Uncertainty directed factorial clinical trials.

The development and evaluation of novel treatment combinations is a key component of modern clinical research. The primary goals of factorial clinical trials of treatment combinations range from the estimation of intervention-specific effects, or the discovery of potential synergies, to the identification of combinations with the highest response probabilities. Most factorial studies use balanced or block randomization, with an equal number of patients assigned to each treatment combination, irrespective of the specific goals of the trial. Here, we introduce a class of Bayesian response-adaptive designs for factorial clinical trials with binary outcomes. The study design was developed using Bayesian decision-theoretic arguments and adapts the randomization probabilities to treatment combinations during the enrollment period based on the available data. Our approach enables the investigator to specify a utility function representative of the aims of the trial, and the Bayesian response-adaptive randomization algorithm aims to maximize this utility function. We considered several utility functions and factorial designs tailored to them. Then, we conducted a comparative simulation study to illustrate relevant differences of key operating characteristics across the resulting designs. We also investigated the asymptotic behavior of the proposed adaptive designs. We also used data summaries from three recent factorial trials in perioperative care, smoking cessation, and infectious disease prevention to define realistic simulation scenarios and illustrate advantages of the introduced trial designs compared to other study designs.

Inference in response-adaptive clinical trials when the enrolled population varies over time.

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.

Looking ahead in early-phase trial design to improve the drug development process: examples in oncology.

BACKGROUND: Clinical trial design must consider the specific resource constraints and overall goals of the drug development process (DDP); for example, in designing a phase I trial to evaluate the safety of a drug and recommend a dose for a subsequent phase II trial. Here, we focus on design considerations that involve the sequence of clinical trials, from early phase I to late phase III, that constitute the DDP. METHODS: We discuss how stylized simulation models of clinical trials in an oncology DDP can quantify important relationships between early-phase trial designs and their consequences for the remaining phases of development. Simulations for three illustrative settings are presented, using stylized models of the DDP that mimic trial designs and decisions, such as the potential discontinuation of the DDP. RESULTS: We describe: (1) the relationship between a phase II single-arm trial sample size and the likelihood of a positive result in a subsequent phase III confirmatory trial; (2) the impact of a phase I dose-finding design on the likelihood that the DDP will produce evidence of a safe and effective therapy; and (3) the impact of a phase II enrichment trial design on the operating characteristics of a subsequent phase III confirmatory trial. CONCLUSIONS: Stylized models of the DDP can support key decisions, such as the sample size, in the design of early-phase trials. Simulation models can be used to estimate performance metrics of the DDP under realistic scenarios; for example, the duration and the total number of patients enrolled. These estimates complement the evaluation of the operating characteristics of early-phase trial design, such as power or accuracy in selecting safe and effective dose levels.

BRCA1 recruitment to transcriptional pause sites is required for R-loop-driven DNA damage repair.

The mechanisms contributing to transcription-associated genomic instability are both complex and incompletely understood. Although R-loops are normal transcriptional intermediates, they are also associated with genomic instability. Here, we show that BRCA1 is recruited to R-loops that form normally over a subset of transcription termination regions. There it mediates the recruitment of a specific, physiological binding partner, senataxin (SETX). Disruption of this complex led to R-loop-driven DNA damage at those loci as reflected by adjacent γ-H2AX accumulation and ssDNA breaks within the untranscribed strand of relevant R-loop structures. Genome-wide analysis revealed widespread BRCA1 binding enrichment at R-loop-rich termination regions (TRs) of actively transcribed genes. Strikingly, within some of these genes in BRCA1 null breast tumors, there are specific insertion/deletion mutations located close to R-loop-mediated BRCA1 binding sites within TRs. Thus, BRCA1/SETX complexes support a DNA repair mechanism that addresses R-loop-based DNA damage at transcriptional pause sites.

Integration of survival data from multiple studies.

We introduce a statistical procedure that integrates datasets from multiple biomedical studies to predict patients' survival, based on individual clinical and genomic profiles. The proposed procedure accounts for potential differences in the relation between predictors and outcomes across studies, due to distinct patient populations, treatments and technologies to measure outcomes and biomarkers. These differences are modeled explicitly with study-specific parameters. We use hierarchical regularization to shrink the study-specific parameters towards each other and to borrow information across studies. The estimation of the study-specific parameters utilizes a similarity matrix, which summarizes differences and similarities of the relations between covariates and outcomes across studies. We illustrate the method in a simulation study and using a collection of gene expression datasets in ovarian cancer. We show that the proposed model increases the accuracy of survival predictions compared to alternative meta-analytic methods.

