Group Sequential Approach Research Articles

A Bayesian quasi-likelihood design for identifying the minimum effective dose and maximum utility dose in dose-ranging studies.

Most existing dose-ranging study designs focus on assessing the dose-efficacy relationship and identifying the minimum effective dose. There is an increasing interest in optimizing the dose based on the benefit-risk tradeoff. We propose a Bayesian quasi-likelihood dose-ranging design that jointly considers safety and efficacy to simultaneously identify the minimum effective dose and the maximum utility dose to optimize the benefit-risk tradeoff. The binary toxicity endpoint is modeled using a beta-binomial model. The efficacy endpoint is modeled using the quasi-likelihood approach to accommodate various types of data (e.g. binary, ordinal or continuous) without imposing any parametric assumptions on the dose-response curve. Our design utilizes a utility function as a measure of benefit-risk tradeoff and adaptively assign patients to doses based on the doses' likelihood of being the minimum effective dose and maximum utility dose. The design takes a group-sequential approach. At each interim, the doses that are deemed overly toxic or futile are dropped. At the end of the trial, we use posterior probability criteria to assess the strength of the dose-response relationship for establishing the proof-of-concept. If the proof-of-concept is established, we identify the minimum effective dose and maximum utility dose. Our simulation study shows that compared with some existing designs, the Bayesian quasi-likelihood dose-ranging design is robust and yields competitive performance in establishing proof-of-concept and selecting the minimum effective dose. Moreover, it includes an additional feature for further maximum utility dose selection.

Adaptive hybrid control design for comparative clinical trials with historical control data

Abstract We propose an adaptive hybrid control causal (AHCC) design to leverage historical control data to reduce the sample size demanded by standard randomised controlled trials (RCT). Under the causal inference framework, we define the causal estimand of the average treatment effect and derive the corresponding estimator based on the trial data and historical control data. The AHCC design takes a multistage or group sequential approach. The number of patients randomised to the concurrent control is adaptively adjusted based on the amount of information borrowed from the historical control data. At each stage, based on the interim data, the contribution of the historical control data, quantified by the effective sample size, is updated and used to determine the randomisation ratio between the treatment and control arms for the next stage, with the goal to resemble a standard RCT upon the completion of the trial. Simulation studies show that the AHCC design has desirable operating characteristics. For example, it saves on sample size when substantial information can be borrowed from the historical control, and it maintains power when little information can be borrowed from the historical control.

A Bayesian-adaptive decision-theoretic approach can reduce the sample sizes for multiarm exercise oncology trials

ObjectivesAdaptive designs may reduce trial sample sizes and costs. This study illustrates a Bayesian-adaptive decision-theoretic design applied to a multiarm exercise oncology trial. Study Design and SettingIn the Physical exercise during Adjuvant Chemotherapy Effectiveness Study (PACES) trial, 230 breast cancer patients receiving chemotherapy were randomized to supervised resistance and aerobic exercise (OnTrack), home-based physical activity (OncoMove) or usual care (UC). Data were reanalyzed as an adaptive trial using both Bayesian decision-theoretic and a frequentist group-sequential approach incorporating interim analyses after every 36 patients. Endpoint was chemotherapy treatment modifications (any vs. none). Bayesian analyses were performed for different continuation thresholds and settings with and without arm dropping and both in a ‘pick-the-winner’ and a ‘pick-all-treatments-superior-to-control’ setting. ResultsTreatment modifications occurred in 34% of patients in UC and OncoMove vs. 12% in OnTrack (P = 0.002). Using a Bayesian-adaptive decision-theoretic design, OnTrack was identified as most effective after 72 patients in the ‘pick-the-winner’ setting and after 72–180 patients in the ‘pick-all-treatments-superior-to-control’ setting. In a frequentist setting, the trial would have been stopped after 180 patients, and the proportion of patients with treatment modifications was significantly lower for OnTrack than UC. ConclusionA Bayesian-adaptive decision-theoretic approach substantially reduced the sample size required for this three-arm exercise trial, especially in the ‘pick-the-winner’ setting.

A Bayesian group sequential design for randomized biosimilar clinical trials with adaptive information borrowing from historical data

ABSTRACT At the time of developing a biosimilar, the reference product has been on market for years and thus ample data are available on its efficacy and characteristics. We develop a Bayesian adaptive design for randomized biosimilar clinical trials to leverage the rich historical data on the reference product. This design takes a group sequential approach. At each interim, we employ the elastic meta-analytic-predictive (EMAP) prior methodology to adaptively borrow information from the historical data of the reference product to make go/no-go decision based on Bayesian posterior probabilities. In addition, the randomization ratio between the test and reference arms is adaptively adjusted at the interim with the goal to balance the sample size of the two arms at the end of trials. Simulation study shows that the proposed Bayesian adaptive design can substantially reduce the sample size of the reference arm, while achieving comparable power as the traditional randomized clinical trials that ignore the historical data. We apply our design to a biosimilar trial for treating breast cancer patients.

