Trial Designs In Oncology Research Articles

Augmenting Prognostication: Utilizing Activity Trackers to Enhance Survival Prediction in Metastatic Non-Small Cell Lung Cancer

Introduction/BackgroundPrognostication by Performance Status (PS) assessment is a fundamental element of treatment decisions and clinical trial design in oncology, but it is limited by subjectivity and potential miscommunication between patient, physician, and family. Activity tracker offers the potential to collect a broad range of patient-generated data to supplement the assessment of PS. Patients and MethodsPatients with metastatic NSCLC (mNSCLC) participated in a single institute, prospective, observational feasibility study conducted at Huntsman Cancer Institute. Patients were given a Fitbit® activity tracker, which collects their steps taken, distance moved, heart rate, and activity intensity. At baseline, PS was assessed by physicians and patients, and demographics and clinical data were collected. We defined novel indices of health: Heart rate Activity zone Mismatch (HAM) and excessive Sedentary Heart Rate (eSHR). We used multivariable Cox proportional hazards models adjusted for age, sex, and treatment line to estimate and test the prognostic ability of clinical and fitness metrics on overall survival (OS). Each prognostic model was evaluated using Harrell's concordance index (C-index). ResultsFifty-five patients with mNSCLC were enrolled. The median OS was 10.4 months (95% CI: 7.2, 15.2). PS-physician (HR=2.0; p<0.001) and Fitbit® metrics were associated with OS, including daily total steps (1,000-steps) (HR=0.8; p=0.004), HAM (HR=2; p=0.02), eSHR (HR=0.3; p=0.001). The prognostic model that includes PS-physician was associated with the best concordance (C-index=0.75), followed by daily total distance (C-index=0.74) and steps (C-index=0.73) ConclusionsTracker-based measures were prognostic of survival in mNSCLC and may be useful as a supplement or alternative to PS in practice and clinical trials. Micro AbstractPerformance status (PS) is the foundation for clinical trial eligibility and the basis for treatment decisions in thoracic oncology, but it is limited by subjectivity and potential miscommunication between patient, physician, and family. Activity trackers offer a collection of a broad range of patient-generated data that can be used to supplement PS in clinical trials and practice while minimizing subjectivity.

Biomarker-Driven Oncology Trial Design and Subgroup Characterization: Challenges and Potential Solutions.

In oncology drug development, using biomarkers to select a study population more likely to benefit from a therapeutic effect is critical to increase the efficiency of a clinical trial in demonstrating effectiveness. This perspective delves into therapeutic product approvals that were tested in pivotal trials with all-comers populations, but ultimately received US Food and Drug Administration approval for use within specific patient subgroups identified by biomarkers. Despite initial designs for efficacy and safety assessments in overall populations, a favorable benefit-risk assessment was primarily established in biomarker-positive subgroups. Analyzing these cases, we summarize key considerations pivotal to totality of evidence for regulatory benefit-risk assessments for biomarker-defined subgroup versus all-comers approvals, including biological and clinical rationales, biomarker prevalence, safety data, overall trial design, and subgroup efficacy characterization. Furthermore, a decision tree is proposed to guide optimal clinical trial design, delineating between patient enrichment and stratification, accounting for key factors including biological and clinical rationale, marker type (discreate or continuous), prevalence, assay readiness, and turnaround times for marker assessment. Finally, a recommended approach for subgroup characterization involves prespecifying magnitude of improvement that would be considered clinically meaningful in the biomarker-negative subgroup, which can be supplemented with methodologies such as Bayesian to incorporate evidence from similar studies when available. In summary, this perspective underscores the importance of clinical trial innovations, statistical methodologies and regulatory considerations, to optimize biomarker-driven drug development for patients with cancer.

Pragmaticism in Cancer Clinical Trials.

Clinical trials are essential for advancing oncology treatment strategies and have contributed significantly to the decline in cancer mortality rates over the past decades. Traditional explanatory trials, focused on establishing intervention efficacy in ideal settings, often lack generalizability and may not reflect real-world patient care scenarios. Furthermore, increasing complexity in cancer clinical trial design has led to challenges such as protocol deviations, slow enrollment leading to lengthened durations of trial, and escalating costs. By contrast, pragmatic trials aim to assess intervention effectiveness in more representative patient populations under routine clinical conditions. Here, we review the principles, methodologies, challenges, and advantages of incorporating pragmatic features (PFs) into cancer clinical trials. We illustrate the application of pragmatic trial designs in oncology and discuss the QUASAR collaborative, TAPUR study, and the ongoing PRAGMATICA-LUNG trial. Although not all oncology trials may be amenable to adopting fully pragmatic designs, integration of PFs when feasible will enhance trial generalizability and real-world applicability. Project Pragmatica and similar initiatives advocate for the integration of real-world practice with clinical trials, fostering a nuanced approach to oncology research that balances efficacy and effectiveness assessments, ultimately with a goal of improving patient outcomes.

