Overall Survival and Quality-of-Life Superiority in Modern Phase 3 Oncology Trials: A Meta-Epidemiological Analysis.

Overall Survival and Quality-of-Life Superiority in Modern Phase 3 Oncology Trials: A Meta-Epidemiological Analysis - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice JAMA Oncol . 2025 Jun 1;11(7):718–724. doi: 10.1001/jamaoncol.2025.1002 Overall Survival and Quality-of-Life Superiority in Modern Phase 3 Oncology Trials A Meta-Epidemiological Analysis Alexander D Sherry 1, ✉ , Avital M Miller 1 , Jnana Preeti Parlapalli 2 , Gabrielle S Kupferman 1 , Esther J Beck 1, 3 , Jordan McDonald 1 , Ramez Kouzy 1 , Joseph Abi Jaoude 4 , Timothy A Lin 1 , Nina N Sanford 5 , Fumiko Chino 6 , Bishal Gyawali 7, 8, 9 , Christopher Booth 7, 8, 9 , Pavlos Msaouel 10, 11 , Ethan B Ludmir 12, 13 Author information Article notes Copyright and License information 1 Division of Radiation Oncology, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston 2 Department of Computer Science and Engineering, Texas A&M University, College Station 3 Grossman School of Medicine, New York University, New York, New York 4 Department of Radiation Oncology, Stanford University, Stanford, California 5 Department of Radiation Oncology, University of Texas Southwestern, Dallas 6 Division of Radiation Oncology, Department of Breast Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston 7 Division of Cancer Care and Epidemiology, Queen’s Cancer Research Institute, Kingston, Ontario, Canada 8 Department of Oncology, Queen’s University, Kingston, Ontario, Canada 9 Public Health Sciences, Queen’s University, Kingston, Ontario, Canada 10 Division of Cancer Medicine, Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston 11 Division of Pathology and Laboratory Medicine, Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston 12 Division of Radiation Oncology, Department of Gastrointestinal Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston 13 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston ✉ Corresponding Author: Alexander D. Sherry, MD, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Unit 1422, Houston, TX 77030 ( alexanderdsherry@gmail.com ). Accepted for Publication: March 8, 2025. Published Online: June 1, 2025. doi: 10.1001/jamaoncol.2025.1002 Author Contributions: Drs Sherry and Ludmir had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Sherry, Beck, Kouzy, Abi Jaoude, Lin, Booth, Msaouel, Ludmir. Acquisition, analysis, or interpretation of data: Sherry, Miller, Parlapalli, Kupferman, Beck, McDonald, Kouzy, Abi Jaoude, Lin, Sanford, Chino, Gyawali, Booth, Ludmir. Drafting of the manuscript: Sherry, Kouzy, Abi Jaoude. Critical review of the manuscript for important intellectual content: All authors. Statistical analysis: Sherry, Miller, Parlapalli, Beck, Kouzy, Abi Jaoude, Msaouel, Ludmir. Obtained funding: Ludmir. Administrative, technical, or material support: Kouzy. Supervision: Sherry, Sanford, Msaouel, Ludmir. Conflict of Interest Disclosures: Dr Sherry reported receipt of personal fees from Sermo. Dr Chino reported receipt of grants from Merck. Dr Gyawali reported receipt of personal fees from Vivio. Dr Msaouel reported receipt of honoraria for service on a scientific advisory board for Mirati Therapeutics, Bristol Myers Squibb, and Exelixis; consulting for Axiom Healthcare Strategies; nonbranded educational programs supported by DAVA Oncology, Exelixis, and Pfizer; and research funding for clinical trials from Regeneron Pharmaceuticals, Summit Therapeutics, Merck, Takeda, Bristol Myers Squibb, Mirati Therapeutics, Gateway for Cancer Research, and the University of Texas MD Anderson Cancer Center. Dr Ludmir reported consulting for Xerient. No other disclosures were reported. Funding/Support: This research was supported in part by the National Institutes of Health/National Cancer Institute under award P30CA016672 and by the Andrew Sabin Family Fellowship Foundation (to Drs Msaouel and Ludmir). Role of the Funder/Sponsor: The study supporters had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Data Sharing Statement: See Supplement 2 . Additional Contributions: We thank Donald Noorwood, BS, of Editing Services, Research Medical Library at The University of Texas MD Anderson Cancer Center, for manuscript editing (no compensation outside of regular salary). ✉ Corresponding author. Received 2025 Jan 12; Accepted 2025 Mar 8; Issue date 2025 Jul. Copyright 2025 American Medical Association. All rights reserved, including those for text and data mining, AI training, and similar technologies. PMC Copyright notice PMCID: PMC12127959  PMID: 40451185 See commentary " The Role of Meta-Epidemiology in Oncology Research-Bridging Evidence and Practice. " with doi: 10.1001/jamaoncol.2025.1000. Key Points Question What is the proportion of phase 3 oncology trials improving overall survival or quality of life? Findings In this meta-epidemiological study, across 791 trials, 53% of trials were interpreted as showing primary end-point superiority; however, overall survival and quality-of-life superiority were shown in just 28% and 11% of trials, respectively. In 32% of trials, superiority of either overall survival or quality of life was demonstrated; in 6%, both were shown. Meaning Although phase 3 trials are often interpreted as demonstrating superiority, overall