Sample size adaptation designs and efficiency comparison with group sequential designs.

This study is to give a systematic account of sample size adaptation designs (SSADs) and to provide direct proof of the efficiency advantage of general SSADs over group sequential designs (GSDs) from a different perspective. For this purpose, a class of sample size mapping functions to define SSADs is introduced. Under the two-stage adaptive clinical trial setting, theorems are developed to describe the properties of SSADs. Sufficient conditions are derived and used to prove analytically that SSADs based on the weighted combination test can be uniformly more efficient than GSDs in a range of likely values of the true treatment difference δ $$ \delta $$ . As shown in various scenarios, given a GSD, a fully adaptive SSAD can be obtained that has sufficient statistical power similar to that of the GSD but has a smaller average sample size for all δ $$ \delta $$ in the range. The associated sample size savings can be substantial. A practical design example and suggestions on the steps to find efficient SSADs are also provided.

Modified Goldilocks Design with strict type I error control in confirmatory clinical trials.

Goldilocks Design (GD) utilizes predictive probability to adaptively select a trial's sample size based on accumulating data. In order to control type I error at a desired level for a subset of the null space, extensive simulations at the study design stage are required to choose critical values, which is a challenge for this type of Bayesian adaptive design to be used for confirmatory trials. In this article, we propose a Modified Goldilocks Design (MGD) where type I error is analytically controlled over the entire null space. We do so by applying the conditional invariance principle and a combination test approach on [Formula: see text]-values that are obtained from independent cohorts of subjects. Simulation studies show that despite analytic control of type I error rate, the proposed MGD has similar power when compared with the original GD. We further apply it to an example trial with time-to-event endpoint in oncology.

Bayesian Semi-parametric Design (BSD) for adaptive dose-finding with multiple strata.

In the era of precision medicine, it is of increasing interest to consider multiple strata (e.g. indications, regions, or subgroups) within a single oncology dose-finding study when identifying the maximum tolerated dose (MTD). We propose two Bayesian semi-parametric designs (BSD) for dose-finding with multiple strata to allow for both adaptively dosing patients based on various toxicity profiles and efficient identification of the MTD for each stratum. We develop non-parametric priors based on the Dirichlet process to allow for a flexible prior distribution and negate the need for a pre-specified exchangeability parameter. The two BSD models are built under different prior beliefs of strata heterogeneity and allow for appropriate borrowing of information across similar strata. Simulation studies are performed to evaluate the BSD model performance by comparing it with existing methods, including the fully stratified, exchangeability, and exchangeability-non-exchangeability models. In general, our BSD models outperform the competing methods in correctly identifying the MTD for different strata and necessitate a smaller sample size to determine the MTD. The BSD models are robust to various heterogeneity assumptions and can be easily extended to other binary and time to event endpoints.

Design optimization for dose-finding trials: a review.

Dose selection is one of the most difficult and crucial decisions to make during drug development. As a consequence, the dose-finding trial is a major milestone in the drug development plan and should be properly designed. This article will review the most recent methodologies for optimizing the design of dose-finding studies: all of them are based on the modeling of the dose-response curve, which is now the gold standard approach for analyzing dose-finding studies instead of the traditional ANOVA/multiple testing approach. We will address the optimization of both fixed and adaptive designs and briefly outline new methodologies currently under investigation, based on utility functions.

Adaptive treatment allocation for comparative clinical studies with recurrent events data.

In long-term clinical studies, recurrent event data are sometimes collected and used to contrast the efficacies of two different treatments. The event reoccurrence rates can be compared using the popular negative binomial model, which incorporates information related to patient heterogeneity into a data analysis. For treatment allocation, a balanced approach in which equal sample sizes are obtained for both treatments is predominately adopted. However, if one treatment is superior, then it may be desirable to allocate fewer subjects to the less-effective treatment. To accommodate this objective, a sequential response-adaptive treatment allocation procedure is derived based on the doubly adaptive biased coin design. Our proposed treatment allocation schemes have been shown to be capable of reducing the number of subjects receiving the inferior treatment while simultaneously retaining a test power level that is comparable to that of a balanced design. The redesign of a clinical study illustrates the advantages of using our procedure.

Quantification of prior impact in terms of effective current sample size.