A generalized Bayesian optimal interval design for dose optimization in immunotherapy.

For novel immuno-oncology therapies, the primary purpose of a dose-finding trial is to identify an optimal dose (OD), defined as the tolerable dose having adequate efficacy and immune response under the unpredictable dose-outcome (toxicity, efficacy, and immune response) relationships. In addition, the multiple low or moderate-grade toxicities rather than dose-limiting toxicities (DLTs) and multiple levels of efficacy should be evaluated differently in dose-finding to determine true OD for developing novel immuno-oncology therapies. We proposed a generalized Bayesian optimal interval design for immunotherapy, simultaneously considering efficacy and toxicity grades and immune response outcomes. The proposed design, named gBOIN-ETI design, is model-assisted and easy to implement to develop immunotherapy efficiently. The operating characteristics of the gBOIN-ETI are compared with other dose-finding trial designs in oncology by simulation across various realistic settings. Our simulations show that the gBOIN-ETI design could outperform the other available approaches in terms of both the percentage of correct OD selection and the average number of patients allocated to the OD across various realistic trial settings.

A multistate platform model for time-to-event endpoints in oncology clinical trials.

A multistate platform model was developed to describe time-to-event (TTE) endpoints in an oncology trial through the following states: initial, tumor response (TR), progressive disease (PD), overall survival (OS) event (death), censor to the last evaluable tumor assessment (progression-free survival [PFS] censor), and censor to study end (OS censor), using an ordinary differential equation framework. Two types of piecewise functions were used to describe the hazards for different events. Piecewise surge functions were used for events that require tumor assessments at the scheduled study visit times (TR, PD, and PFS censor). Piecewise constant functions were used to describe hazards for events that occur evenly throughout the study (OS event and OS censor). The multistate TTE model was applied to describe TTE endpoints from a published phase III study. The piecewise surge functions well-described the observed surges of hazards/events for TR, PD, PFS, and OS occurring near scheduled tumor assessments and showed good agreement with all Kaplan-Meier curves. With the flexibility of piecewise hazard functions, the model was able to evaluate covariate effects in a time-variant fashion to better understand the temporal patterns of disease prognosis through different disease states. This model can be applied to advance the field of oncology trial design and optimization by: (1) enabling robust estimations of baseline hazards and covariate effects for multiple TTE endpoints, (2) providing a platform model for understanding the composition and correlations between different TTE endpoints, and (3) facilitating oncology trial design optimization through clinical trial simulations.

Information fraction estimation: Strategies for a phase 3 non-inferiority maximum duration design with time to event outcome.

There is considerable debate surrounding the choice of methods to estimate information fraction for futility monitoring in a randomized non-inferiority maximum duration trial. This question was motivated by a pediatric oncology study that aimed to establish non-inferiority for two primary outcomes. While non-inferiority was determined for one outcome, the futility monitoring of the other outcome failed to stop the trial early, despite accumulating evidence of inferiority. For a one-sided trial design for which the intervention is inferior to the standard therapy, futility monitoring should provide the opportunity to terminate the trial early. Our research focuses on the Total Control Only (TCO) method, which is defined as a ratio of observed events to total events exclusively within the standard treatment regimen. We investigate its properties in stopping a trial early in favor of inferiority. Simulation results comparing the TCO method with alternative methods, one based on the assumption of an inferior treatment effect (TH0), and the other based on a specified hypothesis of a non-inferior treatment effect (THA), were provided under various pediatric oncology trial design settings. The TCO method is the only method that provides unbiased information fraction estimates regardless of the hypothesis assumptions and exhibits a good power and a comparable type I error rate at each interim analysis compared to other methods. Although none of the methods is uniformly superior on all criteria, the TCO method possesses favorable characteristics, making it a compelling choice for estimating the information fraction when the aim is to reduce cancer treatment-related adverse outcomes.

