Cross-validated risk scores adaptive enrichment (CADEN) design.

We propose a Cross-validated ADaptive ENrichment design (CADEN) in which a trial population is enriched with a subpopulation of patients who are predicted to benefit from the treatment more than an average patient (the sensitive group). This subpopulation is found using a risk score constructed from the baseline (potentially high-dimensional) information about patients. The design incorporates an early stopping rule for futility. Simulation studies are used to assess the properties of CADEN against the original (non-enrichment) cross-validated risk scores (CVRS) design that constructs a risk score at the end of the trial. We show that when there exists a sensitive group of patients, CADEN achieves a higher power and a reduction in the expected sample size, in comparison to the CVRS design. We illustrate the application of the design in two real clinical trials. We conclude that the new design offers improved statistical efficiency in comparison to the existing non-enrichment method, as well as increased benefit to patients. The method has been implemented in an R package caden.

Challenges of designing and conducting cohort studies and clinical trials in populations of pregnant people.

Rheumatic and musculoskeletal diseases often affect individuals of childbearing age. The incidence and prevalence of rheumatic and musculoskeletal diseases is rising. More pregnancies in patients with rheumatic and musculoskeletal diseases are anticipated and some rheumatic and musculoskeletal diseases are associated with pregnancy complications (eg, miscarriages, fetal deaths, preterm births, and hypertensive disorders in pregnancy). Despite the need to understand the use of drugs to treat rheumatic and musculoskeletal diseases in pregnancy, clinical trials in pregnancy are rare, therapeutics in pregnancy are understudied, and pregnant individuals are routinely excluded as premarketing trial participants. Data on the effectiveness and safety of disease-modifying antirheumatic drugs are most often based on post-marketing observational data. Observational studies assessing the bidirectional relationship between rheumatic and musculoskeletal diseases and pregnancy, as well as interventional studies of treatments during pregnancy, are scarce. Historical reluctance to perform studies in what was deemed an at-risk group persists in pharmaceutical companies, regulatory bodies, and ethics boards. Additionally, patients must be engaged partners, which requires trust that the research respects the needs and interests of the patient and complies with the rules intended to protect the pregnant person and the fetus from harm. In this Series paper, we share challenges we have encountered in conducting prospective cohort studies and interventional trials of postmarketing approved medications, assessing pregnancy specific outcomes in pregnant women with rheumatic and musculoskeletal diseases in the EU, the UK, and the USA. We discuss the changing landscape around trials in pregnancy and present possible solutions to our challenges.

Pulmonary Hypertension: Intensification and Personalization of Combination Rx (PHoenix): A phase IV randomized trial for the evaluation of dose-response and clinical efficacy of riociguat and selexipag using implanted technologies.

Approved therapies for the treatment of patients with pulmonary arterial hypertension (PAH) mediate pulmonary vascular vasodilatation by targeting distinct biological pathways. International guidelines recommend that patients with an inadequate response to dual therapy with a phosphodiesterase type-5 inhibitor (PDE5i) and endothelin receptor antagonist (ERA), are recommended to either intensify oral therapy by adding a selective prostacyclin receptor (IP) agonist (selexipag), or switching from PDE5i to a soluble guanylate-cyclase stimulator (sGCS; riociguat). The clinical equipoise between these therapeutic choices provides the opportunity for evaluation of individualized therapeutic effects. Traditionally, invasive/hospital-based investigations are required to comprehensively assess disease severity and demonstrate treatment benefits. Regulatory-approved, minimally invasive monitors enable equivalent measurements to be obtained while patients are at home. In this 2 × 2 randomized crossover trial, patients with PAH established on guideline-recommended dual therapy and implanted with CardioMEMS™ (a wireless pulmonary artery sensor) and ConfirmRx™ (an insertable cardiac rhythm monitor), will receive ERA + sGCS, or PDEi + ERA + IP agonist. The study will evaluate clinical efficacy via established clinical investigations and remote monitoring technologies, with remote data relayed through regulatory-approved online clinical portals. The primary aim will be the change in right ventricular systolic volume measured by magnetic resonance imaging (MRI) from baseline to maximal tolerated dose with each therapy. Using data from MRI and other outcomes, including hemodynamics, physical activity, physiological measurements, quality of life, and side effect reporting, we will determine whether remote technology facilitates early evaluation of clinical efficacy, and investigate intra-patient efficacy of the two treatment approaches.

