Progression-free survival in control arms of clinical trials: Analysis of FDA cancer drug approvals between 2014 and 2023.

11031 Background: FDA regularly approves new cancer drugs based on the results of surrogate endpoints, often using expedited programs. When assessing the efficacy of new anticancer treatments, the choice of the control arm plays a crucial role. Herein, we evaluated the progression-free survival (PFS) performance of control arms in trials that led to FDA approvals for oncology treatments. Methods: We investigated FDA drug approvals issued between January 2014 and December 2023 for the treatment of advanced cancers, available on the FDA.gov website. We analysed publications of phase III or double-arm phase II trials that prompted approvals. For each clinical trial, we collected: i) The assumed median PFS (amPFS) value of the control arm, generally indicated in the study protocol for the sample size calculation; ii) The control arm’s recorded median PFS (rmPFS) and its confidence interval (CI). Control arms of each trial were considered under- or out-performing if amPFS exceeded the highest CI of rmPFS or was inferior than the lowest CI of rmPFS, respectively. All other cases were defined as “within the range”. Results: We found 72 trials leading to new cancer drug approvals. Immune checkpoint inhibitors (ICIs) and small molecules were investigated in the majority of cases, in 27 (37.5%) and 19 (26.4%) trials, respectively. Control arms under- or out-performed in 24/72 (33.3%) and 8/72 (11.1%) cases, respectively. In the remaining 40/72 (55.5%) trials, control arms performances were “within the range”. Interestingly, in 12/27 (44.4%) trials leading to ICIs approval, control arms underperformed. We found that in 6/14 (42.9%), 5/16 (31.3%) and 5/16 (31.3%) trials leading to new approvals in lung, breast and genitourinary cancers, respectively, the control arms underperformed. In 18/24 (75.0%) underperforming control arms, the discrepancy between amPFS and rmPFS was larger than 20%. Among the 19 trials with underperforming control arm and leading to expedited approval, in 7 (36.8%) cases a final overall survival (OS) advantage was not observed. Conclusions: The PFS benefit observed in several trials leading to FDA approval of new anticancer drugs may have been influenced by shorter-than-expected PFS in the control arms. Accurate analysis of control arms outcomes in clinical trials is essential for a comprehensive assessment of the efficacy of experimental drugs.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Oncology
Published
2025-05-28
DOI
https://doi.org/10.1200/jco.2025.43.16_suppl.11031
Primary Topic
Economic and Financial Impacts of Cancer
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Progression-free survival in control arms of clinical trials: Analysis of FDA cancer drug approvals between 2014 and 2023.

Lucia Longo, Angela Viggiano, Fabio Salomone, Luigi Liguori et al.
Journal of Clinical Oncology
Economic and Financial Impacts of Cancer
article

Progression-free survival in control arms of clinical trials: Analysis of FDA cancer drug approvals between 2014 and 2023.

Lucia Longo, Angela Viggiano, Fabio Salomone, Luigi Liguori, Luigi Formisano, M. Isernia, Alberto Servetto, Sasha D’Ambrosio, Roberto Bianco, Fabiana Napolitano, Anna Russo, Filippo Vitale, Annarita Avanzo
article en

Abstract

11031 Background: FDA regularly approves new cancer drugs based on the results of surrogate endpoints, often using expedited programs. When assessing the efficacy of new anticancer treatments, the choice of the control arm plays a crucial role. Herein, we evaluated the progression-free survival (PFS) performance of control arms in trials that led to FDA approvals for oncology treatments. Methods: We investigated FDA drug approvals issued between January 2014 and December 2023 for the treatment of advanced cancers, available on the FDA.gov website. We analysed publications of phase III or double-arm phase II trials that prompted approvals. For each clinical trial, we collected: i) The assumed median PFS (amPFS) value of the control arm, generally indicated in the study protocol for the sample size calculation; ii) The control arm’s recorded median PFS (rmPFS) and its confidence interval (CI). Control arms of each trial were considered under- or out-performing if amPFS exceeded the highest CI of rmPFS or was inferior than the lowest CI of rmPFS, respectively. All other cases were defined as “within the range”. Results: We found 72 trials leading to new cancer drug approvals. Immune checkpoint inhibitors (ICIs) and small molecules were investigated in the majority of cases, in 27 (37.5%) and 19 (26.4%) trials, respectively. Control arms under- or out-performed in 24/72 (33.3%) and 8/72 (11.1%) cases, respectively. In the remaining 40/72 (55.5%) trials, control arms performances were “within the range”. Interestingly, in 12/27 (44.4%) trials leading to ICIs approval, control arms underperformed. We found that in 6/14 (42.9%), 5/16 (31.3%) and 5/16 (31.3%) trials leading to new approvals in lung, breast and genitourinary cancers, respectively, the control arms underperformed. In 18/24 (75.0%) underperforming control arms, the discrepancy between amPFS and rmPFS was larger than 20%. Among the 19 trials with underperforming control arm and leading to expedited approval, in 7 (36.8%) cases a final overall survival (OS) advantage was not observed. Conclusions: The PFS benefit observed in several trials leading to FDA approval of new anticancer drugs may have been influenced by shorter-than-expected PFS in the control arms. Accurate analysis of control arms outcomes in clinical trials is essential for a comprehensive assessment of the efficacy of experimental drugs.

Journal of Clinical OncologyVol. 43(16_suppl)
Southwestern Medical Center (US), Southwestern Medical Center (US), University of Naples Federico II (IT), The University of Texas Southwestern Medical Center (US)
Openalex Percentile: Top 83%
Economic and Financial Impacts of Cancer
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.