Comparison of tumor-agnostic and tumor-specific clinical oncology trial designs: a systematic review and meta-analysis.

Aim: To examine whether tumor-specific and tumor-agnostic oncology trials produce comparable estimates of objective response rate (ORR) in BRAF-altered cancers. Materials & methods: Electronic database searches were performed to identify phase I-III clinical trials testing tyrosine kinase inhibitors from 2000 to 2021. A random-effects model was used to pool ORRs. A total of22 cohorts from five tumor-agnostic trialsand 41 cohorts from 27 tumor-specific trials had published ORRs. Results: There was no significant difference between pooled ORRs from either trial design for multitumor analyses (37 vs 50%; p=0.05); thyroid cancer (57 vs 33%; p=0.10); non-small-cell lung cancer (39 vs 53%; p=0.18); or melanoma (55 vs 51%; p=0.58). Conclusion: For BRAF-altered advanced cancers, tumor-agnostic trials do not yield substantially different results from tumor-specific trials.

Unlocking multidimensional cancer therapeutics using geometric data science

Personalised approaches to cancer therapeutics primarily involve identification of patient sub-populations most likely to benefit from targeted drugs. Such a stratification has led to plethora of designs of clinical trials that are often too complex due to the need for incorporating biomarkers and tissue types. Many statistical methods have been developed to address these issues; however, by the time such methodology is available research in cancer has moved on to new challenges and therefore in order to avoid playing catch-up it is necessary to develop new analytic tools alongside. One of the challenges facing cancer therapy is to effectively and appropriately target multiple therapies for sensitive patient population based on a panel of biomarkers across multiple cancer types, and matched future trial designs. We present novel geometric methods (mathematical theory of hypersurfaces) to visualise complex cancer therapeutics data as multidimensional, as well as geometric representation of oncology trial design space in higher dimensions. The hypersurfaces are used to describe master protocols, with application to a specific example of a basket trial design for melanoma, and thus setup a framework for further incorporating multi-omics data as multidimensional therapeutics.

Analysis of patient-reported outcomes in the approval of novel oncology drugs in the United States, 2017–2022

With the growing notion of patient-focused drug development, the quality of life and other patient-reported outcomes (PROs) of cancer patients are gaining considerable attention. Several drug regulatory agencies, including the U.S. Food and Drug Administration (FDA), are calling attention to PROs. This review aims to comprehensively characterise the application of PROs and regulatory considerations for PROs in the FDA-approved novel oncology drugs. The FDA review documents and labels for novel oncology drugs approved from July 2017 to July 2022 were retrieved. We collected and analysed drug approval information, types of endpoints for PROs, PRO measures, designs of trials including PROs, and regulatory comments on PRO-related contents. Results demonstrated that PROs were used more commonly for solid tumours than hematologic malignancies, which might be correlated with the disease characteristics. We further categorised and analysed existing PRO measures, providing insight for tool selection in future oncology trial design. Our findings also indicated that PROs currently do not play a significant role in oncology drug approvals. The major deficiencies related to PROs commented on by FDA reviewers were analysed, followed by recommendations for improvements. This review demonstrates that PROs currently do not play a significant role in oncology drug marketing review, and how they can be used to support the approval of new oncology drugs is still in the exploratory stage. This current situation is not only related to the deficiencies in the design and implementation of PRO-related contents in oncology trials, but more importantly, it is a reminder that we should pay more attention to patient experience in the development of oncology drugs. This study was not supported by any funding.

Evolution of Phase II Oncology Trial Design: from Single Arm to Master Protocol.

The recent development of novel anticancer treatments with diverse mechanisms of action has accelerated the detection of treatment candidates tremendously. The rapidly changing drug development landscapes and the high failure rates in Phase III trials both underscore the importance of more efficient and robust phase II designs. The goals of phase II oncology studies are to explore the preliminary efficacy and toxicity of the investigational product and to inform future drug development strategies such as go/no-go decisions for phase III development, or dose/indication selection. These complex purposes of phase II oncology designs call for efficient, flexible, and easy-to-implement clinical trial designs. Therefore, innovative adaptive study designs with the potential of improving the efficiency of the study, protecting patients, and improving the quality of information gained from trials have been commonly used in Phase II oncology studies. Although the value of adaptive clinical trial methods in early phase drug development is generally well accepted, there is no comprehensive review and guidance on adaptive design methods and their best practice for phase II oncology trials. In this paper, we review the recent development and evolution of phase II oncology design, including frequentist multistage design, Bayesian continuous monitoring, master protocol design, and innovative design methods for randomized phase II studies. The practical considerations and the implementation of these complex design methods are also discussed.

