arXiv subjects

James Wason

Publications and source records attributed to James Wason.

14 recordsLinked to original sources

arXiv2025

Sample size calculations for multilevel factorial longitudinal cluster randomised trials

Typically, trials investigate the impact of either an individual-level intervention on participant outcomes, or the impact of a cluster-level intervention on participant outcomes. Factorial designs consider two (or more) treatments for each of two (or more) different factors. In factorial trial designs, trial units (individuals or clusters) are each randomised to a level of each of the treatments; these designs allow assessment of the interactions between different interventions. Recently, there has been growing interest in the design of trials that jointly assess the impact of individual- and cluster-level interventions (i.e. multi-level interventions); requiring the development of methodology that accommodates randomisation at multiple levels. While recent work has developed sample size methodology for variants combining standard cluster randomisation and individual randomisation, that work does not apply to longitudinal cluster randomised trial designs such as the stepped wedge design or cluster randomised crossover design. Here we present dedicated sample size methodology for "split-plot factorial longitudinal cluster randomised trials" with continuous outcomes: allowing for joint assessment of individual-level and cluster-level interventions that allows for the impact of the cluster-level intervention to be assessed using any longitudinal cluster randomised trial design. We show how the power to detect given effects of the individual-level intervention, the cluster-level intervention, and the interaction between the two depends on standard results for individually-randomised trials and longitudinal cluster randomised trials. We apply these results to the SharES trial, which considered the effects of a patient- and clinician-level interventions for patients with breast cancer on patient knowledge about the risks and benefits of treatment.

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arXiv2020

Efficient adaptive designs for clinical trials of interventions for COVID-19

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this paper we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.

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arXiv2018

Admissible multi-arm stepped-wedge cluster randomized trial designs

Numerous publications have now addressed the principles of designing, analyzing, and reporting the results of, stepped-wedge cluster randomized trials. In contrast, there is little research available pertaining to the design and analysis of multi-arm stepped-wedge cluster randomized trials, utilized to evaluate the effectiveness of multiple experimental interventions. In this paper, we address this by explaining how the required sample size in these multi-arm trials can be ascertained when data are to be analyzed using a linear mixed model. We then go on to describe how the design of such trials can be optimized to balance between minimizing the cost of the trial, and minimizing some function of the covariance matrix of the treatment effect estimates. Using a recently commenced trial that will evaluate the effectiveness of sensor monitoring in an occupational therapy rehabilitation program for older persons after hip fracture as an example, we demonstrate that our designs could reduce the number of observations required for a fixed power level by up to 58%. Consequently, when logistical constraints permit the utilization of any one of a range of possible multi-arm stepped-wedge cluster randomized trial designs, researchers should consider employing our approach to optimize their trials efficiency.

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arXiv2018

Blinded and unblinded sample size re-estimation in crossover trials balanced for period

The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these parameters at the design stage means that there is often a risk a trial may be under- or over-powered. For many study designs, this problem has been addressed by considering adaptive design methodology that allows for the re-estimation of the required sample size during a trial. Here, we propose and compare several approaches for this in multi-treatment crossover trials. Specifically, regulators favour re-estimation procedures to maintain the blinding of the treatment allocations. We therefore develop blinded estimators for the within and between person variances, following simple or block randomisation. We demonstrate that, provided an equal number of patients are allocated to sequences that are balanced for period, the proposed estimators following block randomisation are unbiased. We further provide a formula for the bias of the estimators following simple randomisation. The performance of these procedures, along with that of an unblinded approach, is then examined utilising three motivating examples, including one based on a recently completed four-treatment four-period crossover trial. Simulation results show that the performance of the proposed blinded procedures is in many cases similar to that of the unblinded approach, and thus they are an attractive alternative.

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arXiv2018

Design optimisation and post-trial analysis in group sequential stepped-wedge cluster randomised trials

Recently, methodology was presented to facilitate the incorporation of interim analyses in stepped-wedge (SW) cluster randomised trials (CRTs). Here, we extend this previous discussion. We detail how the stopping boundaries, allocation sequences, and per-cluster per-period sample size of a group sequential SW-CRT can be optimised. We then describe methods by which point estimates, p-values, and confidence intervals, which account for the sequential nature of the design, can be calculated. We demonstrate that optimal sequential designs can reduce the expected required number of measurements under the null hypothesis, compared to the classical design, by up to 30%, with no cost to the maximal possible required number of measurements. Furthermore, the adjusted analysis procedure almost universally reduces the average bias in the point estimate, and consistently provides a confidence interval with coverage close to the nominal level. In contrast, the coverage of a naive 95% confidence interval is observed to range between 92 and 98%. Methodology is now readily available for the efficient design and analysis of group sequential SW-CRTs. In scenarios in which there are substantial ethical or financial reasons to terminate a SW-CRT as soon as possible, trialists should strongly consider a group sequential approach.