Current practices in studies applying the target trial emulation framework: a protocol for a systematic review.

INTRODUCTION: Observational studies represent an alternative to estimate real-world causal effects in the absence of available randomised controlled trials (RCTs). Target trial emulation is a framework for the application of RCT design principles to emulate a hypothetical open-label RCT (the hypothetical target trial) using existing observational data as the primary data source as opposed to the prospective recruitment and measurement of randomised units. The aim of this systematic review is to investigate the practices of studies applying the target trial emulation framework to evaluate the effectiveness of interventions. METHODS AND ANALYSIS: We will systematically search in Medline (via Ovid), Embase (via Ovid, entries from medRxiv are included), PsycINFO (via Ovid), SCOPUS, Web of Science, Cochrane Library, the ISRCTN registry and ClinicalTrials.gov for all study reports and protocols which used the trial emulation framework (without time restriction). We will extract information concerning study design, data source, analysis, results, interpretation and dissemination. Two reviewers will perform study selection, data extraction and quality assessment. Disagreements between reviewers will be resolved by a third reviewer. A narrative approach will be used to synthesise and report qualitative and quantitative data. Reporting of the review will be informed by Preferred Reporting Items for Systematic Review and Meta-Analysis guidance (PRISMA). ETHICS AND DISSEMINATION: Ethical approval is not required as it is a protocol for a systematic review. Findings will be disseminated through peer-reviewed publications and conference presentations.

Online error rate control for platform trials.

Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I error rate, which is complicated by the fact that the hypotheses are tested at different times and are not necessarily pre-specified. Online error rate control methodology provides a possible solution to the problem of multiplicity for platform trials where a relatively large number of hypotheses are expected to be tested over time. In the online multiple hypothesis testing framework, hypotheses are tested one-by-one over time, where at each time-step an analyst decides whether to reject the current null hypothesis without knowledge of future tests but based solely on past decisions. Methodology has recently been developed for online control of the false discovery rate as well as the familywise error rate (FWER). In this article, we describe how to apply online error rate control to the platform trial setting, present extensive simulation results, and give some recommendations for the use of this new methodology in practice. We show that the algorithms for online error rate control can have a substantially lower FWER than uncorrected testing, while still achieving noticeable gains in power when compared with the use of a Bonferroni correction. We also illustrate how online error rate control would have impacted a currently ongoing platform trial.

Bayesian borrowing for basket trials with longitudinal outcomes.

Basket trials are a novel clinical trial design in which a single intervention is investigated in multiple patient subgroups, or "baskets." They offer the opportunity to share information between subgroups, potentially increasing power to detect treatment effects. Basket trials offer several advantages over running a series of separate trials, including reduced sample sizes, increased efficiency, and reduced costs. Primarily, basket trials have been undertaken in Phase II oncology settings, but could be a promising design in other areas where a shared underlying biological mechanism drives different diseases. One such area is chronic aging-related diseases. However, trials in this area frequently have longitudinal outcomes, and therefore suitable methods are needed to share information in this setting. In this paper, we extend three Bayesian borrowing methods for a basket design with continuous longitudinal endpoints. We demonstrate our methods on a real-world dataset and in a simulation study where the aim is to detect positive basketwise treatment effects. Methods are compared with standalone analysis of each basket without borrowing. Our results confirm that methods that share information can improve power to detect positive treatment effects and increase precision over independent analysis in many scenarios. In highly heterogeneous scenarios, there is a trade-off between increased power and increased risk of type I errors. Our proposed methods for basket trials with continuous longitudinal outcomes aim to facilitate their applicability in the area of aging related diseases. Choice of method should be made based on trial priorities and the expected basketwise distribution of treatment effects.

