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

Grayling, Michael J.; Mander, Adrian; Wason, James

doi:10.1002/bimj.201700125

Cited by 12 publications

(15 citation statements)

References 31 publications

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“…Finally, the proposed sample size formulas are only applicable to cross‐sectional parallel CRTs. As there is an increasing interest in alternative cluster‐randomized designs, such as the cluster‐randomized crossover design (Li et al., 2019) and the stepped wedge design (Grayling et al., 2018; Li et al., 2018), it would be valuable to develop corresponding sample size formulas to account for right truncations for these alternative designs. For example, one may extend the sample size formulas in Li et al.…”

Section: Discussionmentioning

confidence: 99%

Sample size and power considerations for cluster randomized trials with count outcomes subject to right truncation

Tong

2021

Biometrical J

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Cluster randomized trials (CRTs) are widely used in epidemiological and public health studies assessing population‐level effect of group‐based interventions. One important application of CRTs is the control of vector‐borne disease, such as malaria. However, a particular challenge for designing these trials is that the primary outcome involves counts of episodes that are subject to right truncation. While sample size formulas have been developed for CRTs with clustered counts, they are not directly applicable when the counts are right truncated. To address this limitation, we discuss two marginal modeling approaches for the analysis of CRTs with truncated counts and develop two corresponding closed‐form sample size formulas to facilitate the design of such trials. The proposed sample size formulas allow investigators to explore the power under a large number of scenarios without computationally intensive simulations. The proposed formulas are validated in extensive simulations. We further explore the implication of right truncation on power and apply the proposed formulas to illustrate the power calculation for a malaria control CRT where the primary outcome is subject to right truncation.

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Section: Discussionmentioning

confidence: 99%

Sample size and power considerations for cluster randomized trials with count outcomes subject to right truncation

Tong

2021

Biometrical J

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“…The estimator is unbiased, if all randomization blocks are complete. This estimation technique has also been extended to τ 2 in cross‐over as well as cluster randomized trials (Grayling, Mander, & Wason, 2018a, 2018b). Since this estimator relies on balanced data, we did not consider it here.…”

Section: Discussionmentioning

confidence: 99%

Sample size recalculation in multicenter randomized controlled clinical trials based on noncomparative data

Harden

Friede

2020

Biometrical J

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Many late‐phase clinical trials recruit subjects at multiple study sites. This introduces a hierarchical structure into the data that can result in a power‐loss compared to a more homogeneous single‐center trial. Building on a recently proposed approach to sample size determination, we suggest a sample size recalculation procedure for multicenter trials with continuous endpoints. The procedure estimates nuisance parameters at interim from noncomparative data and recalculates the sample size required based on these estimates. In contrast to other sample size calculation methods for multicenter trials, our approach assumes a mixed effects model and does not rely on balanced data within centers. It is therefore advantageous, especially for sample size recalculation at interim. We illustrate the proposed methodology by a study evaluating a diabetes management system. Monte Carlo simulations are carried out to evaluate operation characteristics of the sample size recalculation procedure using comparative as well as noncomparative data, assessing their dependence on parameters such as between‐center heterogeneity, residual variance of observations, treatment effect size and number of centers. We compare two different estimators for between‐center heterogeneity, an unadjusted and a bias‐adjusted estimator, both based on quadratic forms. The type 1 error probability as well as statistical power are close to their nominal levels for all parameter combinations considered in our simulation study for the proposed unadjusted estimator, whereas the adjusted estimator exhibits some type 1 error rate inflation. Overall, the sample size recalculation procedure can be recommended to mitigate risks arising from misspecified nuisance parameters at the planning stage.

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“…The greater the number of sample size re-estimations or number of nuisance parameters, the higher this inflation. In individually randomised trials, conducting sample size re-estimation without knowledge of treatment indicators (known perhaps confusingly as "blinded sample size re-estimation") reduces this risk 1415 16 and this is achieved using pooled data to estimate nuisance parameters 14 and methods exist to extend this to some cluster trial designs 9 .…”

Section: Interim Monitoring Of Parameters That Inform the Sample Size Calculationmentioning

confidence: 99%

“…Cluster randomised trials (CRTs) are a firmly established alternative to individually randomised trials. 1 While there is a growing body of methodological work on how to monitor individually randomised trials [2][3][4] and technical literature on how these methods can be extended to CRTs, [5][6][7][8][9] practical resources for monitoring cluster trials is limited. 3 Cluster trials have many different features to individually randomised trials.…”

Section: Introductionmentioning

confidence: 99%

Interim data monitoring in cluster randomised trials: Practical issues and a case study

et al. 2021

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Background There is an abundance of guidance for the interim monitoring of individually randomised trials. While methodological literature exists on how to extend these methods to cluster randomised trials, there is little guidance on practical implementation. Cluster trials have many features which make their monitoring needs different. We outline the methodological and practical challenges of interim monitoring of cluster trials; and apply these considerations to a case study. Case study The E-MOTIVE study is an 80-cluster randomised trial of a bundle of interventions to treat postpartum haemorrhage. The proposed data monitoring plan includes (1) monitor sample size assumptions, (2) monitor for evidence of selection bias, and (3) an interim assessment of the primary outcome, as well as monitoring data completeness. The timing of the sample size monitoring is chosen with both consideration of statistical precision and to allow time to recruit more clusters. Monitoring for selection bias involves comparing individual-level characteristics and numbers recruited between study arms to identify any post-randomisation participant identification bias. An interim analysis of outcomes presented with 99.9% confidence intervals using the Haybittle–Peto approach should mitigate any concern regarding the inflation of type-I error. The pragmatic nature of the trial means monitoring for adherence is not relevant, as it is built into a process evaluation. Conclusions The interim analyses of cluster trials have a number of important differences to monitoring individually randomised trials. In cluster trials, there will often be a greater need to monitor nuisance parameters, yet there will often be considerable uncertainty in their estimation. This means the utility of sample size re-estimation can be questionable particularly when there are practical or funding difficulties associated with making any changes to planned sample sizes. Perhaps most importantly interim monitoring has the potential to identify selection bias, particularly in trials with post-randomisation identification or recruitment. Finally, the pragmatic nature of cluster trials might mean that the utility of methods to allow for interim monitoring of outcomes based on statistical testing, or monitoring for adherence to study interventions, are less relevant. Our intention is to facilitate the planning of future cluster randomised trials and to promote discussion and debate to improve monitoring of these studies.

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

Cited by 12 publications

References 31 publications

Sample size and power considerations for cluster randomized trials with count outcomes subject to right truncation

Sample size and power considerations for cluster randomized trials with count outcomes subject to right truncation

Sample size recalculation in multicenter randomized controlled clinical trials based on noncomparative data

Interim data monitoring in cluster randomised trials: Practical issues and a case study

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