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arXiv2017

Efficient determination of optimised multi-arm multi-stage experimental designs with control of generalised error-rates

Primarily motivated by the drug development process, several publications have now presented methodology for the design of multi-arm multi-stage experiments with normally distributed outcome variables of known variance. Here, we extend these past considerations to allow the design of what we refer to as an abcd multi-arm multi-stage experiment. We provide a proof of how strong control of the a-generalised type-I familywise error-rate can be ensured. We then describe how to attain the power to reject at least b out of c false hypotheses, which is related to controlling the b-generalised type-II familywise error-rate. Following this, we detail how a design can be optimised for a scenario in which rejection of any d null hypotheses brings about termination of the experiment. We achieve this by proposing a highly computationally efficient approach for evaluating the performance of a candidate design. Finally, using a real clinical trial as a motivating example, we explore the effect of the design's control parameters on the statistical operating characteristics.

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arXiv2017

Group Sequential Clinical Trial Designs for Normally Distributed Outcome Variables

In a group sequential clinical trial, accumulated data are analysed at numerous time-points in order to allow early decisions about a hypothesis of interest. These designs have historically been recommended for their ethical, administrative and economic benefits. In this work, we discuss a collection of new Stata commands for computing the stopping boundaries and required group size of various classical group sequential designs, assuming a normally distributed outcome variable. Following this, we demonstrate how the performance of several designs can be compared graphically.

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arXiv2017

A two-stage Fisher exact test for multi-arm studies with binary outcome variables

In small sample studies with binary outcome data, use of a normal approximation for hypothesis testing can lead to substantial inflation of the type-I error-rate. Consequently, exact statistical methods are necessitated, and accordingly, much research has been conducted to facilitate this. Recently, this has included methodology for the design of two-stage multi-arm studies utilising exact binomial tests. These designs were demonstrated to carry substantial efficiency advantages over a fixed sample design, but generally suffered from strong conservatism. An alternative classical means of small sample inference with dichotomous data is Fisher's exact test. However, this method is limited to single-stage designs when there are multiple arms. Therefore, here, we propose a two-stage version of Fisher's exact test, with the potential to stop early to accept or reject null hypotheses, which is applicable to multi-arm studies. In particular, we provide precise formulae describing the requirements for achieving weak or strong control of the familywise error-rate with this design. Following this, we describe how the design parameters may be optimised to confer desirable operating characteristics. For a motivating example based on a phase II clinical trial, we demonstrate that on average our approach is less conservative than corresponding optimal designs based on exact binomial tests.

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arXiv2017

An optimised multi-arm multi-stage clinical trial design for unknown variance

Multi-arm multi-stage trial designs can bring notable gains in efficiency to the drug development process. However, for normally distributed endpoints, the determination of a design typically depends on the assumption that the patient variance in response is known. In practice, this will not usually be the case. To allow for unknown variance, previous research explored the performance of t-test statistics, coupled with a quantile substitution procedure for modifying the stopping boundaries, at controlling the familywise error-rate to the nominal level. Here, we discuss an alternative method based on Monte Carlo simulation that allows the group size and stopping boundaries of a multi-arm multi-stage t-test to be optimised according to some nominated optimality criteria. We consider several examples, provide R code for general implementation, and show that our designs confer a familywise error-rate and power close to the desired level. Consequently, this methodology will provide utility in future multi-arm multi-stage trials.

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arXiv2017

Group Sequential Crossover Trial Designs with Strong Control of the Familywise Error Rate

Crossover designs are an extremely useful tool to investigators, whilst group sequential methods have proven highly proficient at improving the efficiency of parallel group trials. Yet, group sequential methods and crossover designs have rarely been paired together. One possible explanation for this could be the absence of a formal proof of how to strongly control the familywise error rate in the case when multiple comparisons will be made. Here, we provide this proof, valid for any number of initial experimental treatments and any number of stages, when results are analysed using a linear mixed model. We then establish formulae for the expected sample size and expected number of observations of such a trial, given any choice of stopping boundaries. Finally, utilising the four-treatment, four-period TOMADO trial as an example, we demonstrate group sequential methods in this setting could have reduced the trials expected number of observations under the global null hypothesis by over 33%.