survival and quality of life, which may be the most relevant end points to patients, are uncommonly improved. Abstract Importance Alternative end points, such as progression-free survival, are increasingly used in phase 3 randomized clinical trials (RCTs). However, alternative end points are often not valid surrogates for overall survival and quality of life (QOL) and may be less relevant to patients. Objective To determine the proportion of phase 3 RCTs with overall survival or QOL superiority. Design and Setting Meta-epidemiological study of 2-group, superiority-design, interventional phase 3 oncology RCTs screened from ClinicalTrials.gov and published between 2002 and 2024. Main Outcomes and Measures Alternative end-point, overall survival, and QOL superiority in the experimental group vs the reference/control group according to prespecified statistical criteria for each RCT. A secondary goal was to evaluate the quality of QOL analyses, since approaches unadjusted for baseline scores may bias results. Results A total of 791 RCTs representing 555 580 enrolled patients were included. Alternative primary end points were most common (n = 495 [63%]). The primary end point was met in 53% of the RCTs (n = 420); alternative end-point superiority was shown in 55% (n = 434). Overall survival superiority was shown in 28% (n = 221). Patient-reported outcomes were collected in 61% of the RCTs (n = 482), but global QOL results were published in only 34% (n = 271). Most between-group global QOL analyses did not adjust for baseline scores (223 [82%]). Global QOL superiority was shown in 11% (n = 84). Among all RCTs, 32% (n = 257) demonstrated either overall survival or global QOL superiority. Superiority of both overall survival and global QOL was shown in 6% (n = 48). Among 434 RCTs with a positive alternative end point, only a minority showed superiority of either overall survival (185 [43%]) or global QOL (67 [15%]). Conclusions and Relevance Findings of superiority-design phase 3 oncology RCTs are commonly interpreted as positive. However, this is mostly based on improvements in alternative end points. Gains in either overall survival or QOL are uncommon, even when alternative end-point findings are positive. QOL appears both underevaluated and underreported; furthermore, the majority of phase 3 QOL analyses are unadjusted for baseline scores, which lose efficiency and add bias compared with adjusted analyses. To increase the meaningfulness of late-phase research, future trial designs and regulatory processes should be refocused toward overall survival and QOL improvements. This meta-epidemiological study analyzes phase 3 oncology randomized clinical trials to determine the proportions of trials that show improvement in overall survival, quality of life, and alternative end points and compares these outcomes with overall trial interpretation. Introduction Phase 3 oncology randomized clinical trials (RCTs) provide important evidence to advance the standard of cancer care. Historically, overall survival, defined as the duration of time from randomization to death, has been the most common primary end point of phase 3 RCTs. 1 Alternative end points, such as progression-free survival, have now overtaken overall survival as the most common primary end point of phase 3 RCTs. 2 , 3 Alternative end points have multiple advantages compared with overall survival. For example, alternative end points often result in faster trial readouts than overall survival. In addition, event rates for alternative end points are usually higher than for overall survival, decreasing the number of patients needed for enrollment and the cost of trials. Consequently, alternative end points frequently serve as the basis for current cancer drug regulatory approvals, largely based on the assertion that alternative end points are good surrogates of overall survival. 2 , 3 , 4 , 5 While alternative end points endeavor to capture meaningful responses to therapy and serve as close surrogates for overall survival, progression defined by alternative end-point metrics is often only poorly correlated with overall survival or quality of life (QOL). 6 , 7 , 8 , 9 , 10 , 11 , 12 Thus, it is notable that alternative end points are usually referred to as “surrogate end points,” yet it is now known that in many circumstances, these end points are not valid surrogates for overall survival or QOL. 8 , 13 , 14 This realization has enormous importance because overall survival and QOL are exceptionally relevant end points for patients. In survey studies, overall survival and QOL are prioritized by patients over gains in progression-free survival. 15 , 16 Furthermore, therapies improving alternative end points may come at the expense of worsening QOL due to treatment adverse effects. The cost of worsening QOL to achieve alternative end-point improvements may be especially problematic when there is no clear overall survival benefit. This contemporary challenge in trial interpretation is exacerbated by the fact that secondary end points, such as QOL, are often not published. 