Decentralized clinical trials and rare diseases: a Drug Information Association Innovative Design Scientific Working Group (DIA-IDSWG) perspective.

BACKGROUND: Traditional clinical trials require tests and procedures that are administered in centralized clinical research sites, which are beyond the standard of care that patients receive for their rare and chronic diseases. The limited number of rare disease patients scattered around the world makes it particularly challenging to recruit participants and conduct these traditional clinical trials. MAIN BODY: Participating in clinical research can be burdensome, especially for children, the elderly, physically and cognitively impaired individuals who require transportation and caregiver assistance, or patients who live in remote locations or cannot afford transportation. In recent years, there is an increasing need to consider Decentralized Clinical Trials (DCT) as a participant-centric approach that uses new technologies and innovative procedures for interaction with participants in the comfort of their home. CONCLUSION: This paper discusses the planning and conduct of DCTs, which can increase the quality of trials with a specific focus on rare diseases.

A Bayesian platform trial design to simultaneously evaluate multiple drugs in multiple indications with mixed endpoints.

In the era of targeted therapies and immunotherapies, the traditional drug development paradigm of testing one drug at a time in one indication has become increasingly inefficient. Motivated by a real-world application, we propose a master-protocol-based Bayesian platform trial design with mixed endpoints (PDME) to simultaneously evaluate multiple drugs in multiple indications, where different subsets of efficacy measures (eg, objective response and landmark progression-free survival) may be used by different indications as single or multiple endpoints. We propose a Bayesian hierarchical model to accommodate mixed endpoints and reflect the trial structure of indications that are nested within treatments. We develop a two-stage approach that first clusters the indications into homogeneous subgroups and then applies the Bayesian hierarchical model to each subgroup to achieve precision information borrowing. Patients are enrolled in a group-sequential way and adaptively assigned to treatments according to their efficacy estimates. At each interim analysis, the posterior probabilities that the treatment effect exceeds prespecified clinically relevant thresholds are used to drop ineffective treatments and "graduate" effective treatments. Simulations show that the PDME design has desirable operating characteristics compared to existing method.

A proper statistical inference framework to compare clinical trial and real-world progression-free survival data.

The past decade has witnessed an increasing trend in utilizing external control data in clinical trials, especially in the form of synthetic control arms (SCA) derived from real-world or historical trial data. Including such data in clinical trial analysis can improve trial feasibility and efficiency, provided the issues caused by non-randomization and systematic differences are appropriately addressed. Current methodology development in this area focuses on establishing the comparability of patient baseline characteristics between arms, and more research is needed to ensure comparability of other elements such as endpoints. Motivated by the comparative analysis of SCA progression-free survival (PFS) and trial arm PFS, we aim to address another important but little discussed issue for external time-to-event (TTE) data that depend on disease assessment schedules (DAS). Since DAS are generally inconsistent across different data sources, we propose a proper statistical inference framework that harmonizes the DAS through data augmentation by multiple imputation. We demonstrate through extensive simulations that the proposed framework is unbiased in estimating median TTE and hazard ratio, well controls the type I error and achieves desirable power for log-rank test, while the unadjusted analysis can be biased and suffer from severe type I error inflation or power loss depending on the direction of the bias. Given the desirable performance, we recommend the proposed framework for comparative analysis using external DAS-based TTE data in clinical trials.

Impact of COVID-19 pandemic on oncology clinical trial design, data collection and analysis.

BACKGROUND: To identify and assess via simulation the impact of COVID-19 pandemic on oncology trials and discuss potential mitigation strategies for study design, data collection, endpoints and analyses. METHODS: We simulated clinical trials to evaluate the COVID-19 impact on overall survival and progression-free survival. We evaluated survival in single-region trials with different proportions of impacted patients across treatment arms, and in multi-region randomized trials with different proportions of impacted patients across regions. We also assessed the impact on PFS when the missingness of disease assessment and censoring rules vary. Impact on the trial success and robustness of statistical inference was summarized. RESULTS: Without regional impact, the impact on OS analysis is minimal if proportions of impacted patients are similar across arms, however, if a larger proportion of treatment arm patients are impacted, trials may suffer substantial power loss and underestimate treatment effect size. For multi-region trials, if more treatment arm patients are enrolled from more severely impacted regions, trials also have poorer performance. For PFS analysis, the intent-to-treat rule performs well even when the treatment arm patients are more likely to miss disease assessments, while the consecutive-missing censoring rule may lead to poorer performance. CONCLUSION: COVID-19 affects oncology trials. Simulations would be highly informative to Data Monitoring Committee in understanding the impact and making appropriate recommendations, upon which the sponsor could start planning potential remedies. We also recommend a decision tree for choosing the appropriate methods for PFS evaluation in the presence of missing disease assessments due to COVID-19.

Bayesian Divide-and-Conquer Propensity Score Based Approaches for Leveraging Real World Data in Single Arm Clinical Trials.

There has been a substantial rise in the usage of real-world data (RWD) to supplement trial data in the medical and statistical literature. Propensity score methods such as stratification have been used to balance baseline characteristics and prognostic factors between external patients and current trial patients to improve the estimation of the current trial's parameter of interest. This paper merges propensity score methodology and Bayesian inference to estimate a current trial's parameter of interest as follows: (i) match current patients and external patients by strata using the percentiles of the current patients' propensity scores, (ii) apply a prior within each stratum to leverage RWD to estimate the stratum-specific parameter of interest, and (iii) then use a weighted average scheme to combine the stratum-specific parameters to estimate the overall current trial's parameter of interest. In stage (ii), the three priors used are a double hierarchical prior, an extension of the robust mixture prior, and an extension of the power prior. An extensive simulation study is carried out to evaluate the performance of the proposed approaches.

A Bayesian phase I/II platform design for co-developing drug combination therapies for multiple indications.

There is a growing trend to combine a new targeted or immunotherapy agent with the cancer-specific standard of care to treat different types of cancers. We propose a master-protocol-based, Bayesian phase I/II platform design to co-develop combination (BPCC) therapies in multiple indications. Under the BPCC design, only a single master protocol is needed, and the combined drug is evaluated in different indications in a concurrent or staggered fashion. For each indication, we jointly model dose-toxicity and -efficacy relationships and employ Bayesian hierarchical models to borrow information across them for more efficient indication-specific decision-making. To account for the characteristic of targeted or immunotherapy agents that their efficacy may not monotonically increase with the dose, and often plateau at high doses, we use the utility to quantify the risk-benefit tradeoff of the treatment. At each interim, we update the toxicity and efficacy model, as well as the estimate of the utility, based on the observed data across indications to inform the indication-specific decision of dose escalation and de-escalation and identify the optimal biological dose for each indication. Simulation study shows that the BPCC design has desirable operating characteristics, and that it provides an efficient approach to accelerate the development of combination therapies.