Bayesian methods allow borrowing of historical information through prior distributions. The concept of prior effective sample size (prior ESS) facilitates quantification and communication of such prior information by equating it to a sample size. Prior information can arise from historical observations; thus, the traditional approach identifies the ESS with such a historical sample size. However, this measure is independent of newly observed data, and thus would not capture an actual "loss of information" induced by the prior in case of prior-data conflict. We build on a recent work to relate prior impact to the number of (virtual) samples from the current data model and introduce the effective current sample size (ECSS) of a prior, tailored to the application in Bayesian clinical trial designs. Special emphasis is put on robust mixture, power, and commensurate priors. We apply the approach to an adaptive design in which the number of recruited patients is adjusted depending on the effective sample size at an interim analysis. We argue that the ECSS is the appropriate measure in this case, as the aim is to save current (as opposed to historical) patients from recruitment. Furthermore, the ECSS can help overcome lack of consensus in the ESS assessment of mixture priors and can, more broadly, provide further insights into the impact of priors. An R package accompanies the paper.

A response-adaptive randomization procedure for multi-armed clinical trials with normally distributed outcomes.

We propose a novel response-adaptive randomization procedure for multi-armed trials with continuous outcomes that are assumed to be normally distributed. Our proposed rule is non-myopic, and oriented toward a patient benefit objective, yet maintains computational feasibility. We derive our response-adaptive algorithm based on the Gittins index for the multi-armed bandit problem, as a modification of the method first introduced in Villar et al. (Biometrics, 71, pp. 969-978). The resulting procedure can be implemented under the assumption of both known or unknown variance. We illustrate the proposed procedure by simulations in the context of phase II cancer trials. Our results show that, in a multi-armed setting, there are efficiency and patient benefit gains of using a response-adaptive allocation procedure with a continuous endpoint instead of a binary one. These gains persist even if an anticipated low rate of missing data due to deaths, dropouts, or complete responses is imputed online through a procedure first introduced in this paper. Additionally, we discuss how there are response-adaptive designs that outperform the traditional equal randomized design both in terms of efficiency and patient benefit measures in the multi-armed trial context.

Time-trend impact on treatment estimation in two-arm clinical trials with a binary outcome and Bayesian response adaptive randomization.

Clinical trial design and analysis often assume study population homogeneity, although patient baseline profile and standard of care may evolve over time, especially in trials with long recruitment periods. The time-trend phenomenon can affect the treatment estimation and the operating characteristics of trials with Bayesian response adaptive randomization (BRAR). The mechanism of time-trend impact on BRAR is increasingly being studied but some aspects remain unclear. The goal of this research is to quantify the bias in treatment effect estimation due to the use of BRAR in the presence of time-trend. In addition, simulations are conducted to compare the performance of three commonly used BRAR algorithms under different time-trend patterns with and without early stopping rules. The results demonstrate that using these BRAR methods in a two-arm trial with time-trend may cause type I error inflation and treatment effect estimation bias. The magnitude and direction of the bias are affected by the parameters of the BRAR algorithm and the time-trend pattern.

Optimal adaptive single-arm phase II trials under quantified uncertainty.

Single-arm trials with binary endpoint are firmly established in e.g., early clinical oncology. Here, two-stage designs are often employed to allow early termination of the trial in the case of an unexpectedly large or small response rate to the new treatment. Various designs have been proposed over the last few years which usually require strong assumptions about the true response rate during planning. Often, these designs are not robust to deviations from the planning assumptions. In this paper, we define a Bayesian framework for scoring two-stage designs under uncertainty and investigate the characteristics of designs optimizing a commonly employed performance score of Liu et al. The resulting optimal designs are compared with an alternative, utility-based approach incorporating expected power and sample size. We provide insights in the underlying implicit assumptions of using expected power for scoring adaptive designs and relate the global score function to the practice of sample size recalculation based on conditional power. An in-depth comparison of the features of the different performance scores and their respective optimizing designs provides the guidance for practitioners who face the problem of choosing between the various options. A software implementation of the proposed methods is publicly available online.

Two-stage adaptive enrichment design for testing an active factor.

We propose an adaptive enrichment approach to test an active factor, which is a factor whose effect is non-zero in at least one subpopulation. We implement a two-stage play-the-winner design where all subjects in the second stage are enrolled from the subpopulation that has the highest observed effect in the first stage. We recommend a weighted Fisher's combination of the most powerful test for each stage, respectively: the first stage Hotelling's test and the second stage noncentral chi-square test. The test is further extended to cover binary outcomes and time-to-event outcomes.

Response adaptive randomization procedures in seamless phase II/III clinical trials.