13 , 17 , 18 In light of the increasing use of alternative end points, the frequency of RCTs improving alternative end points and subsequently interpreted as positive with regulatory approval has perhaps overshadowed the frequency of RCTs improving overall survival or QOL. 19 , 20 , 21 In the context of a growing literature advocating for a renewed focus on the patient-centric end points of overall survival and QOL, herein we sought to determine the proportion of phase 3 RCTs that improve alternative end points, overall survival, or QOL and compare these outcomes with the overall trial interpretation. 3 , 12 , 22 , 23 Methods A meta-epidemiological analysis was performed consistent with the modified Preferred Reporting Items for Systematic Reviews and Meta-analyses ( PRISMA ) guidelines. 24 The ClinicalTrials.gov registry was used to screen trials to create a generally representative sample of phase 3 oncology trials. RCTs were identified in May 2024 using the following criteria: for terms, cancer ; for phase, phase 3; for study results, “with results”; and for status, “excluded: not yet recruiting.” Two-group, superiority-design, interventional oncology RCTs were eligible for inclusion. Only trials with a published primary end point were included to avoid trials currently accruing or with immature follow-up. All published articles associated with each trial were reviewed by at least 2 investigators. Alternative end-point and overall survival superiority were defined as the experimental group vs the reference/control group according to the prespecified statistical criteria for each RCT. QOL superiority was defined by either statistical significance or minimum clinically important differences (MCID), consistent with prior work. 10 QOL was classified as global QOL, representing the composite summary measure obtained with the patient-reported outcomes instrument, or domain QOL, referring to measures obtained from specific instrument subscales. Global QOL analyses were further characterized as (1) adjusted for baseline scores vs unadjusted, (2) posttreatment score vs change from baseline, and (3) within-group comparisons only. The associations between trial-level characteristics and outcomes were evaluated using multivariable binary logistic regression models to estimate adjusted odds ratios. Confounders for each multivariable regression were identified by designing directed acyclic graphs with the web-based application DAGitty (eFigure 1 in Supplement 1 ). 25 A P < .05 was considered statistically significant. Analyses were completed in SAS version 9.4 (SAS Institute Inc) and figures were created in R version 4.4.2 (R Foundation for Statistical Computing). Results A total of 791 RCTs, enrolling a total of 555 580 patients, met criteria for inclusion (eFigure 2 and eAppendix in Supplement 1 ). Primary RCT results were published between 2002 and 2024. The median year of primary trial publication was 2017 (IQR, 2014-2020). Overall survival was the primary end point or a co–primary end point in 35% of RCTs (n = 273). The primary end point in 495 RCTs (63%) was an alternative end point; in the remaining 23 RCTs (3%), the primary end point was QOL or was related to toxicity (eTable 1 in Supplement 1 ). The primary end point was met in 53% of the RCTs (n = 420) ( Figure 1 and Figure 2 A). Alternative end-point superiority was shown in 55% of the RCTs (n = 434) ( Table ). Overall, 705 RCTs (89%) had reported overall survival results. Overall survival superiority was shown in 28% of the RCTs (n = 221) ( Figure 1 , Figure 2 B, and Table ). RCTs of metastatic tumors, industry-funded RCTs, and RCTs studying gastrointestinal malignancies were each more likely to demonstrate overall survival superiority (eTable 2 in Supplement 1 ). Notably, industry-funded RCTs tended to be larger than those not funded by industry. Industry-funded RCTs were also more likely to study patients with metastatic cancers, who often exhibit higher mortality rates than patients with nonmetastatic cancers (translating to higher event rates and power to detect overall survival superiority), although our analysis adjusted for this potential confounding. Overall survival superiority was less commonly shown over time ( Figure 2 B; eTable 2 in Supplement 1 ). Among the 434 RCTs with a positive alternative end point, a minority (n = 185 [43%]) showed overall survival superiority. Figure 1. Sankey Diagram of Phase 3 Oncology Randomized Clinical Trial Outcomes According to the Primary End Point, Overall Survival, and Quality of Life (QOL). Open in a new tab Figure 2. Phase 3 Oncology RCTs Achieving Specified Outcomes Over Time. Open in a new tab Results of randomized clinical trials (RCTs) published before 2007 or after 2022 are not shown owing to small sample sizes. The shaded regions represent 95% CIs calculated using the exact binomial method. QOL indicates quality of life. Table. Summary of Eligible Phase 3 Oncology Trial Outcomes in Experimental Group vs Control Group. Outcomes No. (%) of randomized clinical trials Superior Not superior Not reported Primary end point 420 (53) 371 (47) Not applicable a Alternative end point 434 (55) 335 (42) 22 (3) Overall survival 221 (28) 484 (62) 86 (11) Quality of life 84 (11) 189 (24) 518 (66) Open in a new tab a Publication of the primary end point was an inclusion criterion for the current study. Patient-reported outcomes were collected in 482 of 791 RCTs (61%). Authors published QOL results for 340 (43%) and global QOL between-group comparisons for 271 (34%). RCTs of metastatic solid tumors, industry-funded RCTs, and