Optimal allocation to treatment sequences in individually randomized stepped-wedge designs with attrition

Moerbeek, Mirjam

doi:10.1177/17407745231154260

Cited by 3 publications

(4 citation statements)

References 31 publications

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“…Furthermore, we opted in the simulations for a balanced allocation of subjects to sequences. However, Moerbeek's (2023) study demonstrated that uniform allocation is not necessarily the most optimal and depends on the correlation rate as well as the attrition rate (which we assumed to be absent in our study). It is, therefore, advisable to consider an imbalanced allocation in certain specific situations by increasing the number of participants allocated to certain sequences (typically the first and the last ones in particular).…”

Section: Discussionmentioning

confidence: 89%

“…However, its use is reviewed among multiple designs addressing challenges in small clinical trials (Allemang-Trivalle et al 2023;Cornu et al 2013) to study rare disorders (Abrahamyan et al 2016) or studies of fragile populations such as children (Balevic While several publications focused on the analysis of ISW-RTs 2 designs, none, to our knowledge, proposed a sample size derivation. There is a recent study that focused on deriving the optimal allocation of individuals to treatment sequences in ISW-RT designs, considering the nesting of repeated measurements and possible attrition (Moerbeek 2023). However, it did not address the issue of determining the required sample size and its variations based on different parameters.…”

Section: Introductionmentioning

confidence: 99%

“…While several publications focused on the analysis of ISW‐RTs 2 designs, none, to our knowledge, proposed a sample size derivation. There is a recent study that focused on deriving the optimal allocation of individuals to treatment sequences in ISW‐RT designs, considering the nesting of repeated measurements and possible attrition (Moerbeek 2023). However, it did not address the issue of determining the required sample size and its variations based on different parameters.…”

Section: Introductionmentioning

confidence: 99%

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Sample Size Calculation for an Individual Stepped‐Wedge Randomized Trial

Allemang‐Trivalle,

Maruani,

Giraudeau

2024

Biometrical J

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In the individual stepped‐wedge randomized trial (ISW‐RT), subjects are allocated to sequences, each sequence being defined by a control period followed by an experimental period. The total follow‐up time is the same for all sequences, but the duration of the control and experimental periods varies among sequences. To our knowledge, there is no validated sample size calculation formula for ISW‐RTs unlike stepped‐wedge cluster randomized trials (SW‐CRTs). The objective of this study was to adapt the formula used for SW‐CRTs to the case of individual randomization and to validate this adaptation using a Monte Carlo simulation study. The proposed sample size calculation formula for an ISW‐RT design yielded satisfactory empirical power for most scenarios except scenarios with operating characteristic values near the boundary (i.e., smallest possible number of periods, very high or very low autocorrelation coefficient). Overall, the results provide useful insights into the sample size calculation for ISW‐RTs.

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sample Size Calculation for an Individual Stepped‐Wedge Randomized Trial

Allemang‐Trivalle,

Maruani,

Giraudeau

2024

Biometrical J

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“…The stepped wedge design is a longitudinal trials design, and in such designs attrition is the rule rather than the exception. For the individually randomized stepped wedge design [ 34 ], it has already been shown that attrition may have a large effect on the optimal allocation and also precision of the treatment effect estimator. For large values of the individual autocorrelation coefficient the design is uniform in the case attrition is absent.…”

Section: Resultsmentioning

confidence: 99%

Optimal allocation of clusters in stepped wedge designs with a decaying correlation structure

Moerbeek

2023

PLoS ONE

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The cluster randomized stepped wedge design is a multi-period uni-directional switch design in which all clusters start in the control condition and at the beginning of each new period a random sample of clusters crosses over to the intervention condition. Such designs often use uniform allocation, with an equal number of clusters at each treatment switch. However, the uniform allocation is not necessarily the most efficient. This study derives the optimal allocation of clusters to treatment sequences in the cluster randomized stepped wedge design, for both cohort and cross-sectional designs. The correlation structure is exponential decay, meaning the correlation decreases with the time lag between two measurements. The optimal allocation is shown to depend on the intraclass correlation coefficient, the number of subjects per cluster-period and the cluster and (in the case of a cohort design) individual autocorrelation coefficients. For small to medium values of these autocorrelations those sequences that have their treatment switch earlier or later in the study are allocated a larger proportion of clusters than those clusters that have their treatment switch halfway the study. When the autocorrelation coefficients increase, the clusters become more equally distributed across the treatment sequences. For the cohort design, the optimal allocation is almost equal to the uniform allocation when both autocorrelations approach the value 1. For almost all scenarios that were studied, the efficiency of the uniform allocation is 0.8 or higher. R code to derive the optimal allocation is available online.

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Efficient designs for three-sequence stepped wedge trials with continuous recruitment

Hooper,

Quintin,

Kasza

2024

Clinical Trials

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Background/Aims: The standard approach to designing stepped wedge trials that recruit participants in a continuous stream is to divide time into periods of equal length. But the choice of design in such cases is infinitely more flexible: each cluster could cross from the control to the intervention at any point on the continuous time-scale. We consider the case of a stepped wedge design with clusters randomised to just three sequences (designs with small numbers of sequences may be preferred for their simplicity and practicality) and investigate the choice of design that minimises the variance of the treatment effect estimator under different assumptions about the intra-cluster correlation. Methods: We make some simplifying assumptions in order to calculate the variance: in particular that we recruit the same number of participants, [Formula: see text], from each cluster over the course of the trial, and that participants present at regularly spaced intervals. We consider an intra-cluster correlation that decays exponentially with separation in time between the presentation of two individuals from the same cluster, from a value of [Formula: see text] for two individuals who present at the same time, to a value of [Formula: see text] for individuals presenting at the start and end of the trial recruitment interval. We restrict attention to three-sequence designs with centrosymmetry – the property that if we reverse time and swap the intervention and control conditions then the design looks the same. We obtain an expression for the variance of the treatment effect estimator adjusted for effects of time, using methods for generalised least squares estimation, and we evaluate this expression numerically for different designs, and for different parameter values. Results: There is a two-dimensional space of possible three-sequence, centrosymmetric stepped wedge designs with continuous recruitment. The variance of the treatment effect estimator for given [Formula: see text] and [Formula: see text] can be plotted as a contour map over this space. The shape of this variance surface depends on [Formula: see text] and on the parameter [Formula: see text], but typically indicates a broad, flat region of close-to-optimal designs. The ‘standard’ design with equally spaced periods and 1:1:1 allocation rarely performs well, however. Conclusions: In many different settings, a relatively simple design can be found (e.g. one based on simple fractions) that offers close-to-optimal efficiency in that setting. There may also be designs that are robustly efficient over a wide range of settings. Contour maps of the kind we illustrate can help guide this choice. If efficiency is offered as one of the justifications for using a stepped wedge design, then it is worth designing with optimal efficiency in mind.

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Optimal allocation to treatment sequences in individually randomized stepped-wedge designs with attrition

Cited by 3 publications

References 31 publications

Sample Size Calculation for an Individual Stepped‐Wedge Randomized Trial

Sample Size Calculation for an Individual Stepped‐Wedge Randomized Trial

Optimal allocation of clusters in stepped wedge designs with a decaying correlation structure

Efficient designs for three-sequence stepped wedge trials with continuous recruitment

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