Bayesian joint models for multi-regional clinical trials.

In recent years, multi-regional clinical trials (MRCTs) have increased in popularity in the pharmaceutical industry due to their ability to accelerate the global drug development process. To address potential challenges with MRCTs, the International Council for Harmonisation released the E17 guidance document which suggests the use of statistical methods that utilize information borrowing across regions if regional sample sizes are small. We develop an approach that allows for information borrowing via Bayesian model averaging in the context of a joint analysis of survival and longitudinal data from MRCTs. In this novel application of joint models to MRCTs, we use Laplace's method to integrate over subject-specific random effects and to approximate posterior distributions for region-specific treatment effects on the time-to-event outcome. Through simulation studies, we demonstrate that the joint modeling approach can result in an increased rejection rate when testing the global treatment effect compared with methods that analyze survival data alone. We then apply the proposed approach to data from a cardiovascular outcomes MRCT.

Bayesian multiregional clinical trials using model averaging.

Multiregional clinical trials (MRCTs) provide the benefit of more rapidly introducing drugs to the global market; however, small regional sample sizes can lead to poor estimation quality of region-specific effects when using current statistical methods. With the publication of the International Conference for Harmonisation E17 guideline in 2017, the MRCT design is recognized as a viable strategy that can be accepted by regional regulatory authorities, necessitating new statistical methods that improve the quality of region-specific inference. In this article, we develop a novel methodology for estimating region-specific and global treatment effects for MRCTs using Bayesian model averaging. This approach can be used for trials that compare two treatment groups with respect to a continuous outcome, and it allows for the incorporation of patient characteristics through the inclusion of covariates. We propose an approach that uses posterior model probabilities to quantify evidence in favor of consistency of treatment effects across all regions, and this metric can be used by regulatory authorities for drug approval. We show through simulations that the proposed modeling approach results in lower MSE than a fixed-effects linear regression model and better control of type I error rates than a Bayesian hierarchical model.

Bayesian design of multi-regional clinical trials with time-to-event endpoints.

Sponsors often rely on multi-regional clinical trials (MRCTs) to introduce new treatments more rapidly into the global market. Many commonly used statistical methods do not account for regional differences, and small regional sample sizes frequently result in lower estimation quality of region-specific treatment effects. The International Council for Harmonization E17 guidelines suggest consideration of methods that allow for information borrowing across regions to improve estimation. In response to these guidelines, we develop a novel methodology to estimate global and region-specific treatment effects from MRCTs with time-to-event endpoints using Bayesian model averaging (BMA). This approach accounts for the possibility of heterogeneous treatment effects between regions, and we discuss how to assess the consistency of these effects using posterior model probabilities. We obtain posterior samples of the treatment effects using a Laplace approximation, and we show through simulation studies that the proposed modeling approach estimates region-specific treatment effects with lower mean squared error than a Cox proportional hazards model while resulting in a similar rejection rate of the global treatment effect. We then apply the BMA approach to data from the LEADER trial, an MRCT designed to evaluate the cardiovascular safety of an anti-diabetic treatment.

Airway IL-1ß associates with IL-5 production following dust mite allergen inhalation in humans.

BACKGROUND: Preclinical studies implicate interleukin (IL)-1ß as a key mediator of asthma and have shown the efficacy of IL-1 antagonism for treatment of allergic airway inflammation; human studies in this area are lacking. OBJECTIVES: Our aim was to study the relationship of airway IL-1ß to features of acute allergen-induced asthma exacerbation in humans. METHODS: Dust mite-allergic adults with mild asthma underwent inhalation challenge with Dermatophagoides farinae. Fractional exhaled nitric oxide (FeNO), induced sputum and peripheral blood samples were obtained pre- and 24 h post-challenge. Spirometry was performed before and throughout the challenge at 10-min intervals, and allergen responsiveness was defined by a 20% fall in Forced Expiratory Volume in 1 s (FEV1). Sputum samples were analyzed for inflammatory cells, cytokines and chemokines. Multiple linear regression was employed to test the association between sputum IL-1ß concentration and biomarkers of T helper type 2 (T2)-dominant inflammation. RESULTS: Fourteen volunteers underwent inhaled allergen challenge. Allergen responsive volunteers showed a greater positive change in IL-1ß in sputum following allergen challenge compared to non-responders. Higher pre-challenge sputum IL-1ß was associated with greater increase in sputum IL-5 (p = 0.004), sputum eosinophils (p = 0.001) and blood IL-5 (p = 0.003) following allergen challenge. Allergen-induced sputum IL-1ß production was significantly associated with sputum and blood IL-5 (p < 0.001 and p = 0.007, respectively), sputum IL-4 (p = 0.001), IL-13 (p = 0.026), eosinophils (p = 0.008) and FeNO (p = 0.03). CONCLUSIONS: The positive association between production of IL-1ß and biomarkers of T2 inflammation, particularly IL-5, in humans is consistent with work in animal models that demonstrates a link between IL-1ß and the pathophysiology of allergic asthma. The role of IL-1ß in human asthma warrants further study.

