Scholarlink

Background and&#xa0;Objectives <p>Survival benefit constitutes the primary pillar of therapeutic efficacy in oncology. However, the survival benefits observed in registrational trials broadly range from significant to marginal, even for US Food and Drug Administration (FDA)-approved drugs. This study explores the association between survival benefits and the likelihood of FDA approval and estimates the boundary effect size that distinguishes FDA-approved from non-approved drugs.</p> Methods <p>We screened 3463 phase 3 trials initiated between 1990 and 2021 on ClinicalTrials.gov. Eligibility was restricted to randomized phase 3 trials for novel anticancer agents with overall survival (OS) as a primary or co-primary endpoint. Included trials required published results, including the OS hazard ratio (HR) and 95% confidence interval (CI). A total of 189 eligible trials were identified, encompassing 208 arm-pairs and 158,250 participants. Clinical data were extracted from published reports, while regulatory outcomes were adjudicated at the trial-and-indication level using US Prescribing Information on Drugs@FDA database. The association between OS benefit and approval status was modeled using logistic regression, with generalized estimating equations (GEE) to account for trial-level clustering. Publication bias was assessed via funnel plots and the trim-and-fill method.</p> Results <p>Of the 208 arm-pairs, 79 (38%) supported FDA approval, and 129 (62%) did not. The dataset spanned 27 cancer types, with a mean sample size of 761 participants. The pooled OS HR was 0.70 (95% CI 0.68–0.73) for approved drugs and 0.95 (95% CI 0.93–0.97) for non-approved drugs. Logistic regression revealed a sharp sensitivity of approval probability to the HR for OS. A boundary was observed ranging from 0.74 to 0.86 in the HR for OS, with a 50% probability of FDA approval at HR 0.80. GEE analysis confirmed the robustness of these estimates against trial-level clustering. While funnel plot asymmetry suggested potential publication bias in the non-approved group, trim-and-fill analysis confirmed that the relative disparity in OS HR between approved and non-approved drugs remained consistent.</p> Conclusion <p>FDA approval for anticancer drugs is characterized by distinct OS HR patterns. While these findings provide a clear efficacy benchmark for OS-driven trials, they should be interpreted cautiously given the evolving therapeutic landscape and potential publication bias in negative trials. Our results underscore the central role of survival benefit in regulatory decisions and provide a quantitative metric to support oncology drug development.</p> Funding <p>This study was funded by the Ministry of Education, Culture, Sports, Science and Technology (MEXT): Shunsuke Ono KAKEN-HI: 25K10043.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Association Between Survival Benefits of Anticancer Drugs and Probability of US Food and Drug Administration Approval: A Systematic Review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月31日

查看全文
  • Koji Ishizuka,
  • Shunsuke Ono

摘要

Background and Objectives

Survival benefit constitutes the primary pillar of therapeutic efficacy in oncology. However, the survival benefits observed in registrational trials broadly range from significant to marginal, even for US Food and Drug Administration (FDA)-approved drugs. This study explores the association between survival benefits and the likelihood of FDA approval and estimates the boundary effect size that distinguishes FDA-approved from non-approved drugs.

Methods

We screened 3463 phase 3 trials initiated between 1990 and 2021 on ClinicalTrials.gov. Eligibility was restricted to randomized phase 3 trials for novel anticancer agents with overall survival (OS) as a primary or co-primary endpoint. Included trials required published results, including the OS hazard ratio (HR) and 95% confidence interval (CI). A total of 189 eligible trials were identified, encompassing 208 arm-pairs and 158,250 participants. Clinical data were extracted from published reports, while regulatory outcomes were adjudicated at the trial-and-indication level using US Prescribing Information on Drugs@FDA database. The association between OS benefit and approval status was modeled using logistic regression, with generalized estimating equations (GEE) to account for trial-level clustering. Publication bias was assessed via funnel plots and the trim-and-fill method.

Results

Of the 208 arm-pairs, 79 (38%) supported FDA approval, and 129 (62%) did not. The dataset spanned 27 cancer types, with a mean sample size of 761 participants. The pooled OS HR was 0.70 (95% CI 0.68–0.73) for approved drugs and 0.95 (95% CI 0.93–0.97) for non-approved drugs. Logistic regression revealed a sharp sensitivity of approval probability to the HR for OS. A boundary was observed ranging from 0.74 to 0.86 in the HR for OS, with a 50% probability of FDA approval at HR 0.80. GEE analysis confirmed the robustness of these estimates against trial-level clustering. While funnel plot asymmetry suggested potential publication bias in the non-approved group, trim-and-fill analysis confirmed that the relative disparity in OS HR between approved and non-approved drugs remained consistent.

Conclusion

FDA approval for anticancer drugs is characterized by distinct OS HR patterns. While these findings provide a clear efficacy benchmark for OS-driven trials, they should be interpreted cautiously given the evolving therapeutic landscape and potential publication bias in negative trials. Our results underscore the central role of survival benefit in regulatory decisions and provide a quantitative metric to support oncology drug development.

Funding

This study was funded by the Ministry of Education, Culture, Sports, Science and Technology (MEXT): Shunsuke Ono KAKEN-HI: 25K10043.