RCTs published after 2014 were more likely to publish global QOL results (eTable 3 in Supplement 1 ). RCTs with a positive primary end point appeared more likely to publish global QOL data than RCTs with a negative primary end point (55% [231/420] vs 29% [109/371]; P < .001). Global QOL analyses used statistical significance testing in 255 trials and MCID in 139 trials (eTable 4 in Supplement 1 ). Among the 271 RCTs with global QOL analyses comparing randomized groups, 48 RCTs adjusted for baseline scores (18%). No trial-level factors were associated with baseline score–adjusted analyses (eTable 5 in Supplement 1 ). Of the 223 RCTs (82%) presenting analyses unadjusted for baseline scores, 105 were change-from-baseline analyses and 118 were posttreatment score comparisons. In 7 RCTs, only within-group results were presented, without comparisons between randomized groups. Global QOL superiority was shown in 11% of RCTs (n = 84) ( Figure 1 , Figure 2 C, and Table ). Eighty RCTs (10%) claimed superiority via statistical significance and 24 RCTs (3%) by MCID criteria (eTable 4 in Supplement 1 ). Global QOL was worsened in the experimental group vs the control group in 28 trials (4%) according to statistical significance criteria and in 3 RCTs (0.4%) according to MCID criteria. Industry-funded RCTs and metastatic solid tumor RCTs were more likely to demonstrate global QOL superiority, whereas cooperative group–sponsored RCTs and larger trials were less likely to demonstrate global QOL superiority (eTable 6 in Supplement 1 ). Among the 434 RCTs with a positive alternative end point, 67 (15%) showed global QOL superiority. In 32% of the RCTs (n = 257), superiority of either overall survival or global QOL was demonstrated. Both overall survival and global QOL superiority were shown in 6% of the RCTs (n = 48) ( Figure 1 and Figure 2 D). In a sensitivity analysis of RCTs with at least 5 years of follow-up after the primary publication, 34% (n = 191) demonstrated superiority of either overall survival or global QOL, and in 6% (n = 35), both overall survival and QOL were improved. Among the 434 RCTs with a positive alternative end point, 210 (48%) exhibited superiority of either overall survival or global QOL and 42 (10%) showed superiority of both overall survival and global QOL. In 31 RCTs, either overall survival or global QOL was inferior, including 16 RCTs in which the primary end point was considered superior. Because treatments may provide benefits by improving some aspects of QOL (eg, physical functioning) but not necessarily global QOL, we performed a sensitivity analysis evaluating QOL subscale domains. In this analysis, 80 RCTs (10%) showed superiority in at least 1 QOL subscale domain. Superiority was claimed by statistical significance in 77 RCTs (10%) and by the MCID in 26 RCTs (3%). The experimental group exhibited worse domain QOL than the control group in 39 RCTs (5%) by statistical definitions and in 9 RCTs (1%) by MCID definitions. QOL domain findings were mixed (ie, both superiority and inferiority of the experimental group across different domains) in 25 RCTs (3%) and 7 RCTs (1%) according to statistical significance and MCID definitions, respectively. Discussion This large-scale meta-epidemiological study of 791 phase 3 RCTs defines the frequency of perceived overall survival and QOL gains in late-phase oncology research. Even though the majority of RCTs are interpreted as positive, improvements in overall survival or QOL remain relatively uncommon and are exceedingly rare in combination. QOL data appear to be both underevaluated and underreported, a finding that is consistent with previous smaller studies. 13 , 17 , 18 Taken together, these data suggest that the heightened focus on alternative end points, now representing the most common primary end point, has overshadowed overall survival and QOL among modern phase 3 RCTs. 3 Building on prior literature, the present study’s findings strongly argue for renewed focus on overall survival and QOL in late-phase oncology trials and in the regulatory approval process. 10 , 22 , 23 Multiple previous studies have evaluated overall survival and QOL outcomes in oncology research, corroborating the findings of the present study, which, to our knowledge, represents the largest such study of phase 3 trials. In 2021, Del Paggio et al 3 documented the remarkable increase in alternative primary end points among 298 oncology RCTs. Furthermore, they found overall survival superiority in 34% of trials, consistent with the present analysis of 791 phase 3 RCTs, although QOL gains were not specifically investigated. In the present study, several factors, including sponsor, stage, and histology, were associated with higher odds of overall survival superiority, which may be related to higher event rates that lead to more well-powered trials, as many phase 3 oncology trials are often underpowered for their primary end point. 26 Other factors not directly studied herein may also play a role, such as design and powering considerations with relatively lower “optimism bias,” more efficient multivariable analysis plans, control group selection, differential censoring, and postprogression therapy assessments. 