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.

A multi-arm multi-stage platform design that allows preplanned addition of arms while still controlling the family-wise error.

There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design, that allows additional arms to be added in a platform trial in a preplanned fashion, while still controlling the family-wise error rate, under the assumption of known number and timing of treatments to be added, and no time trends. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. We focus on power under the least favorable configuration which is the power of finding the treatment with a clinically relevant effect out of a set of treatments while the rest have an uninteresting treatment effect. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Compared to Bonferroni, the savings in the total maximum sample size are modest in a trial with three arms, <1% of the total sample size. However, the savings are more substantial in trials with more arms.

Adding experimental treatment arms to multi-arm multi-stage platform trials in progress.

Multi-arm multi-stage (MAMS) platform trials efficiently compare several treatments with a common control arm. Crucially MAMS designs allow for adjustment for multiplicity if required. If for example, the active treatment arms in a clinical trial relate to different dose levels or different routes of administration of a drug, the strict control of the family-wise error rate (FWER) is paramount. Suppose a further treatment becomes available, it is desirable to add this to the trial already in progress; to access both the practical and statistical benefits of the MAMS design. In any setting where control of the error rate is required, we must add corresponding hypotheses without compromising the validity of the testing procedure.To strongly control the FWER, MAMS designs use pre-planned decision rules that determine the recruitment of the next stage of the trial based on the available data. The addition of a treatment arm presents an unplanned change to the design that we must account for in the testing procedure. We demonstrate the use of the conditional error approach to add hypotheses to any testing procedure that strongly controls the FWER. We use this framework to add treatments to a MAMS trial in progress. Simulations illustrate the possible characteristics of such procedures.

A Complex Meta-Regression Model to Identify Effective Features of Interventions From Multi-Arm, Multi-Follow-Up Trials.

Network meta-analysis (NMA) combines evidence from multiple trials to compare the effectiveness of a set of interventions. In many areas of research, interventions are often complex, made up of multiple components or features. This makes it difficult to define a common set of interventions on which to perform the analysis. One approach to this problem is component network meta-analysis (CNMA) which uses a meta-regression framework to define each intervention as a subset of components whose individual effects combine additively. In this article, we are motivated by a systematic review of complex interventions to prevent obesity in children. Due to considerable heterogeneity across the trials, these interventions cannot be expressed as a subset of components but instead are coded against a framework of characteristic features. To analyse these data, we develop a bespoke CNMA-inspired model that allows us to identify the most important features of interventions. We define a meta-regression model with covariates on three levels: intervention, study, and follow-up time, as well as flexible interaction terms. By specifying different regression structures for trials with and without a control arm, we relax the assumption from previous CNMA models that a control arm is the absence of intervention components. Furthermore, we derive a correlation structure that accounts for trials with multiple intervention arms and multiple follow-up times. Although, our model was developed for the specifics of the obesity data set, it has wider applicability to any set of complex interventions that can be coded according to a set of shared features.

A flexible multi-metric Bayesian framework for decision-making in Phase II multi-arm multi-stage studies.

We propose a multi-metric flexible Bayesian framework to support efficient interim decision-making in multi-arm multi-stage phase II clinical trials. Multi-arm multi-stage phase II studies increase the efficiency of drug development, but early decisions regarding the futility or desirability of a given arm carry considerable risk since sample sizes are often low and follow-up periods may be short. Further, since intermediate outcomes based on biomarkers of treatment response are rarely perfect surrogates for the primary outcome and different trial stakeholders may have different levels of risk tolerance, a single hypothesis test is insufficient for comprehensively summarizing the state of the collected evidence. We present a Bayesian framework comprised of multiple metrics based on point estimates, uncertainty, and evidence towards desired thresholds (a Target Product Profile) for (1) ranking of arms and (2) comparison of each arm against an internal control. Using a large public-private partnership targeting novel TB arms as a motivating example, we find via simulation study that our multi-metric framework provides sufficient confidence for decision-making with sample sizes as low as 30 patients per arm, even when intermediate outcomes have only moderate correlation with the primary outcome. Our reframing of trial design and the decision-making procedure has been well-received by research partners and is a practical approach to more efficient assessment of novel therapeutics.

Multi-arm multi-stage (MAMS) randomised selection designs: impact of treatment selection rules on the operating characteristics.