A randomized group sequential enrichment design for immunotherapy and targeted therapy

In targeted therapy or immunotherapy, it is common that only a subpopulation of patients are sensitive to and thus may benefit from the therapy. In practice, based on pre-clinical data, it is often assumed that the sensitive subpopulation is known. Subsequent clinical trial data, however, often show that this assumptions is false. We propose a randomized, group sequential enrichment (GSE) design to evaluate an experimental treatment against a control. The GSE design starts by enrolling patients under broad eligibility criteria and then alters the entry criteria to restrict enrollment to treatment-sensitive patients based on accumulating data of short-term and long-term survival endpoints. The short-term endpoint is used to facilitate enrichment when a limited number of survival events are observed, while the final determination of treatment efficacy and sensitive subpopulation is based on the survival. The group sequential approach is adopted to implement the adaptive enrichment strategy. The proposed design simultaneously achieves two primary goals of precision medicine: to determine whether the experimental drug is superior to the control and to identify the target population that is sensitive to the treatment. A simulation study shows that the proposed design well controls the type I error rate and yields substantially higher power than the conventional group sequential design and existing two-stage enrichment design.

Statistical methods for seamless phase II/III design

The seamless phase II/III design combining phases II and III into a single trial has been shown growing interest for improving the efficiency of drug development, becoming the most frequent adaptive design type. It typically consists of two stages, the trial objectives being often different in each stage. The primary objectives are to select optimal experimental treatment group(s) in the first stage and compare the efficacy between the selected treatment and control groups in the second stage. In the final analysis, appropriate statistical methods should be applied to avoid increasing the type I error rate and selection bias. This paper reviews several statistical methods that can be applied to adaptive seamless phase II/III designs. Especially, the most representative methods such as the group sequential design approach, combination test approach, and conditional error function approach, are organized in a unified framework and the characteristics of the methods are compared. As related topics, statistical methods for the case where endpoints are different between stages 1 and 2, and the method of sample size calculation will also be discussed. Examples of actual applications are also presented.

Accounting for variation in the required sample size in the design of group-sequential trials

IntroductionMost literature on optimal group-sequential designs focuses on minimising the expected sample size. We highlight other factors for consideration. MethodsWe discuss several quantities less-often considered in adaptive design: the median and standard deviation of the random required sample size, and the probability of committing an interim error. We consider how the optimal timing of interim analyses changes when these quantities are accounted for. ResultsIncorporating the standard deviation of the required sample size into an optimality framework, we demonstrate how and when this quantity means using a group-sequential approach is not optimal. The optimal timing of an interim analysis is shown to be highly dependent on the pre-specified preference for minimising the expected sample size relative to its standard deviation. ConclusionsExamining multiple factors, which measure the advantages and disadvantages of group-sequential designs, helps determine the best design for a specific trial.

A multi-stage drop-the-losers design for multi-arm clinical trials.

Multi-arm multi-stage trials can improve the efficiency of the drug development process when multiple new treatments are available for testing. A group-sequential approach can be used in order to design multi-arm multi-stage trials, using an extension to Dunnett’s multiple-testing procedure. The actual sample size used in such a trial is a random variable that has high variability. This can cause problems when applying for funding as the cost will also be generally highly variable. This motivates a type of design that provides the efficiency advantages of a group-sequential multi-arm multi-stage design, but has a fixed sample size. One such design is the two-stage drop-the-losers design, in which a number of experimental treatments, and a control treatment, are assessed at a prescheduled interim analysis. The best-performing experimental treatment and the control treatment then continue to a second stage. In this paper, we discuss extending this design to have more than two stages, which is shown to considerably reduce the sample size required. We also compare the resulting sample size requirements to the sample size distribution of analogous group-sequential multi-arm multi-stage designs. The sample size required for a multi-stage drop-the-losers design is usually higher than, but close to, the median sample size of a group-sequential multi-arm multi-stage trial. In many practical scenarios, the disadvantage of a slight loss in average efficiency would be overcome by the huge advantage of a fixed sample size. We assess the impact of delay between recruitment and assessment as well as unknown variance on the drop-the-losers designs.