The 24-Hour Movement Paradigm: An integrated approach to the measurement and promotion of daily activity in cancer clinical trials

Increased physical activity (PA), improved sleep, and decreased sedentary behavior (SB) are essential components of supportive care for cancer survivors. However, researchers and health care professionals have achieved limited success in improving these behaviors among cancer survivors. One potential reasoning is that, over the past two decades, guidelines for promoting and measuring PA, sleep, and SB have been largely siloed. With greater understanding of these three behaviors, health behavior researchers have recently developed a new paradigm: the 24-Hour movement approach. This approach considers PA, SB, and sleep as movement behaviors along a continuum that represent low through vigorous intensity activity. Together these three behaviors form the sum of an individual's movement across a 24-hour day. While this paradigm has been studied in the general population, its usage is still limited in cancer populations. Here, we seek to highlight (a) the potential benefits of this new paradigm for clinical trial design in oncology; (b) how this approach can allow for greater integration of wearable technology as a means of assessing and monitoring patient health outside the clinical setting, improving patient autonomy through self-monitoring of movement behavior. Ultimately, implementation of the 24-Hour movement paradigm will allow health behavior research in oncology to better promote and assess critical health behaviors to support the long-term well-being for cancer patients and survivors.

Implications of Oncology Trial Design and Uncertainties in Efficacy-Safety Data on Health Technology Assessments.

Background: Advances in cancer medicines have resulted in tangible health impacts, but the magnitude of benefits of approved cancer medicines could vary greatly. Health Technology Assessment (HTA) is a multidisciplinary process used to inform resource allocation through a systematic value assessment of health technology. This paper reviews the challenges in conducting HTA for cancer medicines arising from oncology trial designs and uncertainties of safety-efficacy data. Methods: Multiple databases (PubMed, Scopus and Google Scholar) and grey literature (public health agencies and governmental reports) were searched to inform this policy narrative review. Results: A lack of robust efficacy-safety data from clinical trials and other relevant sources of evidence has made HTA for cancer medicines challenging. The approval of cancer medicines through expedited pathways has increased in recent years, in which surrogate endpoints or biomarkers for patient selection have been widely used. Using these surrogate endpoints has created uncertainties in translating surrogate measures into patient-centric clinically (survival and quality of life) and economically (cost-effectiveness and budget impact) meaningful outcomes, with potential effects on diverting scarce health resources to low-value or detrimental interventions. Potential solutions include policy harmonization between regulatory and HTA authorities, commitment to generating robust post-marketing efficacy-safety data, managing uncertainties through risk-sharing agreements, and using value frameworks. Conclusion: A lack of robust efficacy-safety data is a central problem for conducting HTA of cancer medicines, potentially resulting in misinformed resource allocation.

Abstract 1923: A treatment-agnostic approach to predict patient survival from virtual clinical trials

Abstract We present a new approach to predict overall survival (OS) in untested oncology trial designs, which does not require the use of clinical covariates. This approach is based on generating a virtual population from a quantitative systems pharmacology (QSP) model of cancer immunology, and linking this virtual population to real patients from previous clinical trials. QSP has emerged as a dominant paradigm in recent years for investigating disease mechanisms and allowing prediction of drug effects in silico. However, survival cannot be described mechanistically and intrinsically predicted from a QSP model. We develop a weakly supervised learning approach to impute labels of OS and censorship in the virtual population. This approach requires prior matching of the virtual patients to real patients on the basis of longitudinal tumor growth curves. The idea that there exists a predictive relationship between tumor dynamics and OS was motivated by previous work on tumor growth inhibition (TGI-OS) [Claret, L. et al., J. Clin. Onc., 2013]. In contrast to the TGI-OS framework, we rely solely on simulated QSP dynamical variables for predicting survival. This allows us to derive OS predictions for trial designs different than the ones used for model development. Data from 5 clinical trials for atezolizumab in NSCLC (BIRCH, FIR, OAK, POPLAR, and IMpower110, total N = 1641) were used to link survival labels to virtual patients. 90% of the data was used for training, and 10% was held out for model validation. The imputed OS labels were used to train a log-normal accelerated failure time model on the training data, and predictions of survival in the test data were in good agreement with a Kaplan-Meier estimate of the clinical survival. The model predicted a median OS of 471 days (95% CI 453-490) compared to the observed 475 days, (95% CI 440-517). The rate of change of tumor size under treatment in the QSP model was the most predictive feature of survival, with a hazard ratio of 1.79 between 95th percentile and median of the dynamical range (95% CI 1.69-1.81). Model variables related to tumor antigens, cytotoxic cell death, and T cell dynamics were also relevant to the prediction of survival. This work provides the first example of generating and validating predictions of OS without using covariates from actual clinical trials. We intend to expand this methodology across different trial designs and different subpopulations. For example, our approach could be used to estimate hazard ratios between different treatments or combination therapies, as well as for patients grouped by PDL1 expression or by line of therapy. While this work only considered overall survival as an endpoint, our approach can potentially be extended to endpoints like progression-free-survival. Citation Format: Matthew West, Kenta Yoshida, Jiajie Yu, Vincent Lemaire. A treatment-agnostic approach to predict patient survival from virtual clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1923.

