New insights into the evaluation of randomized controlled trials for rare diseases over a long-term research horizon: a simulation study

Prognostic factors and survival of patients with uterine sarcoma: a German unicenter analysis

PURPOSE

Uterine sarcoma (US) as a histologically heterogeneous group of tumors is rare and associated with poor prognosis. Prognostic factors based on systematic data collection need to be identified to optimize patients' treatment.

METHODS

This unicenter, retrospective cohort study includes 57 patients treated at the University Hospital Freiburg, Germany between 1999 and 2017. Progression-free survival (PFS) and overall survival (OS) were calculated and visualized in Kaplan-Meier curves. Prognostic factors were identified using log-rank test and Cox regression.

RESULTS

44 Leiomyosarcoma (LMS), 7 low-grade endometrial stromal sarcoma (LG-ESS), 4 high-grade ESS and 2 undifferentiated US patients were identified. The median age at time of diagnosis was 51.0 years (range 18-83). The median follow-up time was 35 months. PFS for the total cohort was 14.0 (95%-Confidence-Interval (CI) 9.7-18.3) and OS 36.0 months (95%-CI 22.1-49.9). Tumor pathology was prognostically significant for OS with LG-ESS being the most favorable (mean OS 150.3 months). In the multivariate analysis, patients over 52 years showed a four times higher risk for tumor recurrence (hazard ratio (HR) 4.4; 95%-CI 1.5-12.9). Progesterone receptor negativity was associated with a two times higher risk for death (HR 2.8; 95%-CI 1.0-7.5). For LMS patients age ≥ 52 years (p = 0.04), clear surgical margins (p = 0.01), FIGO stage (p = 0.01) and no application of chemotherapy (p = 0.02) were statistically significant factors for OS.

CONCLUSION

Tumor histology, age at time of diagnosis and progesterone receptor status were prognostic factors for US. Unfavorable OS in LMS patients was associated with advanced FIGO stage, suboptimal cytoreduction and application of chemotherapy.

A systematic review of the "promising zone" design

Introduction

Sample size calculations require assumptions regarding treatment response and variability. Incorrect assumptions can result in under- or overpowered trials, posing ethical concerns. Sample size re-estimation (SSR) methods investigate the validity of these assumptions and increase the sample size if necessary. The “promising zone” (Mehta and Pocock, Stat Med 30:3267–3284, 2011) concept is appealing to researchers for its design simplicity. However, it is still relatively new in the application and has been a source of controversy.

Objectives

This research aims to synthesise current approaches and practical implementation of the promising zone design.

Methods

This systematic review comprehensively identifies the reporting of methodological research and of clinical trials using promising zone. Databases were searched according to a pre-specified search strategy, and pearl growing techniques implemented.

Results

The combined search methods resulted in 270 unique records identified; 171 were included in the review, of which 30 were trials. The median time to the interim analysis was 60% of the original target sample size (IQR 41–73%). Of the 15 completed trials, 7 increased their sample size. Only 21 studies reported the maximum sample size that would be considered, for which the median increase was 50% (IQR 35–100%).

Conclusions

Promising zone is being implemented in a range of trials worldwide, albeit in low numbers. Identifying trials using promising zone was difficult due to the lack of reporting of SSR methodology. Even when SSR methodology was reported, some had key interim analysis details missing, and only eight papers provided promising zone ranges.

Trial Design Challenges and Approaches for Precision Oncology in Rare Tumors: Experiences of the Children's Oncology Group

In the United States, cancer remains the leading cause of disease-related death in children. Although survival from any pediatric cancer has improved dramatically during past decades, a number of cancers continue to yield dismal prognoses, which has motivated the continued study of novel therapeutic strategies. Furthermore, even patients cured of pediatric cancer often experience severe adverse effects of treatment and other long-term health implications, such as cardiotoxicity or loss of fertility. For these patients, improved risk stratification to identify those who could safely receive alternate or less-intensive therapy without affecting prognosis is a key objective. Fortunately, pediatric cancers are rare overall, but even among patients with the same narrow cancer type, there is often broad heterogeneity in terms of prognosis, molecular features or pathology, current treatment strategies, and scientific objectives. As a result, the design of clinical trials in the pediatric cancer setting is challenged by a number of practical issues that must be addressed to ensure trial feasibility for this vulnerable group of patients. In this review, we discuss some of the unique trial design considerations often encountered in any rare tumor setting through the lens of our experiences as faculty statisticians for the Children's Oncology Group, the largest organization in the world dedicated exclusively to pediatric cancer research and clinical trials. These topics include risk stratification within individual trials, relaxation of trial operating characteristics and parameters, use of historical controls, and address of noninferiority-type objectives in small cohorts. We review each in terms of practical motivation, present challenges, and potential solutions described in the literature and implemented in selected example trials from the Children's Oncology Group.