Four 2×2 factorial trials of smartphone CBT to reduce subthreshold depression and to prevent new depressive episodes among adults in the community-RESiLIENT trial (Resilience Enhancement with Smartphone in LIving ENvironmenTs): a master protocol.

INTRODUCTION: The health burden due to depression is ever increasing in the world. Prevention is a key to reducing this burden. Guided internet cognitive-behavioural therapies (iCBT) appear promising but there is room for improvement because we do not yet know which of various iCBT skills are more efficacious than others, and for whom. In addition, there has been no platform for iCBT that can accommodate ongoing evolution of internet technologies. METHODS AND ANALYSIS: Based on our decade-long experiences in developing smartphone CBT apps and examining them in randomised controlled trials, we have developed the Resilience Training App Version 2. This app now covers five CBT skills: cognitive restructuring, behavioural activation, problem-solving, assertion training and behaviour therapy for insomnia. The current study is designed as a master protocol including four 2×2 factorial trials using this app (1) to elucidate specific efficacies of each CBT skill, (2) to identify participants' characteristics that enable matching between skills and individuals, and (3) to allow future inclusion of new skills. We will recruit 3520 participants with subthreshold depression and ca 1700 participants without subthreshold depression, to examine the short-term efficacies of CBT skills to reduce depressive symptoms in the former and to explore the long-term efficacies in preventing depression in the total sample. The primary outcome for the short-term efficacies is the change in depressive symptoms as measured with the Patient Health Questionnaire-9 at week 6, and that for the long-term efficacies is the incidence of major depressive episodes as assessed by the computerised Composite International Diagnostic Interview by week 50. ETHICS AND DISSEMINATION: The trial has been approved by the Ethics Committee of Kyoto University Graduate School of Medicine (C1556). TRIAL REGISTRATION NUMBER: UMIN000047124.

Developing a predictive signature for two trial endpoints using the cross-validated risk scores method.

The existing cross-validated risk scores (CVRS) design has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using high-dimensional data (such as genetic data). The design is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure. In some settings, it is desirable to consider the tradeoff between two outcomes, such as efficacy and toxicity, or cost and effectiveness. With this motivation, we extend the CVRS design (CVRS2) to consider two outcomes. The design employs bivariate risk scores that are divided into clusters. We assess the properties of the CVRS2 using simulated data and illustrate its application on a randomized psychiatry trial. We show that CVRS2 is able to reliably identify the sensitive group (the group for which the new treatment provides benefit on both outcomes) in the simulated data. We apply the CVRS2 design to a psychology clinical trial that had offender status and substance use status as two outcomes and collected a large number of baseline covariates. The CVRS2 design yields a significant treatment effect for both outcomes, while the CVRS approach identified a significant effect for the offender status only after prefiltering the covariates.

Bayesian sample size determination using commensurate priors to leverage preexperimental data.

This paper develops Bayesian sample size formulae for experiments comparing two groups, where relevant preexperimental information from multiple sources can be incorporated in a robust prior to support both the design and analysis. We use commensurate predictive priors for borrowing of information and further place Gamma mixture priors on the precisions to account for preliminary belief about the pairwise (in)commensurability between parameters that underpin the historical and new experiments. Averaged over the probability space of the new experimental data, appropriate sample sizes are found according to criteria that control certain aspects of the posterior distribution, such as the coverage probability or length of a defined density region. Our Bayesian methodology can be applied to circumstances that compare two normal means, proportions, or event times. When nuisance parameters (such as variance) in the new experiment are unknown, a prior distribution can further be specified based on preexperimental data. Exact solutions are available based on most of the criteria considered for Bayesian sample size determination, while a search procedure is described in cases for which there are no closed-form expressions. We illustrate the application of our sample size formulae in the design of clinical trials, where pretrial information is available to be leveraged. Hypothetical data examples, motivated by a rare-disease trial with an elicited expert prior opinion, and a comprehensive performance evaluation of the proposed methodology are presented.