Approximating the Operating Characteristics of Bayesian Uncertainty Directed Trial Designs.

Bayesian response adaptive clinical trials are currently evaluating experimental therapies for several diseases. Adaptive decisions, such as pre-planned variations of the randomization probabilities, attempt to accelerate the development of new treatments. The design of response adaptive trials, in most cases, requires time consuming simulation studies to describe operating characteristics, such as type I/II error rates, across plausible scenarios. We investigate large sample approximations of pivotal operating characteristics in Bayesian Uncertainty directed trial Designs (BUDs). A BUD trial utilizes an explicit metric u to quantify the information accrued during the study on parameters of interest, for example the treatment effects. The randomization probabilities vary during time to minimize the uncertainty summary u at completion of the study. We provide an asymptotic analysis (i) of the allocation of patients to treatment arms and (ii) of the randomization probabilities. For BUDs with outcome distributions belonging to the natural exponential family with quadratic variance function, we illustrate the asymptotic normality of the number of patients assigned to each arm and of the randomization probabilities. We use these results to approximate relevant operating characteristics such as the power of the BUD. We evaluate the accuracy of the approximations through simulations under several scenarios for binary, time-to-event and continuous outcome models.

Leveraging external data in the design and analysis of clinical trials in neuro-oncology.

Integration of external control data, with patient-level information, in clinical trials has the potential to accelerate the development of new treatments in neuro-oncology by contextualising single-arm studies and improving decision making (eg, early stopping decisions). Based on a series of presentations at the 2020 Clinical Trials Think Tank hosted by the Society of Neuro-Oncology, we provide an overview on the use of external control data representative of the standard of care in the design and analysis of clinical trials. High-quality patient-level records, rigorous methods, and validation analyses are necessary to effectively leverage external data. We review study designs, statistical methods, risks, and potential distortions in using external data from completed trials and real-world data, as well as data sources, data sharing models, ongoing work, and applications in glioblastoma.

Adding experimental arms to platform clinical trials: randomization procedures and interim analyses.

Multi-arm clinical trials use a single control arm to evaluate multiple experimental treatments. In most cases this feature makes multi-arm studies considerably more efficient than two-arm studies. A bottleneck for implementation of a multi-arm trial is the requirement that all experimental treatments have to be available at the enrollment of the first patient. New drugs are rarely at the same stage of development. These limitations motivate our study of statistical methods for adding new experimental arms after a clinical trial has started enrolling patients. We consider both balanced and outcome-adaptive randomization methods for experimental designs that allow investigators to add new arms, discuss their application in a tuberculosis trial, and evaluate the proposed designs using a set of realistic simulation scenarios. Our comparisons include two-arm studies, multi-arm studies, and the proposed class of designs in which new experimental arms are added to the trial at different time points.

A Bayesian method for detecting pairwise associations in compositional data.

Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats.

Bayesian response-adaptive designs for basket trials.

We develop a general class of response-adaptive Bayesian designs using hierarchical models, and provide open source software to implement them. Our work is motivated by recent master protocols in oncology, where several treatments are investigated simultaneously in one or multiple disease types, and treatment efficacy is expected to vary across biomarker-defined subpopulations. Adaptive trials such as I-SPY-2 (Barker et al., 2009) and BATTLE (Zhou et al., 2008) are special cases within our framework. We discuss the application of our adaptive scheme to two distinct research goals. The first is to identify a biomarker subpopulation for which a therapy shows evidence of treatment efficacy, and to exclude other subpopulations for which such evidence does not exist. This leads to a subpopulation-finding design. The second is to identify, within biomarker-defined subpopulations, a set of cancer types for which an experimental therapy is superior to the standard-of-care. This goal leads to a subpopulation-stratified design. Using simulations constructed to faithfully represent ongoing cancer sequencing projects, we quantify the potential gains of our proposed designs relative to conventional non-adaptive designs.

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.

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.