27 , 28 , 29 , 30 The present study of phase 3 trials is also consistent with the findings of studies evaluating regulatory approvals. For example, Davis et al 31 found that among 68 approved indications by the European Medicines Agency from 2009 to 2013, 35% showed overall survival superiority and 10% showed QOL superiority. Michaeli and Michaeli 32 suggested that most new US Food and Drug Administration (FDA)–approved drugs only marginally extend overall survival. These challenges also apply to drugs approved under the accelerated pathway, although we did not study this particular question in our dataset. A recent study by Liu et al 20 found that 57% of drug-indication pairs failed to demonstrate overall survival or QOL benefit with at least 5 years of follow-up after accelerated FDA approval. Notably, survey data suggest that most older adults would prefer to wait for greater certainty of overall survival benefit than expedited approval based solely on alternative end point benefits. 33 The lack of consistent overall survival benefits in phase 3 trials has a resounding impact on patient care, as phase 3 trials directly inform the regulatory approval process and the subsequent translation to clinical practice. In light of the present data, the findings of Canavan et al 34 are not so surprising, in which overall survival was not longer among clinical practices that use more lines of systemic therapy for advanced disease vs practices using fewer lines. Our findings provide high-level data emphasizing the urgency of improving the focus of late-phase research on patient-centric values, building on recent survey data suggesting that patient QOL should be prioritized in cancer research. 22 , 23 , 35 , 36 In our study, 39% of the RCTs did not collect patient-reported data; moreover, only 34% of the RCTs had published global QOL comparisons, a surprisingly small proportion. Even fewer of the RCTs (11%) perceived global QOL superiority. These findings are particularly worrisome in the context of novel therapies, which are often associated with toxic effects, uncommonly appear to lengthen lifespan, and may impose considerable financial burden on patients. 37 Treating the underlying disease can improve QOL by alleviating tumor-related symptoms, especially in patients with advanced refractory disease. However, there are trade-offs among imaging-based disease control, overall survival, and QOL, as therapies may worsen QOL due to toxic effects. 13 , 38 Thus, to verify whether novel treatments are providing true benefits to patients, QOL data must be collected and reported more routinely in phase 3 RCTs, particularly in disease settings where survival times are limited. Although toxicity and safety data may sometimes serve as a proxy for QOL, clinician-rated toxicity scales often underestimate patient-reported symptom burden, and thus patient-reported data remain essential to collect and publish. 39 , 40 Additionally, QOL analyses merit the same level of prespecified statistical rigor as other end points. Power and precision of QOL analyses can be improved by comparing scores between randomized groups while adjusting for baseline values. 41 , 42 , 43 , 44 When adjusting for baseline scores, both change-from-baseline analyses and direct comparisons of posttreatment scores are appropriate. 45 However, in the present study, we found that the vast majority of QOL analyses did not adjust for baseline scores, which can add significant bias. 43 , 44 In unadjusted analyses, change-from-baseline comparisons may exacerbate bias further relative to analyses that directly compare posttreatment scores when there is a low correlation between pretreatment and posttreatment values. 43 We suspect that the correlation between pretreatment and posttreatment values is often low in many phase 3 oncology trials. Finally, between-group analyses should always be presented in an RCT. Within-group analyses, which were rarely published in isolation without between-group analyses, can introduce substantial bias, diminish statistical power, and overlook the fundamental RCT goal of between-group comparisons. 46 Future trials may leverage existing modern statistical methodology such as generalized pairwise comparisons to combine alternative end points with overall survival and QOL for analysis. 47 , 48 Strengths and Limitations The strengths of this work include the scale and scope of the RCTs assessed, representing a large, modern collection of pivotal late-phase work. A major limitation was underreporting of data, most prominently QOL data, which may add bias to our assessment. 49 , 50 If trials without QOL or overall survival superiority are less likely to be published, our assessment may overestimate the proportion of trials with QOL or overall survival superiority. Another caveat is that overall survival as well as QOL analyses for certain RCTs may be published in the future after additional follow-up. On this possibility, we have previously found that most secondary end points are published within 1 to 4 years of the primary trial publication. 17 Because the median year of primary trial publication in our study was 2017, the effect of this potential bias on the overall findings is likely limited, as further suggested by our sensitivity analysis excluding newer trials and in a prior study of accelerated approvals. 20 Contemporary challenges in accounting for the influence of postprogression therapies when estimating overall survival may further impact our study’s findings of overall survival benefits. 