Abstract 3890: Sequencing of 888 pediatric solid tumors informs precision oncology trial design and data sharing initiatives in pediatric cancer

Abstract Pediatric pan-cancer genome analyses do not capture the full range of diagnoses encountered in clinical practice. To inform basket trial design and real-world precision oncology practice, we classified diagnoses and assessed the landscape of mutations, including trial-matching, in an unselected cohort of pediatric solid tumors. Since 2013 all Dana-Farber/Boston Children’s patients have been offered participation in the Profile study. Participant tumor samples were sequenced with DFCI-OncoPanel, a targeted panel test sequencing exons of up to 447 cancer genes for single nucleotide variants, insertions and deletions and copy number alterations, and introns and exons of up to 60 genes for rearrangements. Patient diagnosis was classified according to ICD-O, version 3.2. Genomic alterations were analyzed for matching to the actionable mutation lists of precision oncology basket trials (NCI-COG Pediatric MATCH, NCI-MATCH, and the ASCO TAPUR Study v.3). Data will be shared with the Childhood Cancer Data Initiative. There were 888 pediatric patients with sequencing enrolled in Profile between January 2013 and March 2019; 512 (58%) with solid tumors and 376 (42%) with CNS tumors. Fifty-five percent (491/888) of patients had one of ten common pediatric cancer diagnoses: neuroblastoma (n=80), low-grade glioma (n=72), Wilms tumor (n=57), medulloblastoma (n=55), pilocytic astrocytoma (n=47), rhabdomyosarcoma (n=44), osteosarcoma (n=42), ependymoma (n=39), Ewing sarcoma (n=28) and glioblastoma (n=27). The remaining 45% (397/888) had one of 85 distinct rare malignancies with less than 25 cases per diagnosis. Most (80/85) of these rare diagnoses are not represented in prior pediatric pan-cancer sequencing studies. Recurrent (&amp;gt;5%) pathogenic alterations were, in common and rare diagnoses, TP53 mutations(m) and deletions(del) and BRAFm and rearrangements(r), in common diagnoses, MYC/MYCN amplification (amp) and EWSR1r and, in rare diagnoses, CTNNB1m, CDKN2A/Bdel and NF1m/del. We found that 31% (n=271/888) of patients had at least 1 variant matching a basket trial treatment arm. Genes with matching alterations include BRAF (10%), NF1 (4%), PI3KCA (3%), NRAS (2%), BRCA2 (2%), ALK (1%), and FGFR1 (1%). Sequencing of pediatric malignancies is increasing. This study highlights opportunities to use the resulting genomic data to inform genome-selected clinical trial design and uncover drivers in pediatric cancers. The proportion of cases in this cohort with genomic alterations meeting eligibility for basket trials is equivalent to that seen in the pediatric MATCH screening study. Due to the low prevalence of the diagnoses in the long tail of cancer types in this study, defining the genomic landscape of ultra-rare cancers will require data sharing. Classifying pediatric cancer diagnoses using the ICD-O standard ontology system is feasible and will facilitate data sharing. Citation Format: Suzanne J. Forrest, Hersh Gupta, Abigail Ward, Yvonne Li, Duong Doan, Alyaa Al-Ibraheemi, Sanda Alexandrescu, Pratiti Bandopadhayay, Suzanne Shusterman, Elizabeth A. Mullen, Natalie Collins, Susan N. Chi, Karen D. Wright, Priti Kumari, Tali Mazor, Keith L. Ligon, Priyanka Shivdasani, Phani Davineni, Monica Manam, Richard L. Schilsky, Suanna S. Bruinooge, Jaime M. Guidry Auvil, Ethan Cerami, Barrett J. Rollins, Matthew L. Meyerson, Neal I. Lindeman, Laura MacConaill, Bruce E. Johnson, Andrew D. Cherniack, Alanna J. Church, Katherine A. Janeway. Sequencing of 888 pediatric solid tumors informs precision oncology trial design and data sharing initiatives in pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3890.