Decreasing Opioid Addiction and Diversion Using Behavioral Economics Applied Through a Digital Engagement Solution: Protocol for a Randomized Controlled Trial.

BACKGROUND: Despite strong and growing interest in ending the ongoing opioid health crisis, there has been limited success in reducing the prevalence of opioid addiction and the number of deaths associated with opioid overdoses. Further, 1 explanation for this is that existing interventions target those who are opiate-dependent but do not prevent opioid-naïve patients from becoming addicted. OBJECTIVE: Leveraging behavioral economics at the patient level could help patients successfully use, discontinue, and dispose of their opioid medications in an acute pain setting. The primary goal of this project is to evaluate the effect of the 3 versions of the Opioid Management for You (OPY) tool on measures of opioid use relative to the standard of care by leveraging a pragmatic randomized controlled trial (RCT). METHODS: A team of researchers from the Center for Learning Health System Sciences (CLHSS) at the University of Minnesota partnered with M Health Fairview to design, build, and test the 3 versions of the OPY tool: social influence, precommitment, and testimonial version. The tool is being built using the Epic Care Companion (Epic Inc) platform and interacts with the patient through their existing MyChart (Epic Systems Corporation) personal health record account, and Epic patient portal, accessed through a phone app or the MyChart website. We have demonstrated feasibility with pilot data of the social influence version of the OPY app by targeting our pilot to a specific cohort of patients undergoing upper-extremity procedures. This study will use a group sequential RCT design to test the impact of this important health system initiative. Patients who meet OPY inclusion criteria will be stratified into low, intermediate, and high risk of opiate use based on their type of surgery. RESULTS: This study is being funded and supported by the CLHSS Rapid Prospective Evaluation and Digital Technology Innovation Programs, and M Health Fairview. Support and coordination provided by CLHSS include the structure of engagement, survey development, data collection, statistical analysis, and dissemination. The project was initially started in August 2022. The pilot was launched in February 2023 and is still running, with the data last counted in August 2023. The actual RCT is planned to start by early 2024. CONCLUSIONS: Through this RCT, we will test our hypothesis that patient opioid use and diverted prescription opioid availability can both be improved by information delivery applied through a behavioral economics lens via sending nudges directly to the opioid users through their personal health record. TRIAL REGISTRATION: ClinicalTrials.gov NCT06124079; https://clinicaltrials.gov/study/NCT06124079. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52882.

Prospectively shared control data across concurrent randomised clinical trials.

Sharing data from control groups across concurrent randomised clinical trials with identical enrolment criteria and the same control therapy can translate into efficiencies for the drug development process. We discuss potential benefits and risks of prospective data-sharing plans for concurrent randomised trials.

Accessible Data Collections for Improved Decision Making in Neuro-Oncology Clinical Trials.

Drug development can be associated with slow timelines, particularly for rare or difficult-to-treat solid tumors such as glioblastoma. The use of external data in the design and analysis of trials has attracted significant interest because it has the potential to improve the efficiency and precision of drug development. A recurring challenge in the use of external data for clinical trials, however, is the difficulty in accessing high-quality patient-level data. Academic research groups generally do not have access to suitable datasets to effectively leverage external data for planning and analyses of new clinical trials. Given the need for resources to enable investigators to benefit from existing data assets, we have developed the Glioblastoma External (GBM-X) Data Platform which will allow investigators in neuro-oncology to leverage our data collection and obtain analyses. GBM-X strives to provide an uncomplicated process to use external data, contextualize single-arm trials, and improve inference on treatment effects early in drug development. The platform is designed to welcome interested collaborators and integrate new data into the platform, with the expectation that the data collection can continue to grow and remain updated. With such features, GBM-X is designed to help to accelerate evaluation of therapies, to grow with collaborations, and to serve as a model to improve drug discovery for rare and difficult-to-treat tumors in oncology.

Validation of Predictive Analyses for Interim Decisions in Clinical Trials.

