What drives cancer clinical trial accrual? An empirical analysis of studies leading to FDA authorisation (2015-2020)

Patient Enrollment per Month (Accrual) in Clinical Trials Leading to the FDA Approval of New Cancer Drugs

BACKGROUND

Insufficient patient enrollment per month (=accrual) is the leading cause of cancer trial termination.

OBJECTIVE

To identify and quantify factors associated with patient accrual in trials leading to the US Food and Drug Administration (FDA) approval of new cancer drugs.

DATA

All anti-cancer drugs with FDA approval were identified in the Drugs@FDA database (2000-2022). Data on drug indication's background-, treatment-, disease-, and trial-related factors were collected from FDA labels, clinicaltrials.gov, and the Global Burden of Disease study. The association between patient accrual and collected variables was assessed in Poisson regression models reporting adjusted rate ratios (aRR).

RESULTS

We identified 170 drugs with approval in 455 cancer indications on the basis of 292 randomized and 163 single-arm trials. Among randomized trials, median enrollment per month was 38 patients (interquartile range [IQR]: 26-54) for non-orphan, 21 (IQR: 15-38, aRR 0.88, p = 0.361) for common orphan, 20 (IQR: 10-35, aRR 0.73, p <0.001) for rare orphan, and 8 (IQR 6-12, aRR 0.30, p < 0.001) for ultra-rare orphan indications. Patient enrollment was positively associated with disease burden [aRR: 1.0003 per disability-adjusted life year (DALY), p < 0.001), trial sites (aRR: 1.001 per site, p < 0.001), participating countries (aRR: 1.02 per country, p < 0.001), and phase 3 vs. 1/2 trials (aRR: 1.64, p = 0.037). Enrollment was negatively associated with advanced-line vs. first-line treatments (aRR: 0.81, p = 0.010) and monotherapy vs. combination treatments (aRR: 0.80, p = 0.007). Patient enrollment per month was similar between indications with and without a biomarker (median: 27 vs. 32, aRR 0.80, p = 0.117). Patient enrollment per month was substantially lower in government-sponsored than industry-sponsored trials (median: 14 vs. 32, aRR 0.80, p = 0.209). Enrollment was not associated with randomization ratios, crossover, and study blinding.

CONCLUSIONS

Disease incidence and disease burden alongside the number of study sites and participating countries are the main drivers of patient enrollment in clinical trials. For rare disease trials, greater financial incentives could help expedite patient enrollment. Novel trial design features, including skewed randomization, crossover, or open-label masking, did not entice patient enrollment.

Two-to-One Randomization: Rarely Advisable

In a randomized clinical trial, instead of allocating patients equally between the treatment arms, some trials in oncology assign a higher proportion of patients to receive the experimental treatment arm (eg, a two-to-one randomization). In this commentary, we first briefly review the common reasons given for the use of a two-to-one randomization and provide some examples of trials using these designs. We then explain why the risk-benefit ratio of this approach may not be favorable as is commonly assumed.

Radiation therapy with phenotypic medicine: towards N-of-1 personalization

In current clinical practice, radiotherapy (RT) is prescribed as a pre-determined total dose divided over daily doses (fractions) given over several weeks. The treatment response is typically assessed months after the end of RT. However, the conventional one-dose-fits-all strategy may not achieve the desired outcome, owing to patient and tumor heterogeneity. Therefore, a treatment strategy that allows for RT dose personalization based on each individual response is preferred. Multiple strategies have been adopted to address this challenge. As an alternative to current known strategies, artificial intelligence (AI)-derived mechanism-independent small data phenotypic medicine (PM) platforms may be utilized for N-of-1 RT personalization. Unlike existing big data approaches, PM does not engage in model refining, training, and validation, and guides treatment by utilizing prospectively collected patient's own small datasets. With PM, clinicians may guide patients' RT dose recommendations using their responses in real-time and potentially avoid over-treatment in good responders and under-treatment in poor responders. In this paper, we discuss the potential of engaging PM to guide clinicians on upfront dose selections and ongoing adaptations during RT, as well as considerations and limitations for implementation. For practicing oncologists, clinical trialists, and researchers, PM can either be implemented as a standalone strategy or in complement with other existing RT personalizations. In addition, PM can either be used for monotherapeutic RT personalization, or in combination with other therapeutics (e.g. chemotherapy, targeted therapy). The potential of N-of-1 RT personalization with drugs will also be presented.

Accrual and statistical power failure in published adjuvant phase III oncology trials: a comprehensive analysis from 2013 to 2023

BACKGROUND

In a competitive landscape with many ongoing adjuvant randomised controlled trials (RCTs), the prevalence of trials that failed to recruit their targeted sample size and were inadequately powered is unclear. The aims of the study are (i) to determine the percentage of trials with accrual and statistical power failure and (ii) to evaluate their potential impact on the drug development process.

MATERIALS AND METHODS

A systematic review was carried out to identify adjuvant phase III oncology RCTs reported between 2013 and 2023 across all solid tumours. No restrictions were applied regarding the type of intervention or journal of publication. The percentage of trials with accrual failure and power failure was estimated as well as their association with the efficacy endpoints. Logistic regression models were used to estimate the odds ratio (OR) and its 95% confidence interval (CI).

