Designing multi-arm multi-stage clinical trials using a risk-benefit criterion for treatment selection

Multi-arm clinical trials that compare several active treatments to a common control have been proposed as an efficient means of making an informed decision about which of several treatments should be evaluated further in a confirmatory study. Additional efficiency is gained by incorporating interim analyses and in particular, seamless Phase II/III designs have been the focus of recent research. Common to much of this work is the constraint that selection and formal testing should be based on a single efficacy endpoint, despite the fact that in practice, safety considerations will often play a central role in determining selection decisions. Here we develop a multi-arm multistage design for a trial with an efficacy and safety endpoint. The safety endpoint is explicitly considered in the formulation of the problem, selection of experimental arm and hypothesis testing. The design extends group-sequential ideas and considers the scenario where a minimal safety requirement is to be fulfilled and the treatment yielding the best combined safety and efficacy trade-off satisfying this constraint is selected for further testing. The treatment with the best trade-off is selected at the first interim analysis while the whole trial is allowed to comprise of J analyses. We show that the design controls the familywise error rate in the strong sense and illustrate the method through an example and simulation. We find that the design is robust to misspecification of the correlation between the endpoints and requires similar numbers of subjects to a trial based on efficacy alone for moderately correlated endpoints

Group Sequential and Adaptive Designs for Three-Arm 'Gold Standard' Non-Inferiority Trials

This thesis deals with the application of group sequential and adaptive methodology in three-arm non-inferiority trials for the case of normally distributed outcomes. Whenever feasible, use of the three-arm design including a test treatment, an active control and a placebo, is recommended by the health authorities. Nevertheless, especially from an ethical point of view, it is desirable to keep the placebo group size as small as possible. After giving a short introduction to two-arm non-inferiority trials, we investigate a hierarchical single-stage testing procedure for three-arm trials which starts by assessing the superiority comparison between test and placebo and then proceeds to the test versus control non-inferiority comparison. Based on formulas for the overall power we derive optimal sample size allocations that minimise the overall sample size. Interestingly, the placebo group size turns out to be very low under the optimal allocation. The optimal fixed sample size designs will then serve both as a starting point and a benchmark for the designs determined later. Subsequently, a general group sequential design for three-arm non-inferiority trials is presented that aims at further minimising the required sample sizes. By choosing different rejection boundaries for the two comparisons we obtain designs with quite different properties. The influence of the boundaries on the operating characteristics such as the expected sample sizes is investigated by means of a comprehensive comparison to the optimal fixed design. Moreover, approximately optimal boundaries are derived for different optimisation criteria such as minimising the placebo group size. It turns out that the implementation of group sequential methodology can further improve the optimal fixed designs, where the potential early termination of the placebo arm is a key advantage that can make the trial more acceptable for patients. After this, the group sequential testing procedure is extended to adaptive designs that allow data-dependent design changes at the interim analysis. In this context, we discuss optimal mid-trial decision-making based on the observed interim data, with a special focus on sample size re-calculation. In doing so, we will make use of the conditional power and the Bayesian predictive power. Our investigations show the advantages of the proposed adaptive designs over the optimal fixed designs. In particular, the possibility to adapt the sample sizes at interim can help to deal with uncertainties regarding the treatment effects, that often exist in the planning stage of three-arm non-inferiority trials. We conclude with a discussion of the results and an outlook on possible future work

Computational Bayesian Methods Applied to Complex Problems in Bio and Astro Statistics

In this dissertation we apply computational Bayesian methods to three distinct problems. In the first chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model covariance matrices with a Bayesian Normal-Inverse-Wishart model, which we fit with Gibbs sampling. In the second chapter, we are interested in determining the sample sizes necessary to achieve a particular interval width and establish non-inferiority in the analysis of prevalences using two fallible tests. To this end, we use a third order asymptotic approximation. In the third chapter, we wish to synthesize evidence across multiple domains in measurements taken longitudinally across time, featuring a substantial amount of structurally missing data, and fit the model with Hamiltonian Monte Carlo in a simulation to analyze how estimates of a parameter of interest change across sample sizes

Recommendations for designing and analysing multi-arm non-inferiority trials: a review of methodology and current practice

Background and purpose Multi-arm non-inferiority (MANI) trials, here defined as non-inferiority trials with multiple experimental treatment arms, can be useful in situations where several viable treatments exist for a disease area or for testing different dose schedules. To maintain the statistical integrity of such trials, issues regarding both design and analysis must be considered, from both the multi-arm and the non-inferiority perspectives. Little guidance currently exists on exactly how these aspects should be addressed and it is the aim of this paper to provide recommendations to aid the design of future MANI trials. Methods A comprehensive literature review covering four databases was conducted to identify publications associated with MANI trials. Literature was split into methodological and trial publications in order to investigate the required design and analysis considerations for MANI trials and whether they were being addressed in practice. Results A number of issues were identified that if not properly addressed, could lead to issues with the FWER, power or bias. These ranged from the structuring of trial hypotheses at the design stage to the consideration of potential heterogeneous treatment variances at the analysis stage. One key issue of interest was adjustment for multiple testing at the analysis stage. There was little consensus concerning whether more powerful p value adjustment methods were preferred to approximate adjusted CIs when presenting and interpreting the results of MANI trials. We found 65 examples of previous MANI trials, of which 31 adjusted for multiple testing out of the 39 that were adjudged to require it. Trials generally preferred to utilise simple, well-known methods for study design and analysis and while some awareness was shown concerning FWER inflation and choice of power, many trials seemed not to consider the issues and did not provide sufficient definition of their chosen design and analysis approaches. Conclusions While MANI trials to date have shown some awareness of the issues raised within this paper, very few have satisfied the criteria of the outlined recommendations. Going forward, trials should consider the recommendations in this paper and ensure they clearly define and reason their choices of trial design and analysis techniques