Multiple testing procedures for adaptive enrichment designs: combining group sequential and reallocation approaches.

Adaptive enrichment designs involve preplanned rules for modifying enrollment criteria based on accrued data in an ongoing trial. For example, enrollment of a subpopulation where there is sufficient evidence of treatment efficacy, futility, or harm could be stopped, while enrollment for the remaining subpopulations is continued. We propose a new class of multiple testing procedures tailored to adaptive enrichment designs. The procedures synthesize ideas from two general approaches. As in the modified group sequential approach, the procedures gain power by leveraging the covariance among statistics for different stages and different hypotheses. As in the alpha reallocation approach, the procedures lower rejection thresholds for the remaining null hypotheses after others have been rejected. The proposed procedures are proved to have power greater than or equal to several existing methods, and to strongly control the familywise Type I error rate when statistics are normally distributed. The methods are illustrated through simulations of a trial for a surgical intervention for stroke, involving two subpopulations.

Flexible selection of a single treatment incorporating short-term endpoint information in a phase II/III clinical trial.

Seamless phase II/III clinical trials in which an experimental treatment is selected at an interim analysis have been the focus of much recent research interest. Many of the methods proposed are based on the group sequential approach. This paper considers designs of this type in which the treatment selection can be based on short‐term endpoint information for more patients than have primary endpoint data available. We show that in such a case, the familywise type I error rate may be inflated if previously proposed group sequential methods are used and the treatment selection rule is not specified in advance. A method is proposed to avoid this inflation by considering the treatment selection that maximises the conditional error given the data available at the interim analysis. A simulation study is reported that illustrates the type I error rate inflation and compares the power of the new approach with two other methods: a combination testing approach and a group sequential method that does not use the short‐term endpoint data, both of which also strongly control the type I error rate. The new method is also illustrated through application to a study in Alzheimer's disease. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

A Bayesian Group Sequential Approach to Safety Signal Detection

Clinical safety data, usually reported as clinically manifested adverse events (AEs) according to the Medical Dictionary for Regulatory Activities (MedDRA), are routinely collected during the course of a clinical trial involving comparative groups, and periodical monitoring of the safety events is often required to determine whether excessive occurrence of a set of AEs is associated with treatment. To accommodate the structure of reported AEs with the MedDRA system, a Bayesian hierarchical model has been proposed for the analysis of clinical safety data. However, the characteristics of sequential use of the Bayesian method has not been studied. In this paper the Bayesian hierarchical model is applied in a group sequential manner for multiple interim analyses of safety events. A decision-theoretic approach is employed to determine threshold values in the safety signaling process. The proposed approach is illustrated through simulations and a real example.

Influence of Adaptive Analysis on Unnecessary Patient Recruitment: Reanalysis of the RATPAC Trial

Recruitment to clinical trials is a challenging but essential activity in emergency medicine. Conventional fixed-sample trials may continue to recruit patients after efficacy has been demonstrated or when further recruitment is futile. Adaptive trials make use of emerging information to modify aspects of a trial or terminate it prematurely, potentially resulting in savings in terms of sample size, time, and cost. We aim to use sequential testing procedures to reanalyze data from a fixed-sample trial, the Randomised Assessment of Treatment Using Panel Assay of Cardiac Markers (RATPAC) trial, and investigate the potential for adaptive designs to reduce unnecessary recruitment. The trial was reanalyzed with a triangular group sequential design, with interim analyses planned every 3 months. Patients were analyzed in the order in which they entered the original trial. We found that the RATPAC trial could potentially have stopped 1 year earlier, with 722 patients enrolled compared with 2,243 patients in the original trial, making a potential saving of approximately $390,000. Estimates of effect were similar, and the qualitative conclusions of the original and group sequential RATPAC trials were in agreement. However, the group sequential approach is not without limitations and would have resulted in less precise estimates of effect and less information available for the subsequent evaluation of secondary endpoints. Sequential designs are well suited in emergency medicine because of the rapidly obtained outcomes and the need to avoid unnecessary recruitment. We recommend that group sequential designs be considered for clinical trials in emergency medicine.

Group sequential trials revisited: Simple implementation using SAS

The methodology of group sequential trials is now well established and widely implemented. The benefits of the group sequential approach are generally acknowledged, and its use, when applied properly, is accepted by researchers and regulators. This article describes how a wide range of group sequential designs can easily be implemented using two accessible SAS functions. One of these, PROBBNRM is a standard function, while the other, SEQ, is part of the interactive matrix language of SAS, PROC IML. The account focuses on the essentials of the approach and reveals how straightforward it can be. The design of studies is described, including their evaluation in terms of the distribution of final sample size. The conduct of the interim analyses is discussed, with emphasis on the consequences of inevitable departures from the planned schedule of information accrual. The computations required for the final analysis, allowing for the sequential design, are closely related to those conducted at the design stage. Illustrative examples are given and listings of suitable of SAS code are provided.