BACKGROUND: Multi-arm multi-stage (MAMS) randomised trial designs have been proposed to evaluate multiple research questions in the confirmatory setting. In designs with several interventions, such as the 8-arm 3-stage ROSSINI-2 trial for preventing surgical wound infection, there are likely to be strict limits on the number of individuals that can be recruited or the funds available to support the protocol. These limitations may mean that not all research treatments can continue to accrue the required sample size for the definitive analysis of the primary outcome measure at the final stage. In these cases, an additional treatment selection rule can be applied at the early stages of the trial to restrict the maximum number of research arms that can progress to the subsequent stage(s). This article provides guidelines on how to implement treatment selection within the MAMS framework. It explores the impact of treatment selection rules, interim lack-of-benefit stopping boundaries and the timing of treatment selection on the operating characteristics of the MAMS selection design. METHODS: We outline the steps to design a MAMS selection trial. Extensive simulation studies are used to explore the maximum/expected sample sizes, familywise type I error rate (FWER), and overall power of the design under both binding and non-binding interim stopping boundaries for lack-of-benefit. RESULTS: Pre-specification of a treatment selection rule reduces the maximum sample size by approximately 25% in our simulations. The familywise type I error rate of a MAMS selection design is smaller than that of the standard MAMS design with similar design specifications without the additional treatment selection rule. In designs with strict selection rules - for example, when only one research arm is selected from 7 arms - the final stage significance levels can be relaxed for the primary analyses to ensure that the overall type I error for the trial is not underspent. When conducting treatment selection from several treatment arms, it is important to select a large enough subset of research arms (that is, more than one research arm) at early stages to maintain the overall power at the pre-specified level. CONCLUSIONS: Multi-arm multi-stage selection designs gain efficiency over the standard MAMS design by reducing the overall sample size. Diligent pre-specification of the treatment selection rule, final stage significance level and interim stopping boundaries for lack-of-benefit are key to controlling the operating characteristics of a MAMS selection design. We provide guidance on these design features to ensure control of the operating characteristics.

Coupling enterotoxigenic Escherichia coli heat-stable peptide toxin with 8-arm PEG enhances immunogenicity.

Enterotoxigenic Escherichia coli (ETEC) strains, which produce the heat-stable enterotoxin (ST) either alone or in combination with the heat-labile enterotoxin, contribute to the bulk of the burden of child diarrheal disease in resource-limited countries and are associated with mortality. Developing an effective vaccine targeting ST presents challenges due to its potent enterotoxicity, non-immunogenicity, and the risk of autoimmune reaction stemming from its structural similarity to the human endogenous ligands, guanylin, and uroguanylin. This study aimed to assess a novel synthetic vaccine carrier platform employing a single chemical coupling step for making human ST (STh) immunogenic. Specifically, the method involved cross-linking STh to an 8-arm N-hydroxysuccinimide (NHS) ester-activated PEG cross-linker. A conjugate of STh with 8-arm structure was prepared, and its formation was confirmed through immunoblotting analysis. The impact of conjugation on STh epitopes was assessed using ELISAs with polyclonal and monoclonal antibodies targeting various epitopes of STh. Immunization of mice with the conjugate induced the production of anti-STh antibodies, exhibiting neutralizing activity against STh.

Towards a multi-arm multi-stage platform trial of disease modifying approaches in Parkinson's disease.

An increase in the efficiency of clinical trial conduct has been successfully demonstrated in the oncology field, by the use of multi-arm, multi-stage trials allowing the evaluation of multiple therapeutic candidates simultaneously, and seamless recruitment to phase 3 for those candidates passing an interim signal of efficacy. Replicating this complex innovative trial design in diseases such as Parkinson's disease is appealing, but in addition to the challenges associated with any trial assessing a single potentially disease modifying intervention in Parkinson's disease, a multi-arm platform trial must also specifically consider the heterogeneous nature of the disease, alongside the desire to potentially test multiple treatments with different mechanisms of action. In a multi-arm trial, there is a need to appropriately stratify treatment arms to ensure each are comparable with a shared placebo/standard of care arm; however, in Parkinson's disease there may be a preference to enrich an arm with a subgroup of patients that may be most likely to respond to a specific treatment approach. The solution to this conundrum lies in having clearly defined criteria for inclusion in each treatment arm as well as an analysis plan that takes account of predefined subgroups of interest, alongside evaluating the impact of each treatment on the broader population of Parkinson's disease patients. Beyond this, there must be robust processes of treatment selection, and consensus derived measures to confirm target engagement and interim assessments of efficacy, as well as consideration of the infrastructure needed to support recruitment, and the long-term funding and sustainability of the platform. This has to incorporate the diverse priorities of clinicians, triallists, regulatory authorities and above all the views of people with Parkinson's disease.

A comparison of alternative ranking methods in two-stage clinical trials with multiple interventions: An application to the anxiolysis for laceration repair in children trial.

