Multi-arm clinical trials with treatment selection:what can be gained and at what price?

With current success rates of confirmatory studies being only around 50%, new approaches to drug development are paramount. Many trials fail simply because ineffective treatments are identified too late. In this paper, we discuss the utility of multi-arm studies with treatment selection as a potential strategy that can reduce the high attrition rate. We illustrate the large gains in efficiency that are possible based on an example in Alzheimer's disease while outlining the additional challenges that need to be overcome to implement such studies

interAdapt -- An Interactive Tool for Designing and Evaluating Randomized Trials with Adaptive Enrollment Criteria

The interAdapt R package is designed to be used by statisticians and clinical investigators to plan randomized trials. It can be used to determine if certain adaptive designs offer tangible benefits compared to standard designs, in the context of investigators' specific trial goals and constraints. Specifically, interAdapt compares the performance of trial designs with adaptive enrollment criteria versus standard (non-adaptive) group sequential trial designs. Performance is compared in terms of power, expected trial duration, and expected sample size. Users can either work directly in the R console, or with a user-friendly shiny application that requires no programming experience. Several added features are available when using the shiny application. For example, the application allows users to immediately download the results of the performance comparison as a csv-table, or as a printable, html-based report.Comment: 14 pages, 2 figures (software screenshots); v2 includes command line function descriptio

Optimal sample size determination in adaptive seamless phase II/III design

The adaptive seamless phase II/III design combines the conventional separate phases II and III trials into a single trial, and it allows for adaptations (e.g. sample size reassessment and early stopping for futility or success) after the interim analysis. In this study, we propose a simulation-based method to determine the optimal sample size for the adaptive seamless phase II/III design. We assume that a power law relationship exists between the overall sample size and statistical power of the final test. The optimal sample size is defined as the minimum sample size that provides adequate power with overall type I error rate under control. To find the optimal size, we also take correlations between the early and the final outcomes into consideration. The methodology is applied to determining sample sizes in a study for a candidate treatment that can avoid renal damage during cardiac operations while the most effective dose of the treatment will be selected at the interim analysis. PUBLIC HEALTH SIGNIFICANCE Adaptive seamless phase II/III design eliminates the time between the traditional separate trials and better utilizes the data collected before the interim analysis, thus will result in faster clinical trials. Treatment effect can be confirmed at the final test if adequate power is achieved and the overall type I error rate is under control. Using these faster clinical trials, effective treatment can be approved sooner to benefit more patients. In addition, in an adaptive seamless phase II/III design more patients will be allocated to the more effective treatment than they would in conventional clinical trials

NUMERICAL STUDY FOR SEAMLESS CLINICAL TRIALS WITH COVARIATE ADAPTIVE RANDOMIZATION

One important goal of the pharmaceutical industry is to evaluate new therapies in a time-sensitive and cost-effective manner without undermining the integrity and validity of clinical trials. Adaptive seamless phase II/III designs (ASD) have gained popularity for accelerating the drug development process and reducing cost. Covariate adaptive randomization (CAR) is the most popular design in randomized controlled trials to ensure valid treatment comparisons by balancing the prognostic characteristics of patients among treatment groups. Although adaptive seamless clinical trials with CAR have been implemented in practice1, the theoretical understanding of such designs is limited. In addition, current approaches to control the Type 1 error rate in seamless trials are based on theories for complete randomization, which may be invalid under CAR and lead to a Type 1 error rate that deviates from the nominal level. Recently, Ma and Zhu (2019, unpublished) established the theoretical foundation for the adaptive seamless phase II/III trial with CAR and proposed a hypothesis testing approach to control the Type 1 error rate in such trials. In the current research, numerical studies were conducted to investigate the feasibility and advantages of the proposed approach in the seamless design with stratified permutated block (SPB) randomization. The simulation results revealed that the newly developed method well controlled the Type 1 error rate around the nominal level, improved the statistical power compared to the standard two sample t-test and increased the number of replications that the best treatment is selected for Stage II of the seamless trial under the SPB design compared to the complete randomization, which could promote its application in practice

Comparing the MAMS framework with the combination method in multi-arm adaptive trials with binary outcomes

In multi-arm adaptive trials, several treatments are assessed simultaneously and accumulating data are used to inform decisions about the trial, such as whether treatments are dropped or continued. Different methodological approaches have been developed for such trials and research has compared the performance of different subsets of these. One particular approach, for which we use the acronym MAMS(R), has generally not been included in these comparisons because control of the family-wise error rate (FWER) could not be guaranteed. Recently, the MAMS(R) approach has been extended to facilitate the generation of efficient designs which strongly control the FWER. We consider multi-arm two-stage trials with binary outcomes and propose parameterising treatment effects using the log odds ratio. We conduct a simulation study comparing the extended MAMS(R) framework with the well-established combination method both for trials where a different outcome is used for mid-trial analysis and for trials where the same outcome is used throughout. We show how the MAMS(R) framework compares favourably only in scenarios where the same outcome is used. We propose a hybrid selection rule within MAMS(R) methodology and demonstrate that this makes it possible to use the MAMS(R) framework in trials incorporating comparative treatment selection

An R package for implementing simulations for seamless phase II/III clinical trials using early outcomes for treatment selection

Adaptive seamless phase II/III clinical trial designs allowing treatment selection at an interim analysis have gained much attention because of their potential benefits compared to more conventional drug development programmes with separate trials for individual phases. A scenario of particular interest is that in which the final outcome in the trial is based on long-term follow-up, but the interim analysis can only realistically be based on early (short-term) outcomes. A new software package (asd) for the statistical software R implements simulations for designs of this type, in addition to the simpler scenario where treatment selection is based on the definitive (final) outcome. The methodology is briefly described and two examples of proposed trial designs in progressive multiple sclerosis are provided, with R code to illustrate application of the methodology

Methodological aspects of multi-arm adaptive clinical trials

In the present healthcare climate, there is an urgent need to increase the efficiency with which novel therapies are evaluated. Multi-arm adaptive trials allow multiple treatments to be tested within a single protocol and offer the facility to respond to emerging data. Such trials allow treatment arms to be dropped or even added partway through the trial, directing resources to promising treatments. In this thesis, methodologies for two-stage adaptive trials with binary outcomes are explored, focussing on those approaches in which an intermediate outcome may be used for the purposes of treatment selection. Methodology for the multi-arm multi-stage approach developed by Royston et al. (2003, 2011), here denoted MAMS(R), is extended so that feasible and admissible trial designs may be obtained under the log odds ratio parameterisation. A simulation study suggests that these MAMS(R) designs perform favourably compared with the well-established combination method when a common outcome is monitored, but not when an intermediate outcome is incorporated. A proposal is made for increasing the efficiency and flexibility of MAMS(R) methodology by implementing conditional error calculations within a closed testing procedure. This approach allows the trial design to be updated at the interim analysis, resulting in gains in efficiency, particularly in trials where an intermediate outcome is used and where some promising treatments are dropped. The conditional error approach is then extended to offer the facility of adding a new treatment arm to an ongoing multi-arm adaptive trial. The procedure achieves good power, ensures Type I error rate control and performs particularly well if a new treatment arm is added when promising treatments have been dropped from the trial. Recommendations for using the new developments are given. It is hoped that this research will widen the use of MAMS(R) methodology in practice