Research Paper| Volume 14, ISSUE 1, 101404, January 2023

Can we predict trial failure among older adult-specific clinical trials using trial-level factors?

Published:November 25, 2022DOI:



      Conducting older adult-specific clinical trials can help overcome the lack of clinical evidence for older adults due to their underrepresentation in clinical trials. Understanding factors contributing to the successful completion of such trials can help trial sponsors and researchers prioritize studies and optimize study design. We aimed to develop a model that predicts trial failure among older adult-specific cancer clinical trials using trial-level factors.

      Materials and methods

      We identified phase 2–4 interventional cancer clinical trials that ended between 2008 and 2019 and had the minimum age limit of 60 years old or older using Aggregate Analysis of data. We defined trial failure as closed early for reasons other than interim results or toxicity or completed with a sample of <85% of the targeted size. Candidate trial-level predictors were identified from a literature review. We evaluated eight types of machine learning algorithms to find the best model. Model fitting and testing were performed using 5-fold nested cross-validation. We evaluated the model performance using the area under receiver operating characteristic curve (AUROC).


      Of 209 older adult-specific clinical trials, 87 were failed trials per the definition of trial failure. The model with the highest AUROC in the validation set was the least absolute shrinkage and selection operator (AUROC in the test set = 0.70; 95% confidence interval [CI]: 0.53, 0.86). Trial-level factors included in the best model were the study sponsor, the number of participating centers, the number of modalities, the level of restriction on performance score, study location, the number of arms, life expectancy restriction, and the number of target size. Among these factors, the number of centers (odds ratio [OR] = 0.83, 95% CI: 0.71, 0.94), study being in non-US only vs. US only (OR = 0.32, 95% CI: 0.12, 0.82), and life expectancy restriction (OR = 2.17, 95% CI: 1.04, 4.73) were significantly associated with the trial failure.


      We identified trial-level factors predictive of trial failure among older adult-specific clinical trials and developed a prediction model that can help estimate the risk of failure before a study is conducted. The study findings could aid in the design and prioritization of future older adult-specific clinical trials.