BACKGROUND/AIMS: Multi-arm, multi-stage trials frequently include a standard care to which all interventions are compared. This may increase costs and hinders comparisons among the experimental arms. Furthermore, the standard care may not be evident, particularly when there is a large variation in standard practice. Thus, we aimed to develop an adaptive clinical trial that drops ineffective interventions following an interim analysis before selecting the best intervention at the final stage without requiring a standard care. METHODS: We used Bayesian methods to develop a multi-arm, two-stage adaptive trial and evaluated two different methods for ranking interventions, the probability that each intervention was optimal (Pbest) and using the surface under the cumulative ranking curve (SUCRA), at both the interim and final analysis. The proposed trial design determines the maximum sample size for each intervention using the Average Length Criteria. The interim analysis takes place at approximately half the pre-specified maximum sample size and aims to drop interventions for futility if either Pbest or the SUCRA is below a pre-specified threshold. The final analysis compares all remaining interventions at the maximum sample size to conclude superiority based on either Pbest or the SUCRA. The two ranking methods were compared across 12 scenarios that vary the number of interventions and the assumed differences between the interventions. The thresholds for futility and superiority were chosen to control type 1 error, and then the predictive power and expected sample size were evaluated across scenarios. A trial comparing three interventions that aim to reduce anxiety for children undergoing a laceration repair in the emergency department was then designed, known as the Anxiolysis for Laceration Repair in Children Trial (ALICE) trial. RESULTS: As the number of interventions increases, the SUCRA results in a higher predictive power compared with Pbest. Using Pbest results in a lower expected sample size when there is an effective intervention. Using the Average Length Criterion, the ALICE trial has a maximum sample size for each arm of 100 patients. This sample size results in a 86% and 85% predictive power using Pbest and the SUCRA, respectively. Thus, we chose Pbest as the ranking method for the ALICE trial. CONCLUSION: Bayesian ranking methods can be used in multi-arm, multi-stage trials with no clear control intervention. When more interventions are included, the SUCRA results in a higher power than Pbest. Future work should consider whether other ranking methods may also be relevant for clinical trial design.

Multi-arm multi-stage survival trial design with arm-specific stopping rule.

Traditional two-arm randomized trial designs have played a pivotal role in establishing the efficacy of medical interventions. However, their efficiency is often compromised when confronted with multiple experimental treatments or limited resources. In response to these challenges, the multi-arm multi-stage designs have emerged, enabling the simultaneous evaluation of multiple treatments within a single trial. In such an approach, if an arm meets efficacy success criteria at an interim stage, the whole trial stops and the arm is selected for further study. However when multiple treatment arms are active, stopping the trial at the moment one arm achieves success diminishes the probability of selecting the best arm. To address this issue, we have developed a group sequential multi-arm multi-stage survival trial design with an arm-specific stopping rule. The proposed method controls the familywise type I error in a strong sense and selects the best promising treatment arm with a high probability.

Adjusting for treatment selection in phase II/III clinical trials with time to event data.

Phase II/III clinical trials are efficient two-stage designs that test multiple experimental treatments. In stage 1, patients are allocated to the control and all experimental treatments, with the data collected from them used to select experimental treatments to continue to stage 2. Patients recruited in stage 2 are allocated to the selected treatments and the control. Combined data of stage 1 and stage 2 are used for a confirmatory phase III analysis. Appropriate analysis needs to adjust for selection bias of the stage 1 data. Point estimators exist for normally distributed outcome data. Extending these estimators to time to event data is not straightforward because treatment selection is based on correlated treatment effects and stage 1 patients who do not get events in stage 1 are followed-up in stage 2. We have derived an approximately uniformly minimum variance conditional unbiased estimator (UMVCUE) and compared its biases and mean squared errors to existing bias adjusted estimators. In simulations, one existing bias adjusted estimator has similar properties as the practically unbiased UMVCUE while the others can have noticeable biases but they are less variable than the UMVCUE. For confirmatory phase II/III clinical trials where unbiased estimators are desired, we recommend the UMVCUE or the existing estimator with which it has similar properties.

A Bayesian multi-arm multi-stage clinical trial design incorporating information about treatment ordering.

