An empirical analysis of overall survival in drug approvals by the US FDA (2006–2023)

Elbaz, Josh; Haslam, Alyson; Prasad, Vinay

doi:10.1002/cam4.7190

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Significant treatment effect sizes are arguably easier to obtain with composite surrogate endpoints, and in fact, the majority of regulatory drug approvals in oncology are on the basis of positive surrogate endpoints rather than OS. [42][43][44] In light of these two observations, the strength of correlation between surrogate endpoints and OS has come under scrutiny, as well as the clinical meaningfulness of progression-based measures. [45][46][47][48] Thus, an important caveat to this study is that most trials recommended for early closure used surrogate PEPs.…”

Section: Discussionmentioning

confidence: 99%

Bayesian Interim Analysis and Efficiency of Phase III Randomized Trials

Sherry,

Msaouel,

Miller

et al. 2024

Preprint

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IMPORTANCEImproving the efficiency of interim assessments in phase III trials should reduce trial costs, hasten the approval of efficacious therapies, and mitigate patient exposure to disadvantageous randomizations.OBJECTIVEWe hypothesized thatin silicoBayesian early stopping rules improve the efficiency of phase III trials compared with the original frequentist analysis without compromising overall interpretation.DESIGNCross-sectional analysis.SETTING230 randomized phase III oncology trials enrolling 184,752 participants.PARTICIPANTSIndividual patient-level data were manually reconstructed from primary endpoint Kaplan-Meier curves.INTERVENTIONSTrial accruals were simulated 100 times per trial and leveraged published patient outcomes such that only the accrual dynamics, and not the patient outcomes, were randomly varied.MAIN OUTCOMES AND MEASURESEarly stopping was triggered per simulation if interim analysis demonstrated ≥ 85% probability of minimum clinically important difference/3 for efficacy or futility. Trial-level early closure was defined by stopping frequencies ≥ 0.75.RESULTSA total of 12,451 simulations (54%) met early stopping criteria. Trial-level early stopping frequency was highly predictive of the published outcome (OR, 7.24; posterior probability of association, >99.99%; AUC, 0.91;P< 0.0001). Trial-level early closure was recommended for 82 trials (36%), including 62 trials (76%) which had performed frequentist interim analysis. Bayesian early stopping rules were 96% sensitive (95% CI, 91% to 98%) for detecting trials with a primary endpoint difference, and there was a high level of agreement in overall trial interpretation (Bayesian Cohen’s κ, 0.95; 95% CrI, 0.92 to 0.99). However, Bayesian interim analysis was associated with >99.99% posterior probability of reducing patient enrollment requirements (P< 0.0001), with an estimated cumulative enrollment reduction of 20,543 patients (11%; 89 patients averaged equally over all studied trials) and an estimated cumulative cost savings of 851 million USD (3.7 million USD averaged equally over all studied trials).CONCLUSIONS AND RELEVANCEBayesian interim analyses may improve randomized trial efficiency by reducing enrollment requirements without compromising trial interpretation. Increased utilization of Bayesian interim analysis has the potential to reduce costs of late-phase trials, reduce patient exposures to ineffective therapies, and accelerate approvals of effective therapies.KEY POINTSQuestionWhat are the effects of Bayesian early stopping rules on the efficiency of phase III randomized oncology trials?FindingsIndividual-patient level outcomes were reconstructed for 184,752 patients from 230 trials. Compared with the original interim analysis strategy,in silicoBayesian interim analysis reduced patient enrollment requirements and preserved the original trial interpretation.MeaningBayesian interim analysis may improve the efficiency of conducting randomized trials, leading to reduced costs, reduced exposure of patients to disadvantageous treatments, and accelerated approval of efficacious therapies.

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Section: Discussionmentioning

confidence: 99%

Bayesian Interim Analysis and Efficiency of Phase III Randomized Trials

Sherry,

Msaouel,

Miller

et al. 2024

Preprint

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Frequently asked questions on surrogate endpoints in oncology-opportunities, pitfalls, and the way forward

Mittal,

Kim,

Dunn

et al. 2024

eClinicalMedicine

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Analysis of cancer drugs receiving FDA’s Accelerated Approval between 1992 and 2021

Lim,

Kim,

Kang

et al. 2024

Journal of Pharmaceutical Health Services Research

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Objectives This cross-sectional study examines the Food and Drug Administration (FDA)’s Accelerated Approval (AA) pathway for cancer drugs from 1992 to 2021, which expedites the development and approval of new drugs, including biologics, for severe or life-threatening conditions such as cancers. Methods Based on the ‘CDER Drug and Biologic AAs Based on a Surrogate Endpoint’ report, the number of indications where anticancer agents received AA and the conversion rates to Full Approval (FA) were analysed. Outcome measures used in phase II and phase III trials for these drugs were obtained from US National Library of Medicine for comparison. Key findings Of the 278 AA-granted indications, 67% were for anticancer agents. Lymphoma, leukemia, and lung cancer had the highest number of AA indications among all cancer types. The conversion rates to FA varied among periods: Early (1995–2003), Middle (2004–2012), and Late (2013–2021). The conversion rates for drugs were 82%, 79%, and 31%, while biologics exhibited rates of 100%, 78%, and 27%, respectively. The overall response rate was often the primary outcome measure in phase II trials, whereas overall survival and progression-free survival were the common outcome measures in phase III trials. Secondary outcome measures included disease control rate and duration of response. Conclusions This study provides valuable insights for stakeholders seeking to understand the approval criteria and processes for cancer drugs via the AA pathway. However, limitations in data availability, phase-specific variations in drug doses and combinations, and the inability to manage uncertain data should be acknowledged. Research on the AA program to non-cancer drugs is also required.

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An empirical analysis of overall survival in drug approvals by the US FDA (2006–2023)

Cited by 4 publications

References 19 publications

Bayesian Interim Analysis and Efficiency of Phase III Randomized Trials

Bayesian Interim Analysis and Efficiency of Phase III Randomized Trials

Frequently asked questions on surrogate endpoints in oncology-opportunities, pitfalls, and the way forward

Analysis of cancer drugs receiving FDA’s Accelerated Approval between 1992 and 2021

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