Bayesian enrichment strategies for randomized discontinuation trials

Pembrolizumab in patients with rare and ultra-rare sarcomas (AcSé Pembrolizumab): analysis of a subgroup from a non-randomised, open-label, phase 2, basket trial

BACKGROUND

Sarcoma is a heterogeneous group of diseases with few treatment options. Immunotherapy has shown little activity in studies including unselected sarcomas, but immune checkpoint blockers have shown activity in specific histotypes. We evaluated the activity of pembrolizumab in rare and ultra-rare sarcomas.

METHODS

AcSé Pembrolizumab is an ongoing phase 2, basket, multitumour study investigating the activity of pembrolizumab monotherapy in rare cancers. Here, we report the results obtained in patients with selected histotypes of rare sarcomas (incidence of less than one case per 1 000 000 people per year) recruited at 24 French hospitals. Key inclusion criteria were age 15 years or older, Eastern Cooperative Oncology Group performance status of 0-1, and advanced disease that was untreated and resistant to treatment. Patients were given pembrolizumab 200 mg intravenously on day 1 of every 21-day cycle for a maximum of 24 months. The primary endpoint was objective response rate at week 12 using Response Evaluation Criteria in Solid Tumours version 1.1, assessed by local investigators. The primary endpoint and safety were analysed in the intention-to-treat population. The AcSé Pembrolizumab study is registered with ClinicalTrials.gov, NCT03012620.

FINDINGS

Between Sept 4, 2017, and Dec 29, 2020, 98 patients were enrolled, of whom 97 received treatment and were included in analyses (median age 51 years [IQR 35-65]; 53 [55%] were male; 44 [45%] were female; no data were collected on race or ethnicity). 34 (35%) patients had chordomas, 14 (14%) had alveolar soft part sarcomas, 12 (12%) had SMARCA4-deficient sarcomas or malignant rhabdoid tumours, eight (8%) had desmoplastic small round cell tumours, six (6%) had epithelioid sarcomas, four (4%) had dendritic cell sarcomas, three (3%) each had clear cell sarcomas, solitary fibrous tumours, and myxoid liposarcomas, and ten (10%) had other ultra-rare histotypes. As of data cutoff (April 11, 2022), median follow-up was 13·1 months (range 0·1-52·8; IQR 4·3-19·7). At week 12, objective response rate was 6·2% (95% CI 2·3-13·0), with no complete responses and six partial responses in the 97 patients. The most common grade 3-4 adverse events were anaemia (eight [8%] of 97), alanine aminotransferase and aspartate aminotransferase increase (six [6%]), and dyspnoea (five [5%]). 86 serious adverse events were reported in 37 patients. Five deaths due to adverse events were reported, none of which were determined to be related to treatment (two due to disease progression, two due to cancer, and one due to unknown cause).

INTERPRETATION

Our data show the activity and manageable toxicity of pembrolizumab in some rare and ultra-rare sarcoma histotypes, and support the PD-1/PD-L1 pathway as a potential therapeutic target in selected histotypes. The completion of the basket study will provide further evidence regarding the activity and toxicity of pembrolizumab in identified rare types of cancer.

FUNDING

The Ligue contre le cancer, INCa, MSD.

TRANSLATION

For the French translation of the abstract see Supplementary Materials section.

Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles

Background

Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes.

Main text

Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications.

Conclusion

The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-022-02342-5.

Designing and conducting adaptive trials to evaluate interventions in health services and implementation research: practical considerations

Randomized controlled clinical trials are widely considered the gold standard for evaluating the efficacy or effectiveness of interventions in health care. Adaptive trials incorporate changes as the study proceeds, such as modifying allocation probabilities or eliminating treatment arms that are likely to be ineffective. These designs have been widely used in drug discovery studies but can also be useful in health services and implementation research and have been minimally used. As motivating examples, we use an ongoing adaptive trial and two completed parallel group studies and highlight potential advantages, disadvantages, and important considerations when using adaptive trial designs in health services and implementation research. In addition, we investigate the impact on power and the study duration if the two completed parallel-group trials had instead been conducted using adaptive principles. Compared with traditional trial designs, adaptive designs can often allow one to evaluate more interventions, adjust participant allocation probabilities (e.g., to achieve covariate balance), and identify participants who are likely to agree to enroll. These features could reduce resources needed to conduct a trial. However, adaptive trials have potential disadvantages and practical aspects that need to be considered, most notably outcomes that can be rapidly measured and extracted (e.g., long-term outcomes that take significant time to measure from data sources can be challenging), minimal missing data, and time trends. In conclusion, adaptive designs are a promising approach to help identify how best to implement evidence-based interventions into real-world practice in health services and implementation research.

Adaptive Enrichment Designs in Clinical Trials

Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design's structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.

A Bayesian response-adaptive trial in tuberculosis: The endTB trial

PURPOSE

To evaluate the use of Bayesian adaptive randomization for clinical trials of new treatments for multidrug-resistant tuberculosis.

METHODS

We built a response-adaptive randomization procedure, adapting on two preliminary outcomes for tuberculosis patients in a trial with five experimental regimens and a control arm. The primary study outcome is treatment success after 73 weeks from randomization; preliminary responses are culture conversion at 8 weeks and treatment success at 39 weeks. We compared the adaptive randomization design with balanced randomization using hypothetical scenarios.