Seamless phase II/III designs

In recent years, there has been a drive to save development costs and shorten time-to-market of new therapies. Research into novel trial designs to facilitate this goal has led to, amongst other approaches, the development of methodology for seamless phase II/III designs. Such designs allow treatment or dose selection at an interim analysis and comparative evaluation of efficacy with control, in the same study. Methods have gained much attention because of their potential advantages compared to conventional drug development programmes with separate trials for individual phases. In this article, we review the various approaches to seamless phase II/III designs based upon the group-sequential approach, the combination test approach and the adaptive Dunnett method. The objective of this article is to describe the approaches in a unified framework and highlight their similarities and differences to allow choice of an appropriate methodology by a trialist considering conducting such a trial.

Stopping Boundaries of Flexible Sample Size Design With Flexible Trial Monitoring

In the group sequential (GS) approach with a fixed sample size design, the Type I error is controlled by the additivity of exit spending values. However, in a flexible sample size design where the sample size will be recalculated using the interim data, the overall Type I error rate can be inflated. Therefore, the predefined GS stopping boundaries have to be adjusted to maintain the Type I error level at each interim analysis and the at the overall level. The modified α spending function adjusted for sample size reestimation (SSR) is proposed to maintain the Type I error level. We use a unified approach and mathematically quantify the Type I error with and without sample size adjustment constraints. As a result, stopping boundaries can be obtained by inversely solving the exact Type I error functions. This unified approach, using Brownian motion theory, can be applied to normal, survival, and binary endpoints. Extensive simulations show the adjusted stopping boundaries can control the Type I error at each analysis and at the overall level.

Two‐Stage Group Sequential Robust Tests in Family‐Based Association Studies: Controlling Type I Error

In family-based association studies, an optimal test statistic with asymptotic normal distribution is available when the underlying genetic model is known (e.g., recessive, additive, multiplicative, or dominant). In practice, however, genetic models for many complex diseases are usually unknown. Using a single test statistic optimal for one genetic model may lose substantial power when the model is mis-specified. When a family of genetic models is scientifically plausible, the maximum of several tests, each optimal for a specific genetic model, is robust against the model mis-specification. This robust test is preferred over a single optimal test. Recently, cost-effective group sequential approaches have been introduced to genetic studies. The group sequential approach allows interim analyses and has been applied to many test statistics, but not to the maximum statistic. When the group sequential method is applied, type I error should be controlled. We propose and compare several approaches of controlling type I error rates when group sequential analysis is conducted with the maximum test for family-based candidate-gene association studies. For a two-stage group sequential robust procedure with a single interim analysis, two critical values for the maximum tests are provided based on a given alpha spending function to control the desired overall type I error.

A Practical Comparison of Group-Sequential and Adaptive Designs

Sequential methods provide a formal framework by which clinical trial data can be monitored as they accumulate. The results from interim analyses can be used either to modify the design of the remainder of the trial or to stop the trial as soon as sufficient evidence of either the presence or absence of a treatment effect is available. The circumstances under which the trial will be stopped with a claim of superiority for the experimental treatment, must, however, be determined in advance so as to control the overall type I error rate. One approach to calculating the stopping rule is the group-sequential method. A relatively recent alternative to group-sequential approaches is the adaptive design method. This latter approach provides considerable flexibility in changes to the design of a clinical trial at an interim point. However, a criticism is that the method by which evidence from different parts of the trial is combined means that a final comparison of treatments is not based on a sufficient statistic for the treatment difference, suggesting that the method may lack power. The aim of this paper is to compare two adaptive design approaches with the group-sequential approach. We first compare the form of the stopping boundaries obtained using the different methods. We then focus on a comparison of the power of the different trials when they are designed so as to be as similar as possible. We conclude that all methods acceptably control type I error rate and power when the sample size is modified based on a variance estimate, provided no interim analysis is so small that the asymptotic properties of the test statistic no longer hold. In the latter case, the group-sequential approach is to be preferred. Provided that asymptotic assumptions hold, the adaptive design approaches control the type I error rate even if the sample size is adjusted on the basis of an estimate of the treatment effect, showing that the adaptive designs allow more modifications than the group-sequential method.

