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

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

doi:10.1002/bimj.201700092

Cited by 10 publications

(12 citation statements)

References 33 publications

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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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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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“…Accordingly, Grayling et al [12] specified the denominator degrees of freedom as that of a corresponding multi-level single-stage ANOVA design. For our unrestricted model this would designate the denominator degrees of freedom, ν , for sample size n , as …”

Section: Methodsmentioning

confidence: 99%

“…Such an approach, however, was found by Grayling et al [12] to, in many circumstances, lead to a notable inflation of the type-I error rate, and frequently provide power slightly below the desired level under the specified alternative. Therefore, they discussed the utility of a potential α -adjustment procedure, of using the above methodology for interim re-estimation but performing the final hypothesis test using the method of Kenward and Roger [15], and they explored the advantages of a sample size inflation factor.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, a collection of papers have addressed sample size re-estimation in bioequivalence trials using a two-period two-treatment crossover design [8–11]. While more recently, methodology for blinded and unblinded sample size re-estimation in multi-treatment crossover trials balanced for period was described [12]. None of these papers, however, directly allows for re-estimation in the context of the AIM HY-INFORM study, for reasons that will be described shortly.…”

Section: Introductionmentioning

confidence: 99%

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Sample size re-estimation in crossover trials: application to the AIM HY-INFORM study

Wych

Grayling

Mander

2019

Trials

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BackgroundCrossover designs are commonly utilised in randomised controlled trials investigating treatments for long-term chronic illnesses. One problem with this design is its inherent repeated measures necessitate the availability of an estimate of the within-person standard deviation (SD) to perform a sample size calculation, which may be rarely available at the design stage of a trial. Interim sample size re-estimation designs can be used to help alleviate this issue by adapting the sample size mid-way through the trial, using accrued information in a statistically robust way.MethodsThe AIM HY-INFORM study is part of the Informative Markers in Hypertension (AIM HY) Programme and comprises two crossover trials, each with a planned recruitment of 600 participants. The objective of the study is to test whether blood pressure response to first line antihypertensive treatment depends on ethnicity. An interim analysis is planned to reassess the assumptions of the planned sample size for the study. The aims of this paper are: (1) to provide a formula for sample size re-estimation in both crossover trials; and (2) to present a simulation study of the planned interim analysis to investigate alternative within-person SDs to that assumed.ResultsThe AIM HY-INFORM protocol sample size calculation fixes the within-person SD to be 8 mmHg, giving > 90% power for a primary treatment effect of 4 mmHg. Using the method developed here and simulating the interim sample size reassessment, if we were to see a larger within-person SD of 9 mmHg at interim, 640 participants for 90% power 90% of the time in the three-period three-treatment design would be required. Similarly, in the four-period four-treatment crossover design, 602 participants would be required.ConclusionsThe formulas presented here provide a method for re-estimating the sample size in crossover trials. In the context of the AIM HY-INFORM study, simulating the interim analysis allows us to explore the results of a possible increase in the within-person SD from that assumed. Simulations show that without increasing the planned sample size of 600 participants, we can reasonably still expect to achieve 80% power with a small increase in the within-person SD from that assumed.Trial registrationClinicalTrials.gov, NCT02847338. Registered on 28 July 2016.

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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%

Planung multizentrischer randomisierter klinischer Studien mit kontinuierlichem Endpunkt

Harden¹

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Background: Multi-centre randomized controlled clinical trials play an important role in modern evidence-based medicine. Advantages of collecting data from more than one site are numerous, including accelerated recruitment and increased generalisability of results. Mixed models can be applied to account for potential clustering in the data, in particular when many small centres contribute patients to the study. Previously proposed methods on sample size calculation for mixed models only considered balanced treatment allocations which is an unlikely outcome in practice if block randomisation with reasonable choices of block length is used. Methods: We propose a sample size determination procedure for multi-centre trials comparing two treatment groups for a continuous outcome, modelling centre differences using random effects and allowing for arbitrary sample sizes. It is assumed that block randomisation with fixed block length is used at each study site for subject allocation. Simulations are used to assess operation characteristics such as power of the sample size approach. The proposed method is illustrated by an example in disease management systems. Results: A sample size formula as well as a lower and upper boundary for the required overall sample size are given. We demonstrate the superiority of the new sample size formula over the conventional approach of ignoring the multi-centre structure and show the influence of parameters such as block length or centre heterogeneity. The application of the procedure on the example data shows that large blocks require larger sample sizes, if centre heterogeneity is present. Conclusion: Unbalanced treatment allocation can result in substantial power loss when centre heterogeneity is present but not considered at the planning stage. When only few patients by centre will be recruited, one has to weigh the risk of imbalance between treatment groups due to large blocks and the risk of unblinding due to small blocks. The proposed approach should be considered when planning multi-centre trials.

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

Cited by 10 publications

References 33 publications

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

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

Sample size re-estimation in crossover trials: application to the AIM HY-INFORM study

Planung multizentrischer randomisierter klinischer Studien mit kontinuierlichem Endpunkt

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