RESULTS

When we compare the statistical power under adaptive randomization and non-adaptive designs, under several hypothetical scenarios we observe that adaptive randomization requires fewer patients than non-adaptive designs. Moreover, adaptive randomization consistently allocates more participants to effective arm(s). We also show that these advantages are limited to scenarios consistent with the assumptions used to develop the adaptive randomization algorithm.

CONCLUSION

Given the objective of evaluating several new therapeutic regimens in a timely fashion, Bayesian response-adaptive designs are attractive for tuberculosis trials. This approach tends to increase allocation to the effective regimens.

Decision-theoretic designs for small trials and pilot studies: A review

Pilot studies and other small clinical trials are often conducted but serve a variety of purposes and there is little consensus on their design. One paradigm that has been suggested for the design of such studies is Bayesian decision theory. In this article, we review the literature with the aim of summarizing current methodological developments in this area. We find that decision-theoretic methods have been applied to the design of small clinical trials in a number of areas. We divide our discussion of published methods into those for trials conducted in a single stage, those for multi-stage trials in which decisions are made through the course of the trial at a number of interim analyses, and those that attempt to design a series of clinical trials or a drug development programme. In all three cases, a number of methods have been proposed, depending on the decision maker’s perspective being considered and the details of utility functions that are used to construct the optimal design.

Combining progression-free survival and overall survival as a novel composite endpoint for glioblastoma trials

BACKGROUND

The use of auxiliary endpoints may provide efficiencies for clinical trial design, but such endpoints may not have intrinsic clinical relevance or clear linkage to more meaningful endpoints. The purpose of this study was to generate a novel endpoint that considers both overall survival (OS) and earlier events such as progression-free survival (PFS) and determine whether such an endpoint could increase efficiency in the design of glioblastoma clinical trials.

METHODS

Recognizing that the association between PFS and OS varies depending on therapy and tumor type, we developed a statistical model to predict OS based on PFS as the trial progresses. We then evaluated the efficiency of our model using simulations of adaptively randomized trials incorporating PFS and OS distributions from prior published trials in neuro-oncology.

RESULTS

When treatment effects on PFS and OS are concordant, our proposed approach results in efficiency gains compared with randomization based on OS alone while sacrificing minimal efficiency compared with using PFS as the primary endpoint. When treatment effects are limited to PFS, our approach provides randomization probabilities that are close to those based on OS alone.

CONCLUSION

Use of OS as the primary endpoint, combined with statistical modeling of the relationship between OS and PFS during the course of the trial, results in more robust and efficient trial designs than using either endpoint alone.

Robust meta-analytic-predictive priors in clinical trials with historical control information

Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.

Bayesian designs and the control of frequentist characteristics: a practical solution

Frequentist concepts, such as the control of the type I error or the false discovery rate, are well established in the medical literature and often required by regulators. Most Bayesian designs are defined without explicit considerations of frequentist characteristics. Once the Bayesian design is structured, statisticians use simulations and adjust tuning parameters to comply with a set of targeted operating characteristics. These adjustments affect the use of prior information and utility functions. Here we consider a Bayesian decision theoretic approach for experimental designs with explicit frequentist requisites. We define optimal designs under a set of constraints required by a regulator. Our approach combines the use of interpretable utility functions with frequentist criteria, and selects an optimal design that satisfies a set of required operating characteristics. We illustrate the approach using a group-sequential multi-arm Phase II trial and a bridging trial.

Estimation of Progression-Free Survival for All Treated Patients in the Randomized Discontinuation Trial Design

The randomized discontinuation trial (RDT) design is an enrichment-type design that has been used in a variety of diseases to evaluate the efficacy of new treatments. The RDT design seeks to select a more homogeneous group of patients, consisting of those who are more likely to show a treatment benefit if one exists. In oncology, the RDT design has been applied to evaluate the effects of cytostatic agents, that is, drugs that act primarily by slowing tumor growth rather than shrinking tumors. In the RDT design, all patients receive treatment during an initial, open-label run-in period of duration T. Patients with objective response (substantial tumor shrinkage) remain on therapy while those with early progressive disease are removed from the trial. Patients with stable disease (SD) are then randomized to either continue active treatment or switched to placebo. The main analysis compares outcomes, for example, progression-free survival (PFS), between the two randomized arms. As a secondary objective, investigators may seek to estimate PFS for all treated patients, measured from the time of entry into the study, by combining information from the run-in and post run-in periods. For t ≤ T, PFS is estimated by the observed proportion of patients who are progression-free among all patients enrolled. For t > T, the estimate can be expressed as Ŝ(t) = p̂_OR × Ŝ_OR(t - T) + p̂_SD × Ŝ_SD(t - T), where p̂_OR is the estimated probability of response during the run-in period, p̂_SD is the estimated probability of SD, and Ŝ_OR(t - T) and Ŝ_SD(t - T) are the Kaplan-Meier estimates of subsequent PFS in the responders and patients with SD randomized to continue treatment, respectively. In this article, we derive the variance of Ŝ(t), enabling the construction of confidence intervals for both S(t) and the median survival time. Simulation results indicate that the method provides accurate coverage rates. An interesting aspect of the design is that outcomes during the run-in phase have a negative multinomial distribution, something not frequently encountered in practice.