Quality of Life Outcomes in Breast Cancer Patients Receiving Chemotherapy With or Without Radiation Therapy.

PURPOSE: Understanding quality of life (QOL) implications of individual components of breast cancer treatment is important as systemic therapies continue to improve oncologic outcomes. We hypothesized that adjuvant radiation therapy does not significantly impact QOL domains in breast cancer patients undergoing chemotherapy. METHODS: Data was drawn from three prospective studies in women with localized breast cancer being treated with chemotherapy from March 2014 to December 2019. Patient-reported measures were collected at baseline (pretreatment) and post-treatment using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) measure, which consists of 5 subscales. Changes in mean QOL scores in patients who received radiotherapy were compared to those who did not using a one-sided noninferiority method. Statistical significance was determined below 0.05 to meet noninferiority. RESULTS: In a sample of 175 patients, 131 were treated with radiation and 44 had no radiation. The sample consisted mostly of stage I-II breast cancer (78%) with hormone receptor positive (59%) disease, receiving either neoadjuvant (36%) or adjuvant chemotherapy (64%). Mean change in QOL for the group treated with radiation compared to no radiation was noninferior with respect to Physical Well-Being (P = .0027), Social/Family Well-Being (P = .0002), Emotional Well-Being (P = .0203), FACIT-Fatigue Subscale (P = .0072), and the Total FACIT-F score (P = .0005); however, mean change in QOL did not meet noninferiority for Functional Well-Being (P = .0594). CONCLUSION: Patient-reported QOL from baseline to post-treatment, using the Total FACIT-F score, was noninferior in patients treated with versus without radiation therapy. This finding, in addition to individualized QOL subscales, provides important information in the informed decision-making process when discussing the effects of locoregional radiation on QOL in localized breast cancer patients treated with chemotherapy.

Basket trials in oncology: a systematic review of practices and methods, comparative analysis of innovative methods, and an appraisal of a missed opportunity.

Background: Basket trials are increasingly used in oncology drug development for early signal detection, accelerated tumor-agnostic approvals, and prioritization of promising tumor types in selected patients with the same mutation or biomarker. Participants are grouped into so-called baskets according to tumor type, allowing investigators to identify tumors with promising responses to treatment for further study. However, it remains a question as to whether and how much the adoption of basket trial designs in oncology have translated into patient benefits, increased pace and scale of clinical development, and de-risking of downstream confirmatory trials. Methods: Innovation in basket trial design and analysis includes methods that borrow information across tumor types to increase the quality of statistical inference within each tumor type. We build on the existing systematic reviews of basket trials in oncology to discuss the current practices and landscape. We conceptually illustrate recent innovative methods for basket trials, with application to actual data from recently completed basket trials. We explore and discuss the extent to which innovative basket trials can be used to de-risk future trials through their ability to aid prioritization of promising tumor types for subsequent clinical development. Results: We found increasing adoption of basket trial design in oncology, but largely in the design of single-arm phase II trials with a very low adoption of innovative statistical methods. Furthermore, the current practice of basket trial design, which does not consider its impact on the clinical development plan, may lead to a missed opportunity in improving the probability of success of a future trial. Gating phase II with a phase Ib basket trial reduced the size of phase II trials, and losses in the probability of success as a result of not using innovative methods may not be recoverable by running a larger phase II trial. Conclusion: Innovative basket trial methods can reduce the size of early phase clinical trials, with sustained improvement in the probability of success of the clinical development plan. We need to do more as a community to improve the adoption of these methods.

Availability of cancer care services and the organization of care delivery at critical access hospitals.

INTRODUCTION: Critical access hospitals (CAHs) provide an opportunity to meet the needs of individuals with cancer in rural areas. Two common innovative care delivery methods include the use of traveling oncologists and teleoncology. It is important to understand the availability and organization of cancer care services in CAHs due to the growing population with cancer and expected declines in oncology workforce in rural areas. METHODS: Stratified random sampling was used to generate a sample of 50 CAHs from each of the four U.S. Census Bureau-designated regions resulting in a total sample of 200 facilities. Analyses were conducted from 135 CAH respondents to understand the availability of cancer care services and organization of cancer care across CAHs. RESULTS: Almost all CAHs (95%) provided at least one cancer screening or diagnostic service. Forty-six percent of CAHs reported providing at least one component of cancer treatment (chemotherapy, radiation, or surgery) at their facility. CAHs that offered cancer treatment reported a wide range of health care staff involvement, including 34% of respondents reporting involvement of a local oncologist, 38% reporting involvement of a visiting oncologist, and 28% reporting involvement of a non-local oncologist using telemedicine. CONCLUSION: Growing disparities within rural areas emphasize the importance of ensuring access to timely screening and guideline-recommended treatment for cancer in rural communities. These data demonstrated that CAHs are addressing the growing need through a variety of approaches including the use of innovative models that utilize non-local providers and telemedicine to expand access to crucial services for rural residents with cancer.