RESULTS

A total of 282 RCTs met the inclusion criteria with a median sample size of 661 patients and a median accrual period of 4.3 years. Most of these studies were superiority trials (83.0%). Accrual failure was observed in 22.0% of the studies, finishing recruitment without achieving the targeted sample size. Overall, 39.7% of the studies experienced power failure, having less power than specified in the protocol at the date of the read-out. Among superiority RCTs evaluating intermediate survival endpoints, only 31.1% presented statistically significant results. Trials with power failure were less likely to present statistically significant results (37.9% versus 21.9%, P = 0.04). The association was consistent across all cancer types. In the subset of non-inferiority trials, 35.0% formally demonstrated non-inferiority of the experimental arm.

CONCLUSIONS

Nearly 40% of adjuvant phase III RCTs experienced power failure, and the reduction in power significantly impacted the final study results. There is a need for procedural refinements in the design and implementation of future adjuvant RCTs to mitigate these fallacies.

Bayesian Interim Analysis and Efficiency of Phase III Randomized Trials

IMPORTANCE

Improving the efficiency of interim assessments in phase III trials should reduce trial costs, hasten the approval of efficacious therapies, and mitigate patient exposure to disadvantageous randomizations.

OBJECTIVE

We hypothesized that in silico Bayesian early stopping rules improve the efficiency of phase III trials compared with the original frequentist analysis without compromising overall interpretation.

DESIGN

Cross-sectional analysis.

SETTING

230 randomized phase III oncology trials enrolling 184,752 participants.

PARTICIPANTS

Individual patient-level data were manually reconstructed from primary endpoint Kaplan-Meier curves.

INTERVENTIONS

Trial accruals were simulated 100 times per trial and leveraged published patient outcomes such that only the accrual dynamics, and not the patient outcomes, were randomly varied.

MAIN OUTCOMES AND MEASURES

Early stopping was triggered per simulation if interim analysis demonstrated ≥ 85% probability of minimum clinically important difference/3 for efficacy or futility. Trial-level early closure was defined by stopping frequencies ≥ 0.75.

RESULTS

A total of 12,451 simulations (54%) met early stopping criteria. Trial-level early stopping frequency was highly predictive of the published outcome (OR, 7.24; posterior probability of association, >99.99%; AUC, 0.91; P < 0.0001). Trial-level early closure was recommended for 82 trials (36%), including 62 trials (76%) which had performed frequentist interim analysis. Bayesian early stopping rules were 96% sensitive (95% CI, 91% to 98%) for detecting trials with a primary endpoint difference, and there was a high level of agreement in overall trial interpretation (Bayesian Cohen's κ, 0.95; 95% CrI, 0.92 to 0.99). However, Bayesian interim analysis was associated with >99.99% posterior probability of reducing patient enrollment requirements ( P < 0.0001), with an estimated cumulative enrollment reduction of 20,543 patients (11%; 89 patients averaged equally over all studied trials) and an estimated cumulative cost savings of 851 million USD (3.7 million USD averaged equally over all studied trials).

CONCLUSIONS AND RELEVANCE

Bayesian interim analyses may improve randomized trial efficiency by reducing enrollment requirements without compromising trial interpretation. Increased utilization of Bayesian interim analysis has the potential to reduce costs of late-phase trials, reduce patient exposures to ineffective therapies, and accelerate approvals of effective therapies.

KEY POINTS

Question: What are the effects of Bayesian early stopping rules on the efficiency of phase III randomized oncology trials?Findings: Individual-patient level outcomes were reconstructed for 184,752 patients from 230 trials. Compared with the original interim analysis strategy, in silico Bayesian interim analysis reduced patient enrollment requirements and preserved the original trial interpretation. Meaning: Bayesian interim analysis may improve the efficiency of conducting randomized trials, leading to reduced costs, reduced exposure of patients to disadvantageous treatments, and accelerated approval of efficacious therapies.

Challenges for quality and utilization of real-world data for diffuse large B-cell lymphoma in REALYSA, a LYSA cohort

ABSTRACT

Real-world data (RWD) are essential to complement clinical trial (CT) data, but major challenges remain, such as data quality. REal world dAta in LYmphoma and Survival in Adults (REALYSA) is a prospective noninterventional multicentric cohort started in 2018 that included patients newly diagnosed with lymphoma in France. Herein is a proof-of-concept analysis on patients with first-line diffuse large B-cell lymphoma (DLBCL) to (1) evaluate the capacity of the cohort to provide robust data through a multistep validation process; (2) assess the consistency of the results; and (3) conduct an exploratory transportability assessment of 2 recent phase 3 CTs (POLARIX and SENIOR). The analysis population comprised 645 patients with DLBCL included before 31 March 2021 who received immunochemotherapy and for whom 3589 queries were generated, resulting in high data completeness (<4% missing data). Median age was 66 years, with mostly advanced-stage disease and high international prognostic index (IPI) score. Treatments were mostly rituximab, cyclophosphamide, doxorubicin hydrochloride, vincristine, and prednisone (R-CHOP 75%) and reduced dose R-CHOP (13%). Estimated 1-year event-free survival (EFS) and overall survival rates were 77.9% and 90.0%, respectively (median follow-up, 9.9 months). Regarding transportability, when applying the CT's main inclusion criteria (age, performance status, and IPI), outcomes seemed comparable between patients in REALYSA and standard arms of POLARIX (1-year progression-free survival 79.8% vs 79.8%) and SENIOR (1-year EFS, 64.5% vs 60.0%). With its rigorous data validation process, REALYSA provides high-quality RWD, thus constituting a platform for numerous scientific purposes. The REALYSA study was registered at www.clinicaltrials.gov as #NCT03869619.