Bayesian sample size determination in basket trials borrowing information between subsets.

Basket trials are increasingly used for the simultaneous evaluation of a new treatment in various patient subgroups under one overarching protocol. We propose a Bayesian approach to sample size determination in basket trials that permit borrowing of information between commensurate subsets. Specifically, we consider a randomized basket trial design where patients are randomly assigned to the new treatment or control within each trial subset ("subtrial" for short). Closed-form sample size formulae are derived to ensure that each subtrial has a specified chance of correctly deciding whether the new treatment is superior to or not better than the control by some clinically relevant difference. Given prespecified levels of pairwise (in)commensurability, the subtrial sample sizes are solved simultaneously. The proposed Bayesian approach resembles the frequentist formulation of the problem in yielding comparable sample sizes for circumstances of no borrowing. When borrowing is enabled between commensurate subtrials, a considerably smaller trial sample size is required compared to the widely implemented approach of no borrowing. We illustrate the use of our sample size formulae with two examples based on real basket trials. A comprehensive simulation study further shows that the proposed methodology can maintain the true positive and false positive rates at desired levels.

Online multiple hypothesis testing.

Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological applications, an additional complexity is that hypotheses are tested in an online manner, one-by-one over time. However, traditional procedures that control the FDR, such as the Benjamini-Hochberg procedure, assume that all p-values are available to be tested at a single time point. To address these challenges, a new field of methodology has developed over the past 15 years showing how to control error rates for online multiple hypothesis testing. In this framework, hypotheses arrive in a stream, and at each time point the analyst decides whether to reject the current hypothesis based both on the evidence against it, and on the previous rejection decisions. In this paper, we present a comprehensive exposition of the literature on online error rate control, with a review of key theory as well as a focus on applied examples. We also provide simulation results comparing different online testing algorithms and an up-to-date overview of the many methodological extensions that have been proposed.

Design and analysis of umbrella trials: Where do we stand?

Background: The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention. Methods: We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology. Findings: We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse. Conclusions: Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.

Practical guidance for planning resources required to support publicly-funded adaptive clinical trials.

Adaptive designs are a class of methods for improving efficiency and patient benefit of clinical trials. Although their use has increased in recent years, research suggests they are not used in many situations where they have potential to bring benefit. One barrier to their more widespread use is a lack of understanding about how the choice to use an adaptive design, rather than a traditional design, affects resources (staff and non-staff) required to set-up, conduct and report a trial. The Costing Adaptive Trials project investigated this issue using quantitative and qualitative research amongst UK Clinical Trials Units. Here, we present guidance that is informed by our research, on considering the appropriate resourcing of adaptive trials. We outline a five-step process to estimate the resources required and provide an accompanying costing tool. The process involves understanding the tasks required to undertake a trial, and how the adaptive design affects them. We identify barriers in the publicly funded landscape and provide recommendations to trial funders that would address them. Although our guidance and recommendations are most relevant to UK non-commercial trials, many aspects are relevant more widely.

Subgroup analyses in randomized controlled trials frequently categorized continuous subgroup information.

BACKGROUND AND OBJECTIVES: To investigate how subgroup analyses of published Randomized Controlled Trials (RCTs) are performed when subgroups are created from continuous variables. METHODS: We carried out a review of RCTs published in 2016-2021 that included subgroup analyses. Information was extracted on whether any of the subgroups were based on continuous variables and, if so, how they were analyzed. RESULTS: Out of 428 reviewed papers, 258 (60.4%) reported RCTs with a subgroup analysis. Of these, 178/258 (69%) had at least one subgroup formed from a continuous variable and 14/258 (5.4%) were unclear. The vast majority (169/178, 94.9%) dichotomized the continuous variable and treated the subgroup as categorical. The most common way of dichotomizing was using a pre-specified cutpoint (129/169, 76.3%), followed by a data-driven cutpoint (26/169, 15.4%), such as the median. CONCLUSION: It is common for subgroup analyses to use continuous variables to define subgroups. The vast majority dichotomize the continuous variable and, consequently, may lose substantial amounts of statistical information (equivalent to reducing the sample size by at least a third). More advanced methods that can improve efficiency, through optimally choosing cutpoints or directly using the continuous information, are rarely used.