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arXiv2017

Blinded and unblinded sample size re-estimation procedures for stepped-wedge cluster randomized trials

The ability to accurately estimate the sample size required by a stepped-wedge (SW) cluster randomized trial (CRT) routinely depends upon the specification of several nuisance parameters. If these parameters are mis-specified, the trial could be over-powered, leading to increased cost, or under-powered, enhancing the likelihood of a false negative. We address this issue here for cross-sectional SW-CRTs, analyzed with a particular linear mixed model, by proposing methods for blinded and unblinded sample size re-estimation (SSRE). Blinded estimators for the variance parameters of a SW-CRT analyzed using the Hussey and Hughes model are derived. Then, procedures for blinded and unblinded SSRE after any time period in a SW-CRT are detailed. The performance of these procedures is then examined and contrasted using two example trial design scenarios. We find that if the two key variance parameters were under-specified by 50%, the SSRE procedures were able to increase power over the conventional SW-CRT design by up to 29%, resulting in an empirical power above the desired level. Moreover, the performance of the re-estimation procedures was relatively insensitive to the timing of the interim assessment. Thus, the considered SSRE procedures can bring substantial gains in power when the underlying variance parameters are mis-specified. Though there are practical issues to consider, the procedure's performance means researchers should consider incorporating SSRE in to future SW-CRTs.

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arXiv2017

Response adaptive designs for binary responses: how to offer patient benefit while being robust to time trends?

Response-adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not feasible or ethically objectionable. In this paper we discuss and address a major criticism of RAR: the undesirable type I error inflation due to unknown time trends in the trial. Time trends can appear because of changes in the characteristics of recruited patients - so-called "patient drift". Patient drift is a realistic concern for clinical trials in rare diseases because these typically recruit patients over a very long period of time. We compute by simulations how large the type I error inflation is as a function of the time trend magnitude in order to determine in which contexts a potentially costly correction is actually necessary. We then assess the ability of different correction methods to preserve type I error in this context and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations of which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the two-armed and the multi-armed case. We further propose a RAR design for multi-armed clinical trials, which is computationally cheap and robust to several time trends considered.

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arXiv2017

Improving phase II oncology trials using best observed RECIST response as an endpoint by modelling continuous tumour measurements

In many phase II trials in solid tumours, patients are assessed using endpoints based on the Response Evaluation Criteria in Solid Tumours (RECIST) scale. Often, analyses are based on the response rate. This is the proportion of patients who have an observed tumour shrinkage above a pre-defined level and no new tumour lesions. The augmented binary method has been proposed to improve the precision of the estimator of the response rate. The method involves modelling the tumour shrinkage to avoid dichotomising it. However, in many trials the best observed response is used as the primary outcome. In such trials, patients are followed until progression, and their best observed RECIST outcome is used as the primary endpoint. In this paper, we propose a method that extends the augmented binary method so that it can be used when the outcome is best observed response. We show through simulated data and data from a real phase II cancer trial that this method improves power in both single-arm and randomised trials. The average gain in power compared to the traditional analysis is equivalent to approximately a 35% increase in sample size. A modified version of the method is proposed to reduce the computational effort required. We show this modified method maintains much of the efficiency advantages.

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arXiv2015

Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges

Multi-armed bandit problems (MABPs) are a special type of optimal control problem well suited to model resource allocation under uncertainty in a wide variety of contexts. Since the first publication of the optimal solution of the classic MABP by a dynamic index rule, the bandit literature quickly diversified and emerged as an active research topic. Across this literature, the use of bandit models to optimally design clinical trials became a typical motivating application, yet little of the resulting theory has ever been used in the actual design and analysis of clinical trials. To this end, we review two MABP decision-theoretic approaches to the optimal allocation of treatments in a clinical trial: the infinite-horizon Bayesian Bernoulli MABP and the finite-horizon variant. These models possess distinct theoretical properties and lead to separate allocation rules in a clinical trial design context. We evaluate their performance compared to other allocation rules, including fixed randomization. Our results indicate that bandit approaches offer significant advantages, in terms of assigning more patients to better treatments, and severe limitations, in terms of their resulting statistical power. We propose a novel bandit-based patient allocation rule that overcomes the issue of low power, thus removing a potential barrier for their use in practice.

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