Group sequential adaptive designs in series of time-to-event randomised trials in rare diseases: A simulation study

In rare diseases, fully powered large trials may not be doable in a reasonable time frame even with international collaborations. In a previous work, we proposed an approach based on a series of smaller parallel group two-arm randomised controlled trials (RCT) performed over a long research horizon. Within the series of trials, the treatment selected after each trial becomes the control treatment of the next one. We concluded that running more trials with smaller sample sizes and relaxed α-levels leads in the long term and under reasonable assumptions to larger survival benefits with a moderate increase of risk as compared to traditional designs based on larger but fewer trials designed to meet stringent evidence criteria. We now extend this quantitative framework with more 'flexible' designs including interim analyses for futility and/or efficacy, and three-arm adaptive designs with treatment selection at interim. In the simulation study, we considered different disease severities, accrual rates, and hypotheses of how treatments improve over time. For each design, we estimated the long-term survival benefit as the relative difference in hazard rates between the end and the start of the research horizon, and the risk defined as the probability of selecting at the end of the research horizon a treatment inferior to the initial control. We assessed the impact of the α-level and the choice of the stopping rule on the operating characteristics. We also compared the performance of series based on two- vs. three-arm trials. We show that relaxing α-levels within the limit of 0.1 is associated with larger survival gains and moderate increase of risk which remains within acceptable ranges. Including an interim analysis with a futility rule is associated with an additional survival gain and a better risk control as compared to series with no interim analysis, when the α-level is below or equal to 0.1, whereas the benefit of including an interim analysis is rather small for higher α-levels. Including an interim analysis for efficacy yields almost no additional gain. Series based on three-arm trials are associated with a systematic improvement in terms of survival gain and risk control as compared to series of two-arm trials.

Too many targets, not enough patients: rethinking neuroblastoma clinical trials

Neuroblastoma is a rare solid tumour of infancy and early childhood with a disproportionate contribution to paediatric cancer mortality and morbidity. Combination chemotherapy, radiation therapy and immunotherapy remains the standard approach to treat high-risk disease, with few recurrent, actionable genetic aberrations identified at diagnosis. However, recent studies indicate that actionable aberrations are far more common in relapsed neuroblastoma, possibly as a result of clonal expansion. In addition, although the major validated disease driver, MYCN, is not currently directly targetable, multiple promising approaches to target MYCN indirectly are in development. We propose that clinical trial design needs to be rethought in order to meet the challenge of providing rigorous, evidence-based assessment of these new approaches within a fairly small patient population and that experimental therapies need to be assessed at diagnosis in very-high-risk patients rather than in relapsed and refractory patients.

How do you design randomised trials for smaller populations? A framework

How should we approach trial design when we can get some, but not all, of the way to the numbers required for a randomised phase III trial?We present an ordered framework for designing randomised trials to address the problem when the ideal sample size is considered larger than the number of participants that can be recruited in a reasonable time frame. Staying with the frequentist approach that is well accepted and understood in large trials, we propose a framework that includes small alterations to the design parameters. These aim to increase the numbers achievable and also potentially reduce the sample size target. The first step should always be to attempt to extend collaborations, consider broadening eligibility criteria and increase the accrual time or follow-up time. The second set of ordered considerations are the choice of research arm, outcome measures, power and target effect. If the revised design is still not feasible, in the third step we propose moving from two- to one-sided significance tests, changing the type I error rate, using covariate information at the design stage, re-randomising patients and borrowing external information.We discuss the benefits of some of these possible changes and warn against others. We illustrate, with a worked example based on the Euramos-1 trial, the application of this framework in designing a trial that is feasible, while still providing a good evidence base to evaluate a research treatment.This framework would allow appropriate evaluation of treatments when large-scale phase III trials are not possible, but where the need for high-quality randomised data is as pressing as it is for common diseases.