Adaptive Designs: Benefits and Cautions for Neurosurgery Trials.

BACKGROUND: It is well accepted that randomized controlled trials provide the greatest quality of evidence about effectiveness and safety of new interventions. In neurosurgery, randomized controlled trials face challenges, with their use remaining relatively low compared with other clinical areas. Adaptive designs have emerged as a method for improving the efficiency and patient benefit of trials. They allow modifications to the trial design to be made as patient outcome data are collected. The benefit they provide is highly variable, predominantly governed by the time taken to observe the primary endpoint compared with the planned recruitment rate. They also face challenges in design, conduct, and reporting. METHODS: We provide an overview of the benefits and challenges of adaptive designs, with a focus on neurosurgery applications. To investigate how often an adaptive design may be advantageous in neurosurgery, we extracted data on recruitment rates and endpoint lengths for ongoing neurosurgery trials registered in ClinicalTrials.gov. RESULTS: We found that a majority of neurosurgery trials had a relatively short endpoint length compared with the planned recruitment period and therefore may benefit from an adaptive trial. However, we did not identify any ongoing ClinicalTrials.gov registered neurosurgery trials that mentioned using an adaptive design. CONCLUSIONS: Adaptive designs may provide benefits to neurosurgery trials and should be considered for use more widely. Use of some types of adaptive design, such as multiarm multistage, may further increase the number of interventions that can be tested with limited patient and financial resources.

When is a two-stage single-arm trial efficient? An evaluation of the impact of outcome delay.

BACKGROUND: Simon's two-stage design is a widely used adaptive design, particularly in phase II oncology trials due to its simplicity and efficiency. However, its efficiency can be adversely affected when the primary end-point takes time to observe, as is common in practice. METHODS: We propose an optimal design, taking the delay in observing treatment outcome into consideration and compare the efficiency gained from using Simon's design over a single-stage design for real-life oncology trials. Based on the results, we provide a general rule-of-thumb for determining whether a two-stage single-arm design can provide any added advantage over a single-stage design, given the recruitment rate and primary end-point length. RESULTS: We observed an average 15-30% loss in the estimated efficiency gain in real oncology trials that used Simon's design due to the delay in observing the treatment outcome. The delay-optimal design provides some advantage over Simon's design in terms of reduced sample size when the delay is large compared to the recruitment length. DISCUSSION: Simon's two-stage design provides large benefit over a single-stage design, in terms of reduced sample size, when the primary end-point length is no more than 10% of the total recruitment time. It provides no efficiency advantage when this ratio is above 50%.

Sample size estimation using a latent variable model for mixed outcome co-primary, multiple primary and composite endpoints.

Mixed outcome endpoints that combine multiple continuous and discrete components are often employed as primary outcome measures in clinical trials. These may be in the form of co-primary endpoints, which conclude effectiveness overall if an effect occurs in all of the components, or multiple primary endpoints, which require an effect in at least one of the components. Alternatively, they may be combined to form composite endpoints, which reduce the outcomes to a one-dimensional endpoint. There are many advantages to joint modeling the individual outcomes, however in order to do this in practice we require techniques for sample size estimation. In this article we show how the latent variable model can be used to estimate the joint endpoints and propose hypotheses, power calculations and sample size estimation methods for each. We illustrate the techniques using a numerical example based on a four-dimensional endpoint and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required in the multiple primary case is similar to that needed for the outcome with the largest effect size. We show that the empirical power is achieved for each endpoint and that the FWER can be sufficiently controlled using a Bonferroni correction if the correlations between endpoints are less than 0.5. Otherwise, less conservative adjustments may be needed. We further illustrate empirically the efficiency gains that may be achieved in the composite endpoint setting.