Abstract
Introduction
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 ClinicalTrials.gov 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).
Results
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.
Discussion
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.
Keywords
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References
- Older adult participation in cancer clinical trials: a systematic review of barriers and interventions.CA Cancer J Clin. 2021; 71: 78-92
- Older patients are still under-represented in clinical trials of Alzheimer’s disease.Alzheimer’s Res Therapy. 2016; 8: 1-10
- Inclusion of Older Adults in Cancer Clinical Trials, Draft Guidance for Industry.
- Improving the evidence base for treating older adults with cancer: American Society of Clinical Oncology statement.J Clin Oncol. 2015; 33: 3826-3833
- Geriatric oncology research in the cooperative groups: a report of a SIOG special meeting.J Geriatric Oncol. 2010; 1: 40-44
- Terminated trials in the ClinicalTrials. Gov results database: evaluation of availability of primary outcome data and reasons for termination.PLoS One. 2015; 10e0127242
- Trial-level factors affecting accrual and completion of oncology clinical trials: A systematic review.Contemp Clin Trials Commun. 2021; : 100843
- 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
- Speed of accrual into published phase III oncology trials: A comparison across geographic locations.American Society of Clinical Oncology, 2012
- Premature clinical trial discontinuation in the era of immune checkpoint inhibitors.Oncologist. 2018; 23: 1494-1499
- Variation in accrual and race/ethnicity reporting in urological and nonurological related cancer trials.J Urol. 2019; 202: 385-391
- Failed randomized clinical trials in radiation oncology: what can we learn?.Intern J Rad Oncol Biol Phys. 2018; 101: 1018-1024
- 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
- Adult cancer clinical trials that fail to complete: an epidemic?.JNCI: J Nat Cancer Inst. 2014; 106
- Accrual experience of National Cancer Institute cooperative group phase III trials activated from 2000 to 2007.J Clin Oncol. 2010; 28: 5197
- Predicting low accrual in the National Cancer Institute’s cooperative group clinical trials.JNCI: J Nat Cancer Inst. 2016; 108
- 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
- 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
- Modernizing eligibility criteria for molecularly driven trials.J Clin Oncol. 2015; 33: 2815-2820
- Characterization of comorbidities limiting the recruitment of patients in early phase clinical trials.Oncologist. 2019; 24: 96-102
- Impact of prior cancer on eligibility for lung cancer clinical trials.J Natl Cancer Inst. 2014; 106: dju302
- 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
- What is AACT?.
- ClinicalTrials.gov, Trends Charts, and Maps.
- U.S. National Library of Medicine, FDAAA 801 and the Final Rule.
- 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
- Inclusion of elderly patients in oncology clinical trials.Ann Oncol. 2016; 27: 1799-1804
- Unsuccessful trial accrual and human subjects protections: an empirical analysis of recently closed trials.Clin Trials. 2015; 12: 77-83
- Assessing genitourinary cancer clinical trial accrual sufficiency using archived trial data.JCO Clin Cancer Inform. 2020; 4: 614-622
- 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
- Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment.Medicina. 2020; 56: 455
- An asymptotic equivalence of choice of model by cross-validation and Akaike’s criterion.J R Stat Soc B Methodol. 1977; 39: 44-47
- Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration.Ann Intern Med. 2015; 162: W1-W73
- Why we should be wary of single-center trials.Crit Care Med. 2009; 37: 3114-3119
- Clinical studies: Europe or the United States?.Med Dev Diagn Indus. 2004; 26: 134-142
Article info
Publication history
Published online: November 25, 2022
Accepted:
November 14,
2022
Received in revised form:
September 16,
2022
Received:
July 21,
2022
Identification
Copyright
© 2022 Elsevier Ltd. All rights reserved.