Incidence and impact of proportional hazards violations in phase 3 cancer clinical trials.

1561 Background: Hazard ratio (HR)-based analyses used in oncology trials rely on the assumption of proportional hazards, i.e. a HR that is constant over time. Proportional hazards violations (PHVs) may lead to misinterpretation of trial results. Restricted mean survival time (RMST) is valid with non-proportional hazards and has received recent attention specifically for immunotherapy (IO) trials but has not been routinely adopted in oncology trial design as a whole. We aimed to comprehensively characterize the incidence and factors associated with PHVs among phase 3 oncology trials and assess RMST as an alternate measure of treatment effect in survival analysis. Methods: We used Clinicaltrials.gov to identify all superiority-design, 2-arm phase 3 cancer trials with time-dependent endpoints with published results through February 2020. We manually reconstructed patient-level data from published Kaplan-Meier (KM) curves, assessed PHVs with the Schoenfeld residual test (p &lt;.05) and analyzed the RMST. To assess reconstruction accuracy, reported and reconstructed HRs were compared. Univariable logistic regression was used to assess the likelihood of PHVs by trial characteristic, with statistically significant factors (p &lt;.05) included in a multivariable analysis. Concordance of RMST-based and HR-based analysis was established when both tests agreed as to the statistical significance of the comparison. Results: Of 342 KM comparisons eligible for reconstruction, 318 comparisons across 315 trials, enrolling 347,538 patients from 1989-2017, were accurately reconstructed and analyzed. PHVs were identified in 76/318 (23.9%) trials. There was no difference in likelihood of PHVs among IO vs non-IO trials (LR 2.31, 95% CI 0.30 – 17.85, P =.37), nor by disease site, year of trial initiation, or sample size. Few trials with PHVs (16/76) pre-specified a plan to account for non-proportional hazards in statistical design. Trials with an overall survival (OS) primary endpoint (PEP) were less likely to have PHVs than trials with a non-OS PEP (LR: 0.50, 95% CI 0.28 - 0.90, P =.02). Trials whose PEP was non-significant were more likely to have PHVs (LR 1.73, 95% CI 1.01 - 2.97, P =.047). No factor remained significantly associated with PHV in multivariable analysis. Overall, 291/318 (91.5%) KM comparisons were concordant. Among trials with PHVs, 5/76 were significant by RMST but not HR, and 5/76 were significant by HR but not RMST. Of these, 1 led to FDA drug approval, and 2 others are cited in NCCN guidelines. Conclusions: PHVs are common across all phase 3 cancer clinical trials. Attempts to account for PHVs in trial design are lacking despite the potential for trial misinterpretation in the event of non-proportional hazards. RMST-based analysis is broadly concordant with HR-based analysis and may aid in interpretation of trials with PHVs. Hence, we recommend that prospective trials include a priori a statistical plan to account for PHVs.

Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials.

PURPOSEWith deeper insight into precision medicine, more innovative oncology trial designs have been proposed to contribute to the characteristics of novel antitumor drugs. Bayesian information borrowing is an indispensable part of these designs, which shows great advantages in improving the efficiency of clinical trials. Bayesian methods provide an effective framework when incorporating information. However, the key point lies in how to choose an appropriate method for complex oncology clinical trials.METHODSWe divided the borrowing information scenarios into concurrent and nonconcurrent scenarios according to whether the data to be borrowed are observed at the same time as in the current trial or not. Then, we provided an overview of the methods in each scenario. Performance comparison of different methods is carried out with regard to the type I error and power.RESULTSAs demonstrated by the simulation results in each borrowing scenario, the Bayesian hierarchical model and its extensions are more appropriate for concurrent borrowing. The simulation results demonstrate that the Bayesian hierarchical model shows great advantages when the arms are homogeneous. However, such a method should be adopted with caution when heterogeneity exists. We recommend the other methods, considering heterogeneity. Borrow information from informative priors is more suggested for nonconcurrent borrowing scenarios. Multisource exchangeability models are more suitable for multiple historical trials, while meta-analytic-predictive prior should be carefully applied.CONCLUSIONBayesian information borrowing is useful and can improve the efficiency of clinical trial designs. However, we should carefully choose an appropriate information borrowing method when facing a practical innovative oncology trial, as an appropriate method is essential to provide ideal design performance.