Multi-Arm Multi-Stage (MAMS) designs can notably improve efficiency in later stages of drug development, but they can be suboptimal when an order in the effects of the arms can be assumed. In this work, we propose a Bayesian multi-arm multi-stage trial design that selects all promising treatments with high probability and can efficiently incorporate information about the order in the treatment effects as well as incorporate prior knowledge on the treatments. A distinguishing feature of the proposed design is that it allows taking into account the uncertainty of the treatment effect order assumption and does not assume any parametric arm-response model. The design can provide control of the family-wise error rate under specific values of the control mean and we illustrate its operating characteristics in a study of symptomatic asthma. Via simulations, we compare the novel Bayesian design with frequentist multi-arm multi-stage designs and a frequentist order restricted design that does not account for the order uncertainty and demonstrate the gains in the sample sizes the proposed design can provide. We also find that the proposed design is robust to violations of the assumptions on the order.

Fully order restricted multi-arm multi-stage clinical trial design.

We consider a multi-arm trial with two or more active treatments plus a control where it is reasonable to assume an order for the treatment effects of the active arms compared to control. For example, the arms could be a high dose and low dose of a new drug and a placebo. The objective of the trial is to compare each active arm to control while maintaining strong control of the type 1 error rate. We show that when the study is powered to identify all promising treatments, a design that uses the order of the treatment effects to calculate the test statistic and to set the order of testing requires a smaller sample size than a design where each active arm is tested against the control arm independently. Under the considered settings, the sample size for a single-stage trial and a two-stage trial was reduced by at least 20%.

Group sequential multi-arm multi-stage trial design with treatment selection.

A multi-arm trial allows simultaneous comparison of multiple experimental treatments with a common control and provides a substantial efficiency advantage compared to the traditional randomized controlled trial. Many novel multi-arm multi-stage (MAMS) clinical trial designs have been proposed. However, a major hurdle to adopting the group sequential MAMS routinely is the computational effort of obtaining total sample size and sequential stopping boundaries. In this paper, we develop a group sequential MAMS trial design based on the sequential conditional probability ratio test. The proposed method provides analytical solutions for futility and efficacy boundaries to an arbitrary number of stages and arms. Thus, it avoids complicated computational effort for the methods proposed by Magirr et al. Simulation results showed that the proposed method has several advantages compared to the methods implemented in R package MAMS by Magirr et al.

Two-stage response adaptive randomization designs for multi-arm trials with binary outcome.

In recent years, adaptive randomization methods have gained significant popularity in clinical research and trial design due to their ability to provide both efficiency and flexibility in adjusting the statistical procedures of ongoing clinical trials. For a study to compare multiple treatments, a multi-arm two-stage design could be utilized to select the best treatment from the first stage and further compare that treatment with control in the second stage. The traditional design used equal randomization in both stages. To better utilize the interim results from the first stage, we propose to develop response adaptive randomization two-stage designs for a multi-arm clinical trial with binary outcome. Two allocation methods are considered: (1) an optimal allocation based on a sequential design; (2) the play-the-winner rule. Optimal multi-arm two-stage designs are obtained under three criteria: minimizing the expected number of failures, minimizing the average expected sample size, and minimizing the expected sample size under the null hypothesis. Simulation studies show that the proposed adaptive design based on the play-the-winner rule has good performance. A phase II trial for patients with pancreas adenocarcinoma and a germline BRCA/PALB2 mutation was used to illustrate the application of the proposed response adaptive randomization designs.

Group sequential multi-arm multi-stage survival trial design with treatment selection.

Multi-arm trials are increasingly of interest because for many diseases; there are multiple experimental treatments available for testing efficacy. Several novel multi-arm multi-stage (MAMS) clinical trial designs have been proposed. However, a major hurdle to adopting the group sequential MAMS routinely is the computational effort of obtaining stopping boundaries. For example, the method of Jaki and Magirr for time-to-event endpoint, implemented in R package MAMS, requires complicated computational efforts to obtain stopping boundaries. In this study, we develop a group sequential MAMS survival trial design based on the sequential conditional probability ratio test. The proposed method is an improvement of the Jaki and Magirr's method in the following three directions. First, the proposed method provides explicit solutions for both futility and efficacy boundaries to an arbitrary number of stages and arms. Thus, it avoids complicated computational efforts for the trial design. Second, the proposed method provides an accurate number of events for the fixed sample and group sequential designs. Third, the proposed method uses a new procedure for interim analysis which preserves the study power.

Determining the minimum duration of treatment in tuberculosis: An order restricted non-inferiority trial design.

Tuberculosis (TB) is one of the biggest killers among infectious diseases worldwide. Together with the identification of drugs that can provide benefits to patients, the challenge in TB is also the optimisation of the duration of these treatments. While conventional duration of treatment in TB is 6 months, there is evidence that shorter durations might be as effective but could be associated with fewer side effects and may be associated with better adherence. Based on a recent proposal of an adaptive order-restricted superiority design that employs the ordering assumptions within various duration of the same drug, we propose a non-inferiority (typically used in TB trials) adaptive design that effectively uses the order assumption. Together with the general construction of the hypothesis testing and expression for type I and type II errors, we focus on how the novel design was proposed for a TB trial concept. We consider a number of practical aspects such as choice of the design parameters, randomisation ratios, and timings of the interim analyses, and how these were discussed with the clinical team.