29 , 51 Trials conducted outside the US that were not registered on ClinicalTrials.gov were not included, and thus our estimates may not generalize to trials conducted in those settings. Our findings should not be extrapolated to trials with 3 or more groups, as these trials were not included in our study due to the challenge of interpreting multigroup results at scale. Finally, our study did not differentiate between trials conducted with registrational intent vs not, as this covariate was difficult to reliably define at scale; outcomes may systematically differ in the subset of registrational trials. Conclusions The goal of late-phase oncology research should be to improve outcomes that matter to patients. With research designs increasingly focused on alternative metrics that may satisfy the current regulatory approvals process, modern phase 3 oncology RCTs appear to be severely underachieving in creating improvements that directly affect how patients live and feel. Our data provide novel empirical insights to the landscape of current late-phase research and suggest that trial and regulatory priorities should be realigned toward improving outcomes most relevant to patients. Supplement 1. eFigure 1. Directed acyclic graph relating trial features and outcomes eFigure 2. Trial screening flowchart eTable 1. Randomized clinical trial characteristics eTable 2. Multivariable models for OS superiority eTable 3. Multivariable models for publication of QOL data eTable 4. Global QOL results eTable 5. Multivariable models for between-arm global QOL analyses adjusted for baseline scores eTable 6. Multivariable models for global QOL superiority eAppendix. List of included trials jamaoncol-e251002-s001.pdf (2.4MB, pdf) Supplement 2. Data Sharing Statement jamaoncol-e251002-s002.pdf (44.1KB, pdf) References 1. Kemp R, Prasad V. Surrogate endpoints in oncology: when are they acceptable for regulatory and clinical decisions, and are they currently overused? BMC Med. 2017;15(1):134. doi: 10.1186/s12916-017-0902-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Sherry AD, Corrigan KL, Kouzy R, et al. Prevalence, trends, and characteristics of trials investigating local therapy in contemporary phase 3 clinical cancer research. Cancer. 2023;129(21):3430-3438. doi: 10.1002/cncr.34929 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Del Paggio JC, Berry JS, Hopman WM, et al. Evolution of the randomized clinical trial in the era of precision oncology. JAMA Oncol. 2021;7(5):728-734. doi: 10.1001/jamaoncol.2021.0379 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Chen EY, Haslam A, Prasad V. FDA acceptance of surrogate end points for cancer drug approval: 1992-2019. JAMA Intern Med. 2020;180(6):912-914. doi: 10.1001/jamainternmed.2020.1097 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Abi Jaoude J, Kouzy R, Ghabach M, et al. Food and Drug Administration approvals in phase 3 cancer clinical trials. BMC Cancer. 2021;21(1):695. doi: 10.1186/s12885-021-08457-5 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Pasalic D, McGinnis GJ, Fuller CD, et al. Progression-free survival is a suboptimal predictor for overall survival among metastatic solid tumour clinical trials. Eur J Cancer. 2020;136:176-185. doi: 10.1016/j.ejca.2020.06.015 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Belin L, Tan A, De Rycke Y, Dechartres A. Progression-free survival as a surrogate for overall survival in oncology trials: a methodological systematic review. Br J Cancer. 2020;122(11):1707-1714. doi: 10.1038/s41416-020-0805-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Haslam A, Hey SP, Gill J, Prasad V. A systematic review of trial-level meta-analyses measuring the strength of association between surrogate end-points and overall survival in oncology. Eur J Cancer. 2019;106:196-211. doi: 10.1016/j.ejca.2018.11.012 [ DOI ] [ PubMed ] [ Google Scholar ] 9. Zhao F. Surrogate end points and their validation in oncology clinical trials. J Clin Oncol. 2016;34(13):1436-1437. doi: 10.1200/JCO.2016.66.4581 [ DOI ] [ PubMed ] [ Google Scholar ] 10. Samuel JN, Booth CM, Eisenhauer E, Brundage M, Berry SR, Gyawali B. Association of quality-of-life outcomes in cancer drug trials with survival outcomes and drug class. JAMA Oncol. 2022;8(6):879-886. doi: 10.1001/jamaoncol.2022.0864 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Naci H, Zhang Y, Woloshin S, Guan X, Xu Z, Wagner AK. Overall survival benefits of cancer drugs initially approved by the US Food and Drug Administration on the basis of immature survival data: a retrospective analysis. Lancet Oncol. 2024;25(6):760-769. doi: 10.1016/S1470-2045(24)00152-9 [ DOI ] [ PubMed ] [ Google Scholar ] 12. Booth CM, Eisenhauer EA. Progression-free survival: meaningful or simply measurable? J Clin Oncol. 2012;30(10):1030-1033. doi: 10.1200/JCO.2011.38.7571 [ DOI ] [ PubMed ] [ Google Scholar ] 13. Hwang TJ, Gyawali B. Association between progression-free survival and patients’ quality of life in cancer clinical trials. Int J Cancer. 