A Bayesian Group Sequential Approach for Multiple Endpoints

ABSTRACT Making decisions regarding overall treatment effectiveness can be problematic when one considers multiple outcomes, especially if the treatment effects are discordant across the profile of outcomes. A typical example involves a case where a treatment has both increased efficacy in one endpoint (e.g., improved disease-free survival) and increased side effects (e.g., more acute toxicities). Often a study is designed to test one primary hypothesis while other outcomes vital to the decision-making process are not formally incorporated into the study design. We describe a Bayesian approach that provides a mechanism for combining information from two normally distributed endpoints and accounts for the magnitude of those effects. This procedure is implemented for the case of comparing two different treatments to each other and allows for multiple looks at the data. Information from more than one endpoint is combined through the use of utility functions. Our group sequential procedure is demonstrated for the design of a cancer clinical trial that involves two looks at the data. The example shows the effect of different utility functions applied to the same data. Because the selection of the utility function is crucial to the interpretation of the two endpoints, the results are not invariant to the utility function, and great care must be exercised in choosing an appropriate function. Additionally, since results are not robust to the choice of prior, we select a noninformative prior for our example. Despite some limitations in the specification of the utility structure and prior distribution, our procedure provides a mechanism that is useful for simultaneously monitoring multiple endpoints in a clinical trial.

A group sequential, response-adaptive design for randomized clinical trials

There has been considerable methodological research on response-adaptive designs for clinical trials but they have seldom been used in practice. The many reasons for this are summarized in an article by Rosenberger and Lachin, but the two main reasons generally cited are logistical difficulties and the potential for bias due to selection effects, “drift” in patient characteristics or risk factors over time, and other sources. Jennison and Turnbull consider a group sequential, response-adaptive design for continuous outcome variables that partially addresses these concerns while at the same time allowing for early stopping. The key advantage of a group sequential approach in which randomization probabilities are kept constant within sequential groups is that a stratified analysis will eliminate bias due to drift. In this article we consider binary outcomes and an algorithm for altering the allocation ratio that depends on the strength of the accumulated evidence. Specifically, patients are enrolled in groups of size nAk, nBk, k=1, 2, … K, where nAk, nBk are the sample sizes in treatment arms A and B in sequential group k. Patients are initially allocated in a 1:1 ratio. After the kth interim analysis, if the z-value comparing outcomes in the two treatment groups is less than 1.0 in absolute value, the ratio remains 1:1; if the z-value exceeds 1.0, the next sequential group is allocated in the ratio R1 favoring the currently better-performing treatment; if the z-statistic exceeds 1.5, the allocation ratio is R2, and if the z-value exceeds 2.0, the allocation ratio is R3. If the O'Brien-Fleming monitoring boundary is exceeded the trial is terminated. Group sample-sizes are adjusted upward to maintain equal increments of information when allocation ratios exceed one. The z-statistic is derived from a weighted log-odds ratio stratified by sequential group. Simulation studies and theoretical calculations were performed under a variety of scenarios and allocation rules. Results indicate that the method maintains the nominal type I error rate even when there is substantial drift in the patient population. When a true treatment difference exists, a modest reduction in the number of patients assigned to the inferior treatment arm can be achieved at the expense of smaller increases in the total sample size relative to a nonadaptive design. Limitations, such as the impact of delays in observing outcomes, are discussed, as well as areas for further research. We conclude that responsive adaptive designs may be useful for some purposes, particularly in the presence of large treatment effects, although allowing early stopping minimizes the benefits. If such a design is undertaken, the randomization and analysis should be stratified in order to avoid bias due to time trends.

A Group Sequential Approach to Evaluation of Bridging Studies#

The International Conference on Harmonization (ICH) E5 guideline defines a bridging study as a supplementary study conducted in the new region to provide pharmacodynamic or clinical data on efficacy, safety, dosage, and dose regimen to allow extrapolation of the foreign clinical data to the population of the new region. Therefore, a bridging study is usually conducted in the new region only after the test product has been approved for commercial marketing in the original region due to its proven efficacy and safety. The issue of analysis of clinical data generated by the bridging study conducted in the new region to evaluate the similarity for extrapolation of the foreign clinical data to the population of the new region is the information on efficacy, safety, dosage, and dose regimen of the original region that cannot be concurrently obtained from the local bridging studies but is available in the trials conducted in the original region. A group sequential approach is therefore proposed to overcome the issue of internal validity. In particular, we use the region as a group sequence to enroll the patients from the original region first and then to enroll patients from the new region subsequently. Methods for sample size determination for the bridging study in the new region are also proposed.