Michael J. Grayling

Stepped wedge cluster randomized controlled trial designs: a review of reporting quality and design features

BackgroundThe stepped wedge (SW) cluster randomized controlled trial (CRCT) design is being used with increasing frequency. However, there is limited published research on the quality of reporting of SW-CRCTs. We address this issue by conducting a literature review.MethodsMedline, Ovid, Web of Knowledge, the Cochrane Library, PsycINFO, the ISRCTN registry, and ClinicalTrials.gov were searched to identify investigations employing the SW-CRCT design up to February 2015. For each included completed study, information was extracted on a selection of criteria, based on the CONSORT extension to CRCTs, to assess the quality of reporting.ResultsA total of 123 studies were included in our review, of which 39 were completed trial reports. The standard of reporting of SW-CRCTs varied in quality. The percentage of trials reporting each criterion varied to as low as 15.4%, with a median of 66.7%.ConclusionsThere is much room for improvement in the quality of reporting of SW-CRCTs. This is consistent with recent findings for CRCTs. A CONSORT extension for SW-CRCTs is warranted to standardize the reporting of SW-CRCTs.

Do single-arm trials have a role in drug development plans incorporating randomised trials?

Often, single‐arm trials are used in phase II to gather the first evidence of an oncological drugs efficacy, with drug activity determined through tumour response using the RECIST criterion. Provided the null hypothesis of ‘insufficient drug activity’ is rejected, the next step could be a randomised two‐arm trial. However, single‐arm trials may provide a biased treatment effect because of patient selection, and thus, this development plan may not be an efficient use of resources. Therefore, we compare the performance of development plans consisting of single‐arm trials followed by randomised two‐arm trials with stand‐alone single‐stage or group sequential randomised two‐arm trials. Through this, we are able to investigate the utility of single‐arm trials and determine the most efficient drug development plans, setting our work in the context of a published single‐arm non‐small‐cell lung cancer trial. Reference priors, reflecting the opinions of ‘sceptical’ and ‘enthusiastic’ investigators, are used to quantify and guide the suitability of single‐arm trials in this setting. We observe that the explored development plans incorporating single‐arm trials are often non‐optimal. Moreover, even the most pessimistic reference priors have a considerable probability in favour of alternative plans. Analysis suggests expected sample size savings of up to 25% could have been made, and the issues associated with single‐arm trials avoided, for the non‐small‐cell lung cancer treatment through direct progression to a group sequential randomised two‐arm trial. Careful consideration should thus be given to the use of single‐arm trials in oncological drug development when a randomised trial will follow. Copyright

Group sequential crossover trial designs with strong control of the familywise error rate

Abstract Crossover designs are an extremely useful tool to investigators, and 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 analyzed 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, utilizing the four-treatment, four-period TOMADO trial as an example, we demonstrate that group sequential methods in this setting could have reduced the trials expected number of observations under the global null hypothesis by over 33%.

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.

Blinded and unblinded sample size reestimation in crossover trials balanced for period

Abstract 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 overpowered. 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 multitreatment crossover trials. Specifically, regulators favor reestimation 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 randomization. We demonstrate that, provided an equal number of patients are allocated to sequences that are balanced for period, the proposed estimators following block randomization are unbiased. We further provide a formula for the bias of the estimators following simple randomization. The performance of these procedures, along with that of an unblinded approach, is then examined utilizing 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.

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

Abstract 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 misspecified, the trial could be overpowered, leading to increased cost, or underpowered, 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 reestimation (SSRE). First, blinded estimators for the variance parameters of a SW‐CRT analyzed using the Hussey and Hughes model are derived. Following this, 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 underspecified by 50%, the SSRE procedures were able to increase power over the conventional SW‐CRT design by up to 41%, resulting in an empirical power above the desired level. Thus, though there are practical issues to consider, the performance of the procedures means researchers should consider incorporating SSRE in to future SW‐CRTs.

Stepped-wedge cluster randomised trials: where, when and why?

The stepped wedge (SW) cluster randomised trial (CRT) design is being utilised at increasing pace. However, little is known about the standard of reporting of such trials, or how useful the design has proven to be in hindsight. Moreover, much debate exists around when the design should be preferred to the more classical parallel group (PG) CRT. Here, we address these issues by first conducting a thorough review of all SW-CRTs. We are able to ascertain not only the quality of reporting, but also the stated reasons for the designs use. We are also able to highlight instances where, on reflection, alternative designs may have been preferable. We then present a critical appraisal of the design from a logistical and ethical standpoint. With our findings we propose methodology for the incorporation of early stopping for futility within a SW-CRT. Finally, we compare this new design in terms of expected efficiency to the conventional approach as well as to several variants of the PG-CRT design. We assess which is optimal in a range of settings, including balancing the required sample size with the required time for trial completion. We find that to date the standard of reporting of SW-CRTs has been mixed in quality. However, there are many instances in which the design is preferable to the PG-CRT approach. In particular, through the addition of early futility stopping, sample size savings under the null hypothesis of more than 30% can be observed, at little cost to the length of the trial.