2019;144(7):1746-1751. doi: 10.1002/ijc.31957 [ DOI ] [ PubMed ] [ Google Scholar ] 14. Kovic B, Jin X, Kennedy SA, et al. Evaluating progression-free survival as a surrogate outcome for health-related quality of life in oncology: a systematic review and quantitative analysis. JAMA Intern Med. 2018;178(12):1586-1596. doi: 10.1001/jamainternmed.2018.4710 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Brundage MD, Booth CM, Eisenhauer EA, et al. Patients’ attitudes and preferences toward delayed disease progression in the absence of improved survival. J Natl Cancer Inst. 2023;115(12):1526-1534. doi: 10.1093/jnci/djad138 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Mohyuddin GR, Chakraborty R, Berger K, et al. Patient preferences on clinical decision-making in myeloma: a 747-patient survey. Blood. 2024;144(suppl 1):2281. doi: 10.1182/blood-2024-208803 [ DOI ] [ Google Scholar ] 17. Beck EJ, Sherry AD, Florez MA, et al. Secondary endpoint utilization and publication rate among phase III oncology trials. Cancer Res Commun. 2024;4(8):2183-2188. doi: 10.1158/2767-9764.CRC-24-0265 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Salas-Vega S, Iliopoulos O, Mossialos E. Assessment of overall survival, quality of life, and safety benefits associated with new cancer medicines. JAMA Oncol. 2017;3(3):382-390. doi: 10.1001/jamaoncol.2016.4166 [ DOI ] [ PubMed ] [ Google Scholar ] 19. Wilson MK, Karakasis K, Oza AM. Outcomes and endpoints in trials of cancer treatment: the past, present, and future. Lancet Oncol. 2015;16(1):e32-e42. doi: 10.1016/S1470-2045(14)70375-4 [ DOI ] [ PubMed ] [ Google Scholar ] 20. Liu IT, Kesselheim AS, Cliff ER. Clinical benefit and regulatory outcomes of cancer drugs receiving accelerated approval. JAMA. 2024;331(17):1471-1479. doi: 10.1001/jama.2024.2396 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Elbaz J, Haslam A, Prasad V. An empirical analysis of overall survival in drug approvals by the US FDA (2006-2023). Cancer Med. 2024;13(8):e7190. doi: 10.1002/cam4.7190 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Booth CM, Sengar M, Goodman A, et al. Common sense oncology: outcomes that matter. Lancet Oncol. 2023;24(8):833-835. doi: 10.1016/S1470-2045(23)00319-4 [ DOI ] [ PubMed ] [ Google Scholar ] 23. Booth CM, Eisenhauer EA, Gyawali B, Tannock IF. Progression-free survival should not be used as a primary end point for registration of anticancer drugs. J Clin Oncol. 2023;41(32):4968-4972. doi: 10.1200/JCO.23.01423 [ DOI ] [ PubMed ] [ Google Scholar ] 24. Murad MH, Wang Z. Guidelines for reporting meta-epidemiological methodology research. Evid Based Med. 2017;22(4):139-142. doi: 10.1136/ebmed-2017-110713 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Textor J, van der Zander B, Gilthorpe MS, Liskiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package “dagitty.” Int J Epidemiol. 2016;45(6):1887-1894. doi: 10.1093/ije/dyw341 [ DOI ] [ PubMed ] [ Google Scholar ] 26. Sherry AD, Msaouel P, Kupferman GS, et al. Evidenced-based prior for estimating the treatment effect of phase III randomized trials in oncology. JCO Precis Oncol. 2024;8:e2400363. doi: 10.1200/PO.24.00363 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Noticewala SS, Patel RR, Ludmir EB. Absence of optimism bias in industry-sponsored cancer trials. J Clin Oncol. 2020;38(15)(suppl):2020. doi: 10.1200/JCO.2020.38.15_suppl.2020 [ DOI ] [ Google Scholar ] 28. Sherry AD, Passy AH, McCaw ZR, et al. Increasing power in phase III oncology trials with multivariable regression: an empirical assessment of 535 primary end point analyses. JCO Clin Cancer Inform. 2024;8:e2400102. doi: 10.1200/CCI.24.00102 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Sherry AD, Msaouel P, Lin TA, et al. Postprogression therapy and confounding for the estimated treatment effect on overall survival in phase III oncology trials. BMJ Oncol. 2024;3(1):e000322. doi: 10.1136/bmjonc-2024-000322 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Hsu EJ, Lin TA, Dabush DR, et al. Association of differential censoring with survival and suboptimal control arms among oncology clinical trials. J Natl Cancer Inst. 2024;116(6):990-994. doi: 10.1093/jnci/djae028 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Davis C, Naci H, Gurpinar E, Poplavska E, Pinto A, Aggarwal A. Availability of evidence of benefits on overall survival and quality of life of cancer drugs approved by European Medicines Agency: retrospective cohort study of drug approvals 2009-13. BMJ. 2017;359:j4530. doi: 10.1136/bmj.j4530 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Michaeli DT, Michaeli T. Overall survival, progression-free survival, and tumor response benefit supporting initial US Food and Drug Administration approval and indication extension of new cancer drugs, 2003-2021. J Clin Oncol. 2022;40(35):4095-4106. doi: 10.1200/JCO.22.00535 [ DOI ] [ PubMed ] [ Google Scholar ] 33. Forrest R, Lagarde M, Aggarwal A, Naci H. Preferences for speed of access versus certainty of the survival benefit of new cancer drugs: a discrete choice experiment. Lancet Oncol. 2024;25(12):1635-1643. doi: 10.1016/S1470-2045(24)00596-5 [ DOI ] [ PubMed ] [ Google Scholar ] 34. Canavan ME, Wang X, Ascha MS, et al. Systemic anticancer therapy and overall survival in patients with very advanced solid tumors. JAMA Oncol. 2024;10(7):887-895. doi: 10.1001/jamaoncol.2024.1129 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Allen CJ, Snyder RA, Horn DM, et al. Defining priorities in value-based cancer care: insights from the Alliance for Clinical Trials in Oncology National Cooperative Group Survey. JCO Oncol Pract. 