Value of information analysis on clinical trial data to help approve or reject new COVID-19 drugs

In a recent study posted to the medRxiv* preprint server, researchers quantified the cost-effective health benefits of pursuing further research or approving or rejecting coronavirus disease 2019 (COVID-19) drugs based on the existing research using a novel method called Value of Information (VOI) analysis.

Study: Emerging Therapies for COVID-19: the value of information from more clinical trials. Image Credit: PHOTOCREO Michal Bednarek/Shutterstock
Study: Emerging Therapies for COVID-19: the value of information from more clinical trials. Image Credit: PHOTOCREO Michal Bednarek/Shutterstock

They modeled the best available evidence from completed randomized controlled trials (RCTs) to conduct these VOI analyses from a US healthcare perspective over a lifetime horizon.

Background

Since the beginning of the COVID-19 pandemic, over 3,500 registered clinical trials investigating potential therapies for COVID-19 have been registered, all competing for resources and patient enrollments.

Amid an environment of uncertainties and incessant flow of information regarding emerging COVID-19 drugs from RCTs, policymakers, researchers, and clinicians need to timely decide whether to give immediate approval, reject, or use Only in Research (OIR), or give Emergency Use Authorization (EUA).

About the study

In the present study, researchers simulated the effect of treatment on a cohort of hospitalized COVID-19 patients, for which meta-analyses or large RCTs reported in-hospital mortality. They used a Markov cohort state-transition model with four health states - hospitalized, recovered from hospital ward or an intensive care unit (ICU), and dead, and based the model parameters on available evidence as of 1 November 2021.

The team estimated quality-adjusted life years (QALYs), life years (LY), and the incremental cost-effectiveness ratio (ICER) for the following drugs compared to usual care: hydroxychloroquine, remdesivir, casirivimab-imdevimab, dexamethasone, baricitinib-remdesivir, tocilizumab, lopinavir-ritonavir, and interferon beta-1a.

To estimate the cost of additional research, the researchers reviewed data of longer duration (26 weeks) trials for 59 novel therapeutic drugs and derived a fixed cost estimate of their trial for three months by pro-rata adjustment. They applied a 3% annual discount rate for costs and a $100,000 willingness-to-pay (WTP) threshold.

Furthermore, they determined the net value of research, and if its value exceeded zero, performing a new RCT would be worthwhile. In other words, a clinical trial could be continued till the incurred cost exceeded the expected benefits.

Study findings

Probabilistic Analysis (PA) indicated an increase in QALY post-treatment with baricitinib-remdesivir, dexamethasone, remdesivir, and tocilizumab, whereas hydroxychloroquine, interferon beta-1a, and lopinavir-ritonavir treatment decreased the quality of life.

At a WTP of $100,000/QALY, VOI analysis demonstrated that treatment with remdesivir, casirivimab-imdevimab, dexamethasone, baricitinib-remdesivir, and tocilizumab led to a positive mean incremental net benefit compared to care as usual. the incremental net health benefit and the incremental net cost-benefit for remdesivir, casirivimab-imdevimab, dexamethasone, baricitinib-remdesivir, and tocilizumab were 0.252, 0.164, 0.545, 0.668 and 0.524 QALYs, and $25,249, $16,375, $54,526, $66,826 and $52,378, respectively.

Additionally, the team noted that further RCTs would not change decisions about treating patients with dexamethasone, casirivimab-imdevimab, baricitinib-remdesivir, lopinavir-ritonavir, and interferon beta-1a. Instead, future research would unnecessarily consume resources, expose trial participants to unnecessary risks and exclude them from receiving alternative treatments (possibly effective).

The implementation strategy with the highest overall value without further need for trials encompassed the implementation of casirivimab-imdevimab ($13.4 billion), dexamethasone ($7.4 billion), and baricitinib-remdesivir ($54.6 billion), and rejection of lopinavir-ritonavir ($0) and interferon beta-1a ($0).

The authors observed the highest net value for hydroxychloroquine in the OIR strategy. Therefore, while further research on this drug would save costs, unless it compensated for (QA)LYs loss, it would be ethically wrong to pursue further research. Remdesivir and tocilizumab attained the highest net value of $21 billion and $7 billion, respectively, with approval, indicating that for these drugs, the potential benefit of further trials did not outweigh the costs of research.

Although the United States Food and Drug Administration (FDA) has granted EUA for remdesivir, casirivimab-imdevimab, and baricitinib-remdesivir; however, based on the current study findings, EUA should not be given for any of the investigated COVID-19 drugs.

Conclusions

Based on the VOI analyses and the net worth of all the examined COVID-19 drugs, the results favored the approval and immediate clinical use of five drugs - remdesivir, casirivimab-imdevimab, dexamethasone, baricitinib-remdesivir, and tocilizumab; and rejected lopinavir-ritonavir and interferon beta-1a.

The study demonstrated how a VOI approach could help quantify research efforts that would give the greatest return from finite resources, accounting for both current uncertainties relevant to the decision amid the ongoing COVID-19 pandemic and the potential consequences of making decisions with and without further evidence.

Additionally, the VOI approach could help avoid unintended harm that would result from the premature use of ineffective or lethal COVID-19 therapies and balance the benefits of further research due to delayed implementation and research costs.

To summarize, using the VOI method, clinical and public health decision-makers could gather real-time data during the ongoing pandemic and discern the optimal time to implement COVID-19 therapies or pursue further research.

*Important notice

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.

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