Sample Size Estimation using a Latent Variable Model for Mixed Outcome Co-Primary, Multiple Primary and Composite Endpoints

Mixed outcome endpoints that combine multiple continuous and discrete components to form co-primary, multiple primary or composite endpoints are often employed as primary outcome measures in clinical trials. There are many advantages to joint modelling the individual outcomes using a latent variable framework, however in order to make use of the model in practice we require techniques for sample size estimation. In this paper we show how the latent variable model can be applied to the three types of joint endpoints and propose appropriate hypotheses, power and sample size estimation methods for each. We illustrate the techniques using a numerical example based on the four dimensional endpoint in the MUSE trial and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required for the multiple primary endpoint is reduced from that required for the individual outcome with the largest effect size. We show that the analytical technique agrees with the empirical power from simulation studies. We further illustrate the reduction in required sample size that may be achieved in trials of mixed outcome composite endpoints through a simulation study and find that the sample size primarily depends on the components driving response and the correlation structure and much less so on the treatment effect structure in the individual endpoints

Quality control measures in clinical trials. risk-based monitoring and central statistical monitoring

Regulatory authorities have encouraged the usage of a risk-based monitoring (RBM) system in clinical trials. In addition to the identification of possible risks, risk-based monitoring also includes their evaluation to enable targeted monitoring. Risks are defined as conditions that could affect patient safety and the integrity of the study. Various studies demonstrated the increasing usage of RBM in practice. The application of the many RBM tools available has not been investigated. Central statistical monitoring (CSM) which falls under the remote monitoring of the RBM system has also been gaining more attention due to the recognition of its efficiency in monitoring clinical trials. This dissertation is dedicated to improving the quality assessments in risk-based monitoring and central statistical monitoring. The first chapter of the thesis provides an overview of clinical research and the types of clinical studies. Furthermore, it specifically focuses on clinical research structure, management, and activities in clinical trials. The different types of clinical trials are illustrated, followed by the management process of the trial and monitoring activities. Section 2.1 highlights the limitations of the current RBM tools. It shows how different an outcome risk assessment of a clinical trial can be when assessed with different RBM tools. Furthermore, this section shows the different risks covered within RBM tools. It shows the need for a risk assessment tool that can cover any risk in a clinical trial. Hence section 2.3 proposes a new risk methodology assessment (RMA) that can be applied to any clinical trial with the ability to add additional risks to the assessment. It presents a scoring method that allows stakeholders to visualize and quantify a risk size. This would guide stakeholders and assist them in the decision plan for mitigating a certain risk by an effective measure and monitoring degree in the monitoring plan. The theoretical RMA approach is presented in a shiny web app with a user-friendly interface to ease its implementation in practice. Section 2.4 proposes a new approach for the benefit of CSM. It presents multiple comparisons of individual center means to the Grand Mean of all centers. The approach is available and has been applied in different contexts. Here its implementation to detect a deviating center is recommended. As it is available for different data types, it shows specifically the comparison for continuous, binomial, and ordinal data types. In a Monte-Carlo simulation study, different model types estimating GM comparisons were tested for the control of Type I error and the highest power for balanced scenarios and unbalanced scenarios observed in clinical trials and observational studies. It also shows the validation of the approach on Real-world data (RWD) from the German Multiple Sclerosis Registry (GMSR). Finally, the approach is presented in shiny web apps to facilitate a common graphical conclusion style for different endpoints

Clinical trial designs using CompARE. An on-line exploratory tool for investigators

Report de Recerca aprovat per la Comissió de doctorat i de recerca del Departament d'EIOConclusions from randomized clinical trials (RCT) rely primarily on the primary endpoint (PE) chosen at the design stage of the study. There should generally be only one PE which should be able to provide the most clinically relevant and scientific evidence regarding the potential eficacy of the new treatment. Therefore, it is of utmost importance to select it appropriately. Composite endpoints, consisting of the union of several endpoints, are often used as PE in RCT. Gomez and Lagakos (2013) develop a statistical methodology to evaluate the convenience of using a CE as opposed to one of its components. Their strategy is based on the asymptotic relative eficiency (ARE), relating the efi is based on the asymptotic relative eficiency (ARE), relating the eciency of using the logrank test based on the CE versus the eficiency based on one of its components. This paper introduces the freeware online platform CompARE that facilitates the study of the performance of different candidate endpoints which could be used as PE at the design stage of a trial. CompARE, through an intuitive interface, implements the novel ARE method.Preprin

Boxplots for grouped and clustered data in toxicology

The vast majority of toxicological papers summarize experimental data as bar charts of means with error bars. While these graphics are easy to generate, they often obscure essential features of the data, such as outliers or subgroups of individuals reacting differently to a treatment. In particular, raw values are of prime importance in toxicology; therefore, we argue they should not be hidden in messy supplementary tables but rather unveiled in neat graphics in the results section. We propose jittered boxplots as a very compact yet comprehensive and intuitively accessible way of visualizing grouped and clustered data from toxicological studies together with individual raw values and indications of statistical significance. A web application to create these plots is available online