It is desirable to work efficiently and cost effectively to evaluate new therapies in a time-sensitive and ethical manner without compromising the integrity and validity of the development process. The seamless phase II/III clinical trial has been proposed to meet this need, and its efficient, ethical and economic advantages can be strengthened by its combination with innovative response adaptive randomization (RAR) procedures. In particular, well-designed frequentist RAR procedures can target theoretically optimal allocation proportions, and there are explicit asymptotic results. However, there has been little research into seamless phase II/III clinical trials with frequentist RAR because of the difficulty in performing valid statistical inference and controlling the type I error rate. In this paper, we propose the framework for a family of frequentist RAR designs for seamless phase II/III trials, derive the asymptotic distribution of the parameter estimators using martingale processes and offer solutions to control the type I error rate. The numerical studies demonstrate our theoretical findings and the advantages of the proposed methods.

An adaptive trial design to optimize dose-schedule regimes with delayed outcomes.

This paper proposes a two-stage phase I-II clinical trial design to optimize dose-schedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decision-making is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design's performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.

TITE-BOIN-ET: Time-to-event Bayesian optimal interval design to accelerate dose-finding based on both efficacy and toxicity outcomes.

One of the primary purposes of an oncology dose-finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de-escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late-onset outcomes. To solve these issues, we propose the time-to-event Bayesian optimal interval design to accelerate dose-finding based on cumulative and pending data of both efficacy and toxicity. The new design, named "TITE-BOIN-ET" design, is nonparametric and a model-assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose-finding trials compared with the model-based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE-BOIN-ET design has advantages compared with the model-based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE-BOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose-finding trials.

[Global expansion of clinical trials: innovation and interaction]. / Expansão global dos ensaios clínicos: inovação e interação.

Innovation in health is characterized by strong interaction with the science and technology sector. Growing interest in the internationalization of research and development leads to questions on opportunities as a function of interactions with other countries, as a mechanism for national capacity-building in innovation. As clinical trials cross national borders and achieve global relevance, networks of relations between the actors become increasingly complex and present themselves as a possibility for characterizing interaction of national innovation systems at the global level, particularly from the point of view of producing activities in science, technology, and innovation in health. This study aimed to analyze the global expansion of clinical trials in order to discuss possible factors related to the interaction between national innovation systems in the countries involved. The methods included literature searches and analysis of secondary data. There is a growing interdependence between national innovation systems, requiring new international innovation structures. New opportunities emerge for the production and international dissemination of knowledge. Clinical trials promote and require interaction between companies, universities and government agencies, proving strategic for structuring national innovation systems in health in the global context.

A inovação em saúde é caracterizada por uma forte interação com o setor de ciência e tecnologia. O crescente interesse pela internacionalização das atividades de pesquisa e desenvolvimento conduz ao questionamento sobre as oportunidades em função das interações com outros países, como mecanismo para a construção de capacidades nacionais de inovação. Conforme os ensaios clínicos atravessaram as fronteiras nacionais, alcançando uma expressão global, as redes de relações entre os atores envolvidos tornaram-se cada vez mais complexas e apresentam-se como uma possibilidade para a caracterização da interação dos sistemas nacionais de inovação no plano global, particularmente pelo ponto de vista da produção de atividades de ciência, tecnologia e inovação em saúde. O objetivo deste trabalho foi estudar a expansão global de ensaios clínicos a fim de se discutir possíveis fatores relacionados com a interação dos sistemas nacionais de inovação dos países envolvidos. Os métodos empregados incluíram pesquisa bibliográfica e análise de dados secundários. Constata-se uma crescente interdependência dos sistemas nacionais de inovação, requerendo novas estruturas internacionais de inovação. Surgem novas oportunidades para a produção e a difusão internacional do conhecimento. Os ensaios clínicos promovem e requerem interação entre empresas, universidades e instâncias governamentais, revelando-se como elemento estratégico para a estruturação dos sistemas nacionais de inovação em saúde no contexto global.

