novel trial designs with biomarker-driven approaches in which the evaluations are conducted for various diseases, several different biomarkers, and multi-targeted therapies in one study [Citation4,Citation5]. These novel trials lead to increased efficiency due to minimized waste of time and resources in the process of drug development research [Citation6].
Basket clinical trial or bucket trial (BT), mostly applied in cancer therapies [Citation6], is a kind of novel one in which one targeted treatment (working based on molecular alteration basically caused by genetics) would be evaluated in a population with different types of disease or histology [Citation5,Citation6].
BTs bear some specific characteristics which can be considered the source of differences with conventional clinical trials. These features are explained below:
2.1. Population
Patients included in one basket trial, must have the same particular biomarker or molecular alteration. This alteration can be common in different types of solid tumors. Therefore, cohorts in BTs are composed of cancerous patients with various histology types. Thus, BTs have high population heterogeneity which leads to other concerns, namely comparator selection, outcome determination, and measure.
2.2. Comparator
Each kind of included disease in a BT has its own comparator or standard of care. Because of the variation in comparator arms among different kinds of cancers, BTs are single-arm trials with no randomization [Citation6].
2.3. Outcome
There are different kinds of outcomes measured in clinical trials such as overall survival, response rate, quality of life, etc. According to ‘clinical trial endpoints for the approval of cancer drugs and biologics,’ the complete response or objective response rate must be considered as an evaluated outcome in single-arm trials [Citation7]. Therefore, the most frequent endpoint used in BTs is the response rate [Citation4].
In addition, remarkable heterogeneity among patients causes variation in outcomes of the BTs and has to be considered in outcome measurement. One of the methods, in conditions with high heterogeneity, is subgroup analysis. However, in situations where the genetic mutation would be rare in each tumor type, the number of patients recruited in each subgroup (based on the histology of the tumor) is low [Citation4]. Therefore, subgroup analysis has lower statistical power in outcome estimation. Moreover, researchers intend to investigate whether an intervention (PM) is effective in all cancer sites, independent of tumor histology. They attempt to pool the outcomes (efficacy of targeted medicine) gained from different subgroups. Thus, the result of the BTs is the efficacy of one PM in a population with specific biomarkers or genetic alteration [Citation8].
The other concern about outcome heterogeneity is increasing the risk of false-positive results which means that the intervention is incorrectly considered effective in one tumor type. For that reason, some statistical methods [Citation4], such as two-stage independent analysis [Citation8], have been recommended to overcome this problem.
3. Concern about economic evaluation based on BTs
Economic evaluations are mostly designed and conducted on the basis of the characteristics and information of clinical trials. Therefore, in the area of precision medicine, economic evaluations could be designed based on novel clinical trials such as BTs. Based on a BT, the approach of an economic evaluation could be biomarker-driven which means the cost-effectiveness ratio is estimated for the biomarker. Consequently, the population included in hypothetical cohort of such an economic evaluation has considerable heterogeneity due to different tumor types and comparators. Patient heterogeneity in economic evaluation studies could have various dimensions including: baseline risk, relative treatment effect, health state utility, and resource utilization [Citation9]. In economic evaluations based on basket trials, heterogeneity in hypothetical population could be mainly referred to the costs and outcomes resulted from various tumor types included.
In economic evaluations designed based on conventional clinical trials, ICERs are usually calculated by mean for whole hypothetical cohort. Therefore, aggregation method is regularly applied in economic evaluations for the measurement of the whole population costs and outcomes and systematic differences among individuals are not usually considered [Citation10]. But, significant patient heterogeneity in economic evaluation, modeled based on BTs, would exist due to the different indications. Therefore, the results based on the mean cannot be generalized to all study population. Because, the amount of gains from an intervention in a subgroup might outweigh the mean of the gains calculated for the whole study population [Citation3]. In this condition, it would be rational to calculate ICER separately for each subgroup of patients [Citation11] and accordingly, the result of an economic evaluation based on a BT will be numbers of ICERs. However, it would be preferable to reach a unique ICER as a result of an economic evaluation for policy makers’ better understanding. Moreover, if the main purpose of a study is to calculate an ICER for one biomarker, multi ICERs calculated by subgroup analysis could not be considered as a final result of such a study. Conclusively, developing a specialized methodology which can calculate a unified ICER for a biomarker by consideration of the patients’ variations in a hypothetical cohort is recommended for economic evaluations based on BTs.
Declaration of interest
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Reviewers disclosure
A reviewer on this manuscript has disclosed Advisory Board: BMS, Genentech, EMD Serono, Merck, Sanofi, Seattle Genetics/Astellas, Astrazeneca, Exelixis, Janssen, Bicycle Therapeutics, Pfizer, Immunomedics; Research Support to Institution: Sanofi, Astrazeneca, Immunomedics; Travel costs: BMS, Astrazeneca; Speaking fees: Physicians Education Resource (PER), Onclive, Research to Practice, Medscape; Writing fees: Uptodate, Editor of Elsevier Practice Update Bladder Cancer Center of Excellence; Steering committee of trials/studies: BMS, Bavarian Nordic, Seattle Genetics, QED (all unpaid), and Astrazeneca, EMD Serono, Debiopharm (paid). Peer reviewers on this manuscript have no other relevant financial relationships or otherwise to disclose.
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