Feasibility of a Platform Trial Design for the Development of Mobile Health Applications: A Review.

Background: A novel adaptive trial design called platform trials (PTs) may offer an effective, efficient, and unbiased approach to evaluate different developer versions of mobile health (m-health) apps. However, the feasibility of their use for this purpose is yet to be explored. Objective: This literature review aims to explore the reported challenges associated with the adaptive PT design to assess its feasibility for the development of m-health apps. Methods: A descriptive literature review using two databases (MEDLINE and Embase) was conducted. Documents published in English between 1947 and September 20, 2020, were eligible for inclusion. Results: The titles and abstracts of 758 records were screened after which 179 full-text articles were assessed for eligibility. A total of 41 articles were included in the synthesis, all published after the year 2000. The synthesis yielded eight distinct categories of challenging issues with PTs relevant to their application in m-health app development, along with potential solutions. These categories are ethical issues (e.g., related to informed consent, equipoise, justice) (with 19 articles contributing content), biases (7 articles), temporal drift (4 articles), miscellaneous statistical issues (3 articles), logistical issues (e.g., cost and human resources, frequent amendments; 6 articles), sample size and power conflict (2 articles), generalizability of the results (2 articles), and operational challenges (1 article). Conclusion: Although PT designs are relatively new, they have promising feasibility for the seamless evaluation of interventions that undergo continuous development, including m-health apps; however, various challenges may hinder their successful implementation.

Benzotriazole-Based 3D Four-Arm Small Molecules Enable 19.1 % Efficiency for PM6 : Y6-Based Ternary Organic Solar Cells.

A third component featuring a planar backbone structure similar to the binary host molecule has been the preferred ingredient for improving the photovoltaic performance of ternary organic solar cells (OSCs). In this work, we explored a new avenue that introduces 3D-structured molecules as guest acceptors. Spirobifluorene (SF) is chosen as the core to combine with three different terminal-modified (rhodanine, thiazolidinedione, and dicyano-substituted rhodanine) benzotriazole (BTA) units, affording three four-arm molecules, SF-BTA1, SF-BTA2, and SF-BTA3, respectively. After adding these three materials to the classical system PM6 : Y6, the resulting ternary devices obtained ultra-high power-conversion efficiencies (PCEs) of 19.1 %, 18.7 %, and 18.8 %, respectively, compared with the binary OSCs (PCE=17.4 %). SF-BTA1-3 can work as energy donors to increase charge generation via energy transfer. In addition, the charge transfer between PM6 and SF-BTA1-3 also acts to enhance charge generation. Introducing SF-BTA1-3 could form acceptor alloys to modify the molecular energy level and inhibit the self-aggregation of Y6, thereby reducing energy loss and balancing charge transport. Our success in 3D multi-arm materials as the third component shows good universality and brings a new perspective. The further functional development of multi-arm materials could make OSCs more stable and efficient.

Stabilized direct learning for efficient estimation of individualized treatment rules.

In recent years, the field of precision medicine has seen many advancements. Significant focus has been placed on creating algorithms to estimate individualized treatment rules (ITRs), which map from patient covariates to the space of available treatments with the goal of maximizing patient outcome. Direct learning (D-Learning) is a recent one-step method which estimates the ITR by directly modeling the treatment-covariate interaction. However, when the variance of the outcome is heterogeneous with respect to treatment and covariates, D-Learning does not leverage this structure. Stabilized direct learning (SD-Learning), proposed in this paper, utilizes potential heteroscedasticity in the error term through a residual reweighting which models the residual variance via flexible machine learning algorithms such as XGBoost and random forests. We also develop an internal cross-validation scheme which determines the best residual model among competing models. SD-Learning improves the efficiency of D-Learning estimates in binary and multi-arm treatment scenarios. The method is simple to implement and an easy way to improve existing algorithms within the D-Learning family, including original D-Learning, Angle-based D-Learning (AD-Learning), and Robust D-learning (RD-Learning). We provide theoretical properties and justification of the optimality of SD-Learning. Head-to-head performance comparisons with D-Learning methods are provided through simulations, which demonstrate improvement in terms of average prediction error (APE), misclassification rate, and empirical value, along with a data analysis of an acquired immunodeficiency syndrome (AIDS) randomized clinical trial.