The Future of Clinical Trial Design in Oncology.

Clinical trials represent a fulcrum for oncology drug discovery and development to bring safe and effective medicines to patients in a timely manner. Clinical trials have shifted from traditional studies evaluating cytotoxic chemotherapy in largely histology-based populations to become adaptively designed and biomarker-driven evaluations of molecularly targeted agents and immune therapies in selected patient subsets. This review will discuss the scientific, methodological, practical, and patient-focused considerations to transform clinical trials. A call to action is proposed to establish the framework for next-generation clinical trials that strikes an optimal balance of operational efficiency, scientific impact, and value to patients. SIGNIFICANCE: The future of cancer clinical trials requires a framework that can efficiently transform scientific discoveries to clinical utility through applications of innovative technologies and dynamic design methodologies. Next-generation clinical trials will offer individualized strategies which ultimately contribute to globalized knowledge and collective learning, through the joint efforts of all key stakeholders including investigators and patients.

Seamless phase 2/3 oncology trial design with flexible sample size determination.

Conventional seamless phase 2/3 design with fixed sample size determination (SSD) has gained its popularity in oncology drug development due to attractive features such as significantly shortening the development timeline, minimizing sample size, as well as early decision making. However, this design is not immune to inaccurate treatment effect assumption when only limited efficacy data are available at study design stage. We propose an innovative seamless phase 2/3 study design with flexible SSD for oncology trials, in which the trial is designed under a distribution of treatment effect instead of one single assumption due to huge uncertainty of treatment effect at design stage and the sample size for end of phase 3 analysis is not predetermined at design stage, but rather dynamically determined based on observed treatment effect at phase 2 portion. Some practical sample size determination rules for end of phase 3 analysis will be discussed. The proposed design can lead to reduced sample size or/and improved power compared with conventional seamless phase 2/3 design with fixed SSD. This innovative study design can be especially useful for programs with aggressive development strategy to expedite the process in delivering efficacious treatment to patients.

YI02.02 Basket and Umbrella Trial Designs in Oncology

YI02.02 Basket and Umbrella Trial Designs in Oncology

Umbrella and basket trials in oncology: ethical challenges

BackgroundNovel precision oncology trial designs, such as basket and umbrella trials, are designed to test new anticancer agents in more effective and affordable ways. However, they present some ethical concerns referred to scientific validity, risk-benefit balance and informed consent. Our aim is to discuss these issues in basket and umbrella trials, giving examples of two ongoing cancer trials: NCI-MATCH (National Cancer Institute – Molecular Analysis for Therapy Choice) and Lung-MAP (Lung Cancer Master Protocol) study.Main bodyWe discuss three ethical requirements for clinical trials which may be challenged in basket and umbrella trial designs. Firstly, we consider scientific validity. Thanks to the new trial designs, patients with rare malignancies have the opportunity to be enrolled and benefit from the trial, but due to insufficient accrual, the trial may generate clinically insignificant findings. Inadequate sample size in study arms and the use of surrogate endpoints may result in a drug approval without confirmed efficacy. Moreover, complexity, limited quality and availability of tumor samples may not only introduce bias and result in unreliable and unrepresentative findings, but also can potentially harm patients and assign them to an inappropriate therapy arm. Secondly, we refer to benefits and risks. Novel clinical trials can gain important knowledge on the variety of tumors, which can be used in future trials to develop effective therapies. However, they offer limited direct benefits to patients. All potential participants must wait about 2 weeks for the results of the genetic screening, which may be stressful and produce anxiety. The enrollment of patients whose tumors harbor multiple mutations in treatments matching a single mutation may be controversial. As to informed consent – the third requirement we discuss, the excessive use of phrases like “personalized medicine”, “tailored therapy” or “precision oncology” might be misleading and cause personal convictions that the study protocol is designed to fulfill the individual health-related needs of participants.ConclusionsWe suggest that further approaches should be implemented to enhance scientific validity, reduce misunderstandings and risks, thus maximizing the benefits to society and to trial participants.