PURPOSE: Adaptive clinical trials use algorithms to predict, during the study, patient outcomes and final study results. These predictions trigger interim decisions, such as early discontinuation of the trial, and can change the course of the study. Poor selection of the Prediction Analyses and Interim Decisions (PAID) plan in an adaptive clinical trial can have negative consequences, including the risk of exposing patients to ineffective or toxic treatments. METHODS: We present an approach that leverages data sets from completed trials to evaluate and compare candidate PAIDs using interpretable validation metrics. The goal is to determine whether and how to incorporate predictions into major interim decisions in a clinical trial. Candidate PAIDs can differ in several aspects, such as the prediction models used, timing of interim analyses, and potential use of external data sets. To illustrate our approach, we considered a randomized clinical trial in glioblastoma. The study design includes interim futility analyses on the basis of the predictive probability that the final analysis, at the completion of the study, will provide significant evidence of treatment effects. We examined various PAIDs with different levels of complexity to investigate if the use of biomarkers, external data, or novel algorithms improved interim decisions in the glioblastoma clinical trial. RESULTS: Validation analyses on the basis of completed trials and electronic health records support the selection of algorithms, predictive models, and other aspects of PAIDs for use in adaptive clinical trials. By contrast, PAID evaluations on the basis of arbitrarily defined ad hoc simulation scenarios, which are not tailored to previous clinical data and experience, tend to overvalue complex prediction procedures and produce poor estimates of trial operating characteristics such as power and the number of enrolled patients. CONCLUSION: Validation analyses on the basis of completed trials and real world data support the selection of predictive models, interim analysis rules, and other aspects of PAIDs in future clinical trials.

The use of external control data for predictions and futility interim analyses in clinical trials.

BACKGROUND: External control (EC) data from completed clinical trials and electronic health records can be valuable for the design and analysis of future clinical trials. We discuss the use of EC data for early stopping decisions in randomized clinical trials (RCTs). METHODS: We specify interim analyses (IAs) approaches for RCTs, which allow investigators to integrate external data into early futility stopping decisions. IAs utilize predictions based on early data from the RCT, possibly combined with external data. These predictions at IAs express the probability that the trial will generate significant evidence of positive treatment effects. The trial is discontinued if this predictive probability becomes smaller than a prespecified threshold. We quantify efficiency gains and risks associated with the integration of external data into interim decisions. We then analyze a collection of glioblastoma (GBM) data sets, to investigate if the balance of efficiency gains and risks justify the integration of external data into the IAs of future GBM RCTs. RESULTS: Our analyses illustrate the importance of accounting for potential differences between the distributions of prognostic variables in the RCT and in the external data to effectively leverage external data for interim decisions. Using GBM data sets, we estimate that the integration of external data increases the probability of early stopping of ineffective experimental treatments by up to 25% compared to IAs that do not leverage external data. Additionally, we observe a reduction of the probability of early discontinuation for effective experimental treatments, which improves the RCT power. CONCLUSION: Leveraging external data for IAs in RCTs can support early stopping decisions and reduce the number of enrolled patients when the experimental treatment is ineffective.

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.

The design and evaluation of hybrid controlled trials that leverage external data and randomization.

Patient-level data from completed clinical studies or electronic health records can be used in the design and analysis of clinical trials. However, these external data can bias the evaluation of the experimental treatment when the statistical design does not appropriately account for potential confounders. In this work, we introduce a hybrid clinical trial design that combines the use of external control datasets and randomization to experimental and control arms, with the aim of producing efficient inference on the experimental treatment effects. Our analysis of the hybrid trial design includes scenarios where the distributions of measured and unmeasured prognostic patient characteristics differ across studies. Using simulations and datasets from clinical studies in extensive-stage small cell lung cancer and glioblastoma, we illustrate the potential advantages of hybrid trial designs compared to externally controlled trials and randomized trial designs.

The effects of releasing early results from ongoing clinical trials.

Most trials do not release interim summaries on efficacy and toxicity of the experimental treatments being tested, with this information only released to the public after the trial has ended. While early release of clinical trial data to physicians and patients can inform enrollment decision making, it may also affect key operating characteristics of the trial, statistical validity and trial duration. We investigate the public release of early efficacy and toxicity results, during ongoing clinical studies, to better inform patients about their enrollment options. We use simulation models of phase II glioblastoma (GBM) clinical trials in which early efficacy and toxicity estimates are periodically released accordingly to a pre-specified protocol. Patients can use the reported interim efficacy and toxicity information, with the support of physicians, to decide which trial to enroll in. We describe potential effects on various operating characteristics, including the study duration, selection bias and power.