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        • Sedrak M.S.
        • Freedman R.A.
        • Cohen H.J.
        • Muss H.B.
        • Jatoi A.
        • Klepin H.D.
        • et al.
        Older adult participation in cancer clinical trials: a systematic review of barriers and interventions.
        CA Cancer J Clin. 2021; 71: 78-92
        • Banzi R.
        • Camaioni P.
        • Tettamanti M.
        • Lucca U.
        Older patients are still under-represented in clinical trials of Alzheimer’s disease.
        Alzheimer’s Res Therapy. 2016; 8: 1-10
        • The Food and Drug Administration
        Inclusion of Older Adults in Cancer Clinical Trials, Draft Guidance for Industry.
        • Hurria A.
        • Levit L.A.
        • Dale W.
        • Mohile S.G.
        • Muss H.B.
        • Fehrenbacher L.
        • et al.
        Improving the evidence base for treating older adults with cancer: American Society of Clinical Oncology statement.
        J Clin Oncol. 2015; 33: 3826-3833
        • Hurria A.
        • Cohen H.J.
        • Extermann M.
        Geriatric oncology research in the cooperative groups: a report of a SIOG special meeting.
        J Geriatric Oncol. 2010; 1: 40-44
        • Williams R.J.
        • Tse T.
        • DiPiazza K.
        • Zarin D.A.
        Terminated trials in the ClinicalTrials. Gov results database: evaluation of availability of primary outcome data and reasons for termination.
        PLoS One. 2015; 10e0127242
        • Hauck C.L.
        • Kelechi T.J.
        • Cartmell K.B.
        • Mueller M.
        Trial-level factors affecting accrual and completion of oncology clinical trials: A systematic review.
        Contemp Clin Trials Commun. 2021; : 100843
        • Fogel D.B.
        Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review.
        Contemp Clin Trials Commun. 2018; 11: 156-164
        • Ruther N.R.
        • Jumonville A.
        • Mathiason M.A.
        • Emmel A.E.
        • Wee S.K.
        • Go R.S.
        Speed of accrual into published phase III oncology trials: A comparison across geographic locations.
        American Society of Clinical Oncology, 2012
        • Khunger M.
        • Rakshit S.
        • Hernandez A.V.
        • Pasupuleti V.
        • Glass K.
        • Galsky M.D.
        • et al.
        Premature clinical trial discontinuation in the era of immune checkpoint inhibitors.
        Oncologist. 2018; 23: 1494-1499
        • Paul K.
        • Sathianathen N.
        • Dahm P.
        • Le C.
        • Konety B.R.
        Variation in accrual and race/ethnicity reporting in urological and nonurological related cancer trials.
        J Urol. 2019; 202: 385-391
        • Nguyen T.K.
        • Nguyen E.K.
        • Warner A.
        • Louie A.V.
        • Palma D.A.
        Failed randomized clinical trials in radiation oncology: what can we learn?.
        Intern J Rad Oncol Biol Phys. 2018; 101: 1018-1024
        • Cheng S.K.
        • Dietrich M.S.
        • Dilts D.M.
        A sense of urgency: evaluating the link between clinical trial development time and the accrual performance of cancer therapy evaluation program (NCI-CTEP) sponsored studies.
        Clin Cancer Res. 2010; 16: 5557-5563
        • Stensland K.D.
        • McBride R.B.
        • Latif A.
        • Wisnivesky J.
        • Hendricks R.
        • Roper N.
        • et al.
        Adult cancer clinical trials that fail to complete: an epidemic?.
        JNCI: J Nat Cancer Inst. 2014; 106
        • Korn E.L.
        • Freidlin B.
        • Mooney M.
        • Abrams J.S.
        Accrual experience of National Cancer Institute cooperative group phase III trials activated from 2000 to 2007.
        J Clin Oncol. 2010; 28: 5197
        • Bennette C.S.
        • Ramsey S.D.
        • McDermott C.L.
        • Carlson J.J.
        • Basu A.
        • Veenstra D.L.
        Predicting low accrual in the National Cancer Institute’s cooperative group clinical trials.
        JNCI: J Nat Cancer Inst. 2016; 108
        • Lyss A.P.
        • Lilenbaum R.C.
        Accrual to National Cancer Institute—sponsored non–small-cell lung Cancer trials: insights and contributions from the CCOP program.
        Clin Lung Cancer. 2009; 10: 410-413
        • Massett H.A.
        • Mishkin G.
        • Rubinstein L.
        • Ivy S.P.
        • Denicoff A.
        • Godwin E.
        • et al.
        Challenges facing early phase trials sponsored by the National Cancer Institute: an analysis of corrective action plans to improve accrual.
        Clin Cancer Res. 2016; 22: 5408-5416
        • Kim E.S.
        • Bernstein D.
        • Hilsenbeck S.G.
        • Chung C.H.
        • Dicker A.P.
        • Ersek J.L.
        • et al.
        Modernizing eligibility criteria for molecularly driven trials.
        J Clin Oncol. 2015; 33: 2815-2820
        • Duma N.
        • Kothadia S.M.
        • Azam T.U.
        • Yadav S.
        • Paludo J.
        • Vera Aguilera J.
        • et al.
        Characterization of comorbidities limiting the recruitment of patients in early phase clinical trials.
        Oncologist. 2019; 24: 96-102
        • Gerber D.E.
        • Laccetti A.L.
        • Xuan L.
        • Halm E.A.
        • Pruitt S.L.
        Impact of prior cancer on eligibility for lung cancer clinical trials.
        J Natl Cancer Inst. 2014; 106: dju302
        • Hernandez-Torres C.
        • Cheung W.Y.
        • Kong S.
        • O’Callaghan C.J.
        • Hsu T.
        Accrual of older adults to cancer clinical trials led by the Canadian cancer trials group–is trial design a barrier?.
        J Geriatric Oncol. 2020; 11: 455-462
        • Clinical Trials Transformation Initiative
        What is AACT?.
        • US National Library of Medicine, Trends Charts, and Maps.
        • The National Institute of Health
        U.S. National Library of Medicine, FDAAA 801 and the Final Rule.
        • De Glas N.
        • Hamaker M.
        • Kiderlen M.
        • De Craen A.
        • Mooijaart S.
        • Van De Velde C.
        • et al.
        Choosing relevant endpoints for older breast cancer patients in clinical trials: an overview of all current clinical trials on breast cancer treatment.
        Breast Cancer Res Treat. 2014; 146: 591-597
        • Le Saux O.
        • Falandry C.
        • Gan H.K.
        • You B.
        • Freyer G.
        • Peron J.
        Inclusion of elderly patients in oncology clinical trials.
        Ann Oncol. 2016; 27: 1799-1804
        • Carlisle B.
        • Kimmelman J.
        • Ramsay T.
        • MacKinnon N.
        Unsuccessful trial accrual and human subjects protections: an empirical analysis of recently closed trials.
        Clin Trials. 2015; 12: 77-83
        • Stensland K.
        • Kaffenberger S.
        • Canes D.
        • Galsky M.
        • Skolarus T.
        • Moinzadeh A.
        Assessing genitourinary cancer clinical trial accrual sufficiency using archived trial data.
        JCO Clin Cancer Inform. 2020; 4: 614-622
        • Schroen A.T.
        • Petroni G.R.
        • Wang H.
        • Thielen M.J.
        • Gray R.
        • Benedetti J.
        • et al.
        Achieving sufficient accrual to address the primary endpoint in phase III clinical trials from US cooperative oncology groups.
        Clin Cancer Res. 2012; 18: 256-262
        • Rajula H.S.R.
        • Verlato G.
        • Manchia M.
        • Antonucci N.
        • Fanos V.
        Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment.
        Medicina. 2020; 56: 455
        • Stone M.
        An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion.
        J R Stat Soc B Methodol. 1977; 39: 44-47
        • Moons K.G.
        • Altman D.G.
        • Reitsma J.B.
        • Ioannidis J.P.
        • Macaskill P.
        • Steyerberg E.W.
        • et al.
        Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration.
        Ann Intern Med. 2015; 162: W1-W73
        • Bellomo R.
        • Warrillow S.J.
        • Reade M.C.
        Why we should be wary of single-center trials.
        Crit Care Med. 2009; 37: 3114-3119
        • Stark N.
        • Peacock J.
        Clinical studies: Europe or the United States?.
        Med Dev Diagn Indus. 2004; 26: 134-142