swCRTdesign: An <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="sans-serif">R</mml:mi></mml:math>Package for Stepped Wedge Trial Design and Analysis

Voldal, Emily C; Hakhu, Navneet R.; Xia, Fan; Heagerty, Patrick J.

doi:10.1016/j.cmpb.2020.105514

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…[4][5][6] Power calculation methods have focused on a linear model with normal approximation for binary outcomes. 16,17 In this article, we illustrate that accounting for a logistic marginal model at the study planning stage, when this is the planned analysis method, tends to result in lower estimated power than when a linear model is incorrectly assumed. The reduction in power is larger when there are a smaller number of individuals at each time-period and fewer time-periods.…”

Section: Discussionmentioning

confidence: 99%

“…Following the landmark paper by Hussey and Hughes, 14 several publications and widely used software [14][15][16][17]21 have the capability to perform power calculation based on an average treatment effect in the following linear random-intercepts model with discrete fixed time-effects:…”

Section: Power Calculation With a Linear Modelmentioning

confidence: 99%

“…13 The majority of power calculation methods and software specific to SW-CRTs have been developed under a linear mixed-effects (LME) modeling framework. [14][15][16][17] Although a linear model is most suited to a continuous outcome, it has been applied in practice to calculate the power for a binary outcome where the intervention effect is on the prevalence difference scale. The assumption of a linear model is not ideal, since: i) the variance of a binary outcome is a function of the mean, hence it changes as the outcome probability changes and ii) the predicted outcome probability based on a linear model may be outside the (0, 1) range.…”

Section: Introductionmentioning

confidence: 99%

“…Methods based on link functions other than the identity have not been extensively discussed for SW-CRTs, 19 and are highlighted as an area for future development. 17 In particular, for binary outcomes under a logit link function, we are aware of two power calculation publications; firstly, a method was developed by Li et al 20 under the generalized estimating equations (GEEs) approach, and secondly, a simulation-based method under a generalized linear mixed-effects model (GLMM) was investigated by Baio et al 21 Neither included a comparison to the power estimated if a linear model was assumed.…”

Section: Introductionmentioning

confidence: 99%

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Power calculation for analyses of cross‐sectional stepped‐wedge cluster randomized trials with binary outcomes via generalized estimating equations

Harrison

Wang

2021

Statistics in Medicine

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Power calculation for stepped-wedge cluster randomized trials (SW-CRTs) presents unique challenges, beyond those of standard parallel cluster randomized trials, due to the need to consider temporal within cluster correlations and background period effects. To date, power calculation methods specific to SW-CRTs have primarily been developed under a linear model. When the outcome is binary, the use of a linear model corresponds to assessing a prevalence difference; yet trial analysis often employs a nonlinear link function.We propose power calculation methods for cross-sectional SW-CRTs under a logistic model fitted by generalized estimating equations. Firstly, under an exchangeable correlation structure, we show the power based on a logistic model is lower than that from assuming a linear model in the absence of period effects. We then evaluate the impact of background prevalence changes over time on power. To allow the correlation among outcomes in the same cluster to change over time and with treatment status, we generalize the methods to more complex correlation structures. Our simulation studies demonstrate that the proposed power calculation methods perform well with the model-based variance under the true correlation structure and reveal that a working independence structure can result in substantial efficiency loss, while a working exchangeable structure performs well even when the underlying correlation structure deviates from exchangeable. An extension to our methods accounts for variable cluster sizes and reveals that unequal cluster sizes have a modest impact on power. We illustrate the approaches by application to a quality of care improvement trial for acute coronary syndrome.

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Section: Discussionmentioning

confidence: 99%

Section: Power Calculation With a Linear Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power calculation for analyses of cross‐sectional stepped‐wedge cluster randomized trials with binary outcomes via generalized estimating equations

Harrison

Wang

2021

Statistics in Medicine

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“…The method is intuitive, requires minimal computational power, and allows for rapid dynamic updates of sample size calculations when different parameters or design options are of interest. To facilitate adoption of our methods, we also provide a function embedded in the R package “swCRTdesign” [ 21 ] for sample size and power calculation for multilevel stepped wedge designs.…”

Section: Introductionmentioning

confidence: 99%

Power and sample size calculation for stepped-wedge designs with discrete outcomes

Xia

Voldal

Heagerty

2021

Trials

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Background Stepped-wedge designs (SWD) are increasingly used to evaluate the impact of changes to the process of care within health care systems. However, to generate definitive evidence, a correct sample size calculation is crucial to ensure such studies are properly powered. The seminal work of Hussey and Hughes (Contemp Clin Trials 28(2):182–91, 2004) provides an analytical formula for power calculations with normal outcomes using a linear model and simple random effects. However, minimal development and evaluation have been done for power calculation with non-normal outcomes on their natural scale (e.g., logit, log). For example, binary endpoints are common, and logistic regression is the natural multilevel model for such clustered data. Methods We propose a power calculation formula for SWD with either normal or non-normal outcomes in the context of generalized linear mixed models by adopting the Laplace approximation detailed in Breslow and Clayton (J Am Stat Assoc 88(421):9–25, 1993) to obtain the covariance matrix of the estimated parameters. Results We compare the performance of our proposed method with simulation-based sample size calculation and demonstrate its use on a study of patient-delivered partner therapy for STI treatment and a study that assesses the impact of providing additional benchmark prevalence information in a radiologic imaging report. To facilitate adoption of our methods we also provide a function embedded in the R package “swCRTdesign” for sample size and power calculation for multilevel stepped-wedge designs. Conclusions Our method requires minimal computational power. Therefore, the proposed procedure facilitates rapid dynamic updates of sample size calculations and can be used to explore a wide range of design options or assumptions.

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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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swCRTdesign: An RPackage for Stepped Wedge Trial Design and Analysis

Cited by 15 publications

References 10 publications

Power calculation for analyses of cross‐sectional stepped‐wedge cluster randomized trials with binary outcomes via generalized estimating equations

Power calculation for analyses of cross‐sectional stepped‐wedge cluster randomized trials with binary outcomes via generalized estimating equations

Power and sample size calculation for stepped-wedge designs with discrete outcomes

Sample Size Calculation for an Individual Stepped‐Wedge Randomized Trial

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