La innovación en salud está caracterizada por una fuerte interacción con el sector de ciencia y tecnología. El creciente interés por la internacionalización de las actividades de investigación y desarrollo conduce al cuestionamiento acerca de oportunidades, en función de las interacciones con otros países, como mecanismo para la construcción de capacidades nacionales de innovación. A medida que los ensayos clínicos atravesaron las fronteras nacionales, tuvieron un alcance global, las redes de relaciones entre los actores implicados se hicieron cada vez más complejas y se presentan como una posibilidad para la caracterización de la interacción de los sistemas nacionales de innovación mundialmente, particularmente, desde el punto de vista de la producción de actividades de ciencia, tecnología e innovación en salud. El objetivo de este trabajo fue estudiar la expansión global de ensayos clínicos, a fin de discutir posibles factores relacionados con la interacción de los sistemas nacionales de innovación de los países implicados. Los métodos empleados incluyeron: investigación bibliográfica y el análisis de datos secundarios. Se constata una creciente interdependencia de los sistemas nacionales de innovación, requiriendo nuevas estructuras internacionales de innovación. Surgen nuevas oportunidades para la producción y la difusión internacional del conocimiento. Los ensayos clínicos promueven y requieren la interacción entre empresas, universidades e instancias gubernamentales, revelándose como un elemento estratégico para la estructuración de los sistemas nacionales de innovación en salud dentro del contexto global.

Two-stage enrichment clinical trial design with adjustment for misclassification in predictive biomarkers.

A two-stage enrichment design is a type of adaptive design, which extends a stratified design with a futility analysis on the marker negative cohort at the first stage, and the second stage can be either a targeted design with only the marker positive stratum, or still the stratified design with both marker strata, depending on the result of the interim futility analysis. In this paper, we consider the situation where the marker assay and the classification rule are possibly subject to error. We derive the sequential tests for the global hypothesis as well as the component tests for the overall cohort and the marker-positive cohort. We discuss the power analysis with the control of the type I error rate and show the adverse impact of the misclassification on the powers. We also show the enhanced power of the two-stage enrichment over the one-stage design and illustrate with examples of the recent successful development of immunotherapy in non-small-cell lung cancer.

Multi-stage enrichment and basket trial designs with population selection.

As biomarker information from early-phase trials can be unreliable due to high variability, it is logical to take a prospective two-stage approach when designing a late-phase confirmatory trial, ie, refining the target population at the first stage and performing the hypothesis testing at the second stage. The use of a reliable intermediate endpoint at the first stage can further improve the trial efficiency from both time and cost perspectives. Nevertheless, there are needs for expanding such two-stage confirmatory designs to more stages for monitoring efficacy on the refined population. There is limited literature on this matter, particularly for two popular designs with population selection midway, ie, the biomarker enrichment design and the basket design. In this manuscript, we focus on these two popular designs and discuss how to implement the interim efficacy analyses after population refinement while controlling type I error. Power and stopping probability are also explored for the two designs.

To add or not to add a new treatment arm to a multiarm study: A decision-theoretic framework.

Multiarm clinical trials, which compare several experimental treatments against control, are frequently recommended due to their efficiency gain. In practise, all potential treatments may not be ready to be tested in a phase II/III trial at the same time. It has become appealing to allow new treatment arms to be added into on-going clinical trials using a "platform" trial approach. To the best of our knowledge, many aspects of when to add arms to an existing trial have not been explored in the literature. Most works on adding arm(s) assume that a new arm is opened whenever a new treatment becomes available. This strategy may prolong the overall duration of a study or cause reduction in marginal power for each hypothesis if the adaptation is not well accommodated. Within a two-stage trial setting, we propose a decision-theoretic framework to investigate when to add or not to add a new treatment arm based on the observed stage one treatment responses. To account for different prospect of multiarm studies, we define utility in two different ways; one for a trial that aims to maximise the number of rejected hypotheses; the other for a trial that would declare a success when at least one hypothesis is rejected from the study. Our framework shows that it is not always optimal to add a new treatment arm to an existing trial. We illustrate a case study by considering a completed trial on knee osteoarthritis.

A Review of Clinical Trials With an Adaptive Design and Health Economic Analysis.