2023;19(10):932-938. doi: 10.1200/OP.23.00159 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Sanford NN, Hong TS, Hall WA. Elucidating the benefit of radiation therapy in GI cancers: rethinking trial end points and patient selection. J Clin Oncol. 2024;42(8):868-871. doi: 10.1200/JCO.23.01402 [ DOI ] [ PubMed ] [ Google Scholar ] 37. Chino F. High-value cancer care and the problem with surrogate endpoints in the quality/cost equation. J Natl Compr Canc Netw. 2019;17(12):1569-1570. doi: 10.6004/jnccn.2019.7372 [ DOI ] [ PubMed ] [ Google Scholar ] 38. Msaouel P, Lee J, Thall PF. Making patient-specific treatment decisions using prognostic variables and utilities of clinical outcomes. Cancers (Basel). 2021;13(11):2741. doi: 10.3390/cancers13112741 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Atkinson TM, Ryan SJ, Bennett AV, et al. The association between clinician-based Common Terminology Criteria for Adverse Events (CTCAE) and patient-reported outcomes (PRO): a systematic review. Support Care Cancer. 2016;24(8):3669-3676. doi: 10.1007/s00520-016-3297-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Culakova E, Mohamed M, Flannery M, et al. Relationships between patient-reported and clinician-rated toxicities and daily functioning in older adults with advanced cancer undergoing systemic therapy. Cancer. 2025;131(4):e35766. doi: 10.1002/cncr.35766 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Frison L, Pocock SJ. Repeated measures in clinical trials: analysis using mean summary statistics and its implications for design. Stat Med. 1992;11(13):1685-1704. doi: 10.1002/sim.4780111304 [ DOI ] [ PubMed ] [ Google Scholar ] 42. Twisk J, Bosman L, Hoekstra T, Rijnhart J, Welten M, Heymans M. Different ways to estimate treatment effects in randomised controlled trials. Contemp Clin Trials Commun. 2018;10:80-85. doi: 10.1016/j.conctc.2018.03.008 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Vickers AJ, Altman DG. Statistics notes: analysing controlled trials with baseline and follow up measurements. BMJ. 2001;323(7321):1123-1124. doi: 10.1136/bmj.323.7321.1123 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Clifton L, Clifton DA. The correlation between baseline score and post-intervention score, and its implications for statistical analysis. Trials. 2019;20(1):43. doi: 10.1186/s13063-018-3108-3 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Laird N. Further comparative analyses of pretest-posttest research designs. Am Stat. 1983;37(4A):329-330. doi: 10.1080/00031305.1983.10483133 [ DOI ] [ Google Scholar ] 46. Bland JM, Altman DG. Comparisons against baseline within randomised groups are often used and can be highly misleading. Trials. 2011;12:264. doi: 10.1186/1745-6215-12-264 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Buyse M. Generalized pairwise comparisons of prioritized outcomes in the two-sample problem. Stat Med. 2010;29(30):3245-3257. doi: 10.1002/sim.3923 [ DOI ] [ PubMed ] [ Google Scholar ] 48. Verbeeck J, De Backer M, Verwerft J, et al. Generalized pairwise comparisons to assess treatment effects: JACC review topic of the week. J Am Coll Cardiol. 2023;82(13):1360-1372. doi: 10.1016/j.jacc.2023.06.047 [ DOI ] [ PubMed ] [ Google Scholar ] 49. Dickersin K, Chan S, Chalmers TC, Sacks HS, Smith H Jr. Publication bias and clinical trials. Control Clin Trials. 1987;8(4):343-353. doi: 10.1016/0197-2456(87)90155-3 [ DOI ] [ PubMed ] [ Google Scholar ] 50. Franco A, Malhotra N, Simonovits G. Social science. Publication bias in the social sciences: unlocking the file drawer. Science. 2014;345(6203):1502-1505. doi: 10.1126/science.1255484 [ DOI ] [ PubMed ] [ Google Scholar ] 51. Berg SA, La Rosa S, Zhang T, et al. Impact of postprogression therapies on overall survival: recommendations from the 2023 Kidney Cancer Association think tank meeting. Urol Oncol. 2025;43(3):135-146. doi: 10.1016/j.urolonc.2024.10.022 [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplement 1. eFigure 1. Directed acyclic graph relating trial features and outcomes eFigure 2. Trial screening flowchart eTable 1. Randomized clinical trial characteristics eTable 2. Multivariable models for OS superiority eTable 3. Multivariable models for publication of QOL data eTable 4. Global QOL results eTable 5. Multivariable models for between-arm global QOL analyses adjusted for baseline scores eTable 6. Multivariable models for global QOL superiority eAppendix. List of included trials jamaoncol-e251002-s001.pdf (2.4MB, pdf) Supplement 2. Data Sharing Statement jamaoncol-e251002-s002.pdf (44.1KB, pdf) Articles from JAMA Oncology are provided here courtesy of American Medical Association ACTIONS View on publisher site Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• A Study of Eribulin-Based Regimen Versus Other Chemotherpy in Triple-Negative Metastatic Breast Cancer (ERI-Based-01).#871657
• RISK-ADAPT Protocol in Metastatic Hormone-Sensitive Prostate Cancer (mHSPC)#1125943
• Optimal Margin Evaluation of Online Adaptive Radiotherapy for Postoperative Treatment of Endometrial and Cervical Cancer#800375
• Diffuse Large B Cell Non-Hodgkin's Lymphoma in the Vulnerable/Frail Elderly. A Multicentric Randomized Phase II Trial#852942
• Chemotherapy in Treating Patients With Prostate Cancer#1137977