OBJECTIVE: An adaptive design uses data collected as a clinical trial progresses to inform modifications to the trial. Hence, adaptive designs and health economics aim to facilitate efficient and accurate decision making. Nevertheless, it is unclear whether the methods are considered together in the design, analysis, and reporting of trials. This review aims to establish how health economic outcomes are used in the design, analysis, and reporting of adaptive designs. METHODS: Registered and published trials up to August 2016 with an adaptive design and health economic analysis were identified. The use of health economics in the design, analysis, and reporting was assessed. Summary statistics are presented and recommendations formed based on the research team's experiences and a practical interpretation of the results. RESULTS: Thirty-seven trials with an adaptive design and health economic analysis were identified. It was not clear whether the health economic analysis accounted for the adaptive design in 17/37 trials where this was thought necessary, nor whether health economic outcomes were used at the interim analysis for 18/19 of trials with results. The reporting of health economic results was suboptimal for the (17/19) trials with published results. CONCLUSIONS: Appropriate consideration is rarely given to the health economic analysis of adaptive designs. Opportunities to use health economic outcomes in the design and analysis of adaptive trials are being missed. Further work is needed to establish whether adaptive designs and health economic analyses can be used together to increase the efficiency of health technology assessments without compromising accuracy.

Expansão global dos ensaios clínicos: inovação e interação / Global expansion of clinical trials: innovation and interaction / Expansión global de los ensayos clínicos: innovación e interacción

A inovação em saúde é caracterizada por uma forte interação com o setor de ciência e tecnologia. O crescente interesse pela internacionalização das atividades de pesquisa e desenvolvimento conduz ao questionamento sobre as oportunidades em função das interações com outros países, como mecanismo para a construção de capacidades nacionais de inovação. Conforme os ensaios clínicos atravessaram as fronteiras nacionais, alcançando uma expressão global, as redes de relações entre os atores envolvidos tornaram-se cada vez mais complexas e apresentam-se como uma possibilidade para a caracterização da interação dos sistemas nacionais de inovação no plano global, particularmente pelo ponto de vista da produção de atividades de ciência, tecnologia e inovação em saúde. O objetivo deste trabalho foi estudar a expansão global de ensaios clínicos a fim de se discutir possíveis fatores relacionados com a interação dos sistemas nacionais de inovação dos países envolvidos. Os métodos empregados incluíram pesquisa bibliográfica e análise de dados secundários. Constata-se uma crescente interdependência dos sistemas nacionais de inovação, requerendo novas estruturas internacionais de inovação. Surgem novas oportunidades para a produção e a difusão internacional do conhecimento. Os ensaios clínicos promovem e requerem interação entre empresas, universidades e instâncias governamentais, revelando-se como elemento estratégico para a estruturação dos sistemas nacionais de inovação em saúde no contexto global.

Innovation in health is characterized by strong interaction with the science and technology sector. Growing interest in the internationalization of research and development leads to questions on opportunities as a function of interactions with other countries, as a mechanism for national capacity-building in innovation. As clinical trials cross national borders and achieve global relevance, networks of relations between the actors become increasingly complex and present themselves as a possibility for characterizing interaction of national innovation systems at the global level, particularly from the point of view of producing activities in science, technology, and innovation in health. This study aimed to analyze the global expansion of clinical trials in order to discuss possible factors related to the interaction between national innovation systems in the countries involved. The methods included literature searches and analysis of secondary data. There is a growing interdependence between national innovation systems, requiring new international innovation structures. New opportunities emerge for the production and international dissemination of knowledge. Clinical trials promote and require interaction between companies, universities and government agencies, proving strategic for structuring national innovation systems in health in the global context.

La innovación en salud está caracterizada por una fuerte interacción con el sector de ciencia y tecnología. El creciente interés por la internacionalización de las actividades de investigación y desarrollo conduce al cuestionamiento acerca de oportunidades, en función de las interacciones con otros países, como mecanismo para la construcción de capacidades nacionales de innovación. A medida que los ensayos clínicos atravesaron las fronteras nacionales, tuvieron un alcance global, las redes de relaciones entre los actores implicados se hicieron cada vez más complejas y se presentan como una posibilidad para la caracterización de la interacción de los sistemas nacionales de innovación mundialmente, particularmente, desde el punto de vista de la producción de actividades de ciencia, tecnología e innovación en salud. El objetivo de este trabajo fue estudiar la expansión global de ensayos clínicos, a fin de discutir posibles factores relacionados con la interacción de los sistemas nacionales de innovación de los países implicados. Los métodos empleados incluyeron: investigación bibliográfica y el análisis de datos secundarios. Se constata una creciente interdependencia de los sistemas nacionales de innovación, requiriendo nuevas estructuras internacionales de innovación. Surgen nuevas oportunidades para la producción y la difusión internacional del conocimiento. Los ensayos clínicos promueven y requieren la interacción entre empresas, universidades e instancias gubernamentales, revelándose como un elemento estratégico para la estructuración de los sistemas nacionales de innovación en salud dentro del contexto global.