Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness

Turner, Elizabeth L.; Yao, Lanqiu; Li, Fan; Prague, Mélanie

doi:10.1177/0962280219859915

Cited by 11 publications

(14 citation statements)

References 62 publications

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“…Turner et al. 113 recently studied the relative merits of these two mainstream missing data approaches for parallel CRTs, and it would be of interest to consider their extensions to stepped wedge CRTs.…”

Section: Discussionmentioning

confidence: 99%

“…When the drop-out mechanism can be considered as missing at random, 112 one may use inverse probability weighting or multilevel multiple imputation to reduce the bias due to missing outcomes. Turner et al 113 recently studied the relative merits of these two mainstream missing data approaches for parallel CRTs, and it would be of interest to consider their extensions to stepped wedge CRTs.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview

Hughes

Hemming

et al. 2020

Stat Methods Med Res

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114

154

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The stepped wedge cluster randomized design has received increasing attention in pragmatic clinical trials and implementation science research. The key feature of the design is the unidirectional crossover of clusters from the control to intervention conditions on a staggered schedule, which induces confounding of the intervention effect by time. The stepped wedge design first appeared in the Gambia hepatitis study in the 1980s. However, the statistical model used for the design and analysis was not formally introduced until 2007 in an article by Hussey and Hughes. Since then, a variety of mixed-effects model extensions have been proposed for the design and analysis of these trials. In this article, we explore these extensions under a unified perspective. We provide a general model representation and regard various model extensions as alternative ways to characterize the secular trend, intervention effect, as well as sources of heterogeneity. We review the key model ingredients and clarify their implications for the design and analysis. The article serves as an entry point to the evolving statistical literatures on stepped wedge designs.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview

Hughes

Hemming

et al. 2020

Stat Methods Med Res

Self Cite

114

154

View full text Add to dashboard Cite

show abstract

“…In Web Appendixes E and F, we identified two messages. First, the new sample size formula can capture the variance inflation due to missing outcomes with assumptions on the marginal missingness proportion and the ICC of the missingness indicator (Turner et al., 2020). When the missingness is independent across individuals, assuming only the marginal missingness proportion is sufficient to apply those formulas.…”

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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“…Data imputation is widely used to deal with missing data in many areas such as medical research (Pedersen et al, 2017;Sullivan et al, 2018;Turner et al, 2019;Stavseth et al, 2019), organizational research (Newman, 2003;Fichman & Cummings, 2003;Newman, 2014) and educational research (Grund et al, 2018;Shi et al, 2019). The main objective of data imputation is to replace any missing data with estimated values to obtain a complete dataset.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Single and MICE Imputation Methods for Missing Values: A Simulation Study

Pauzi¹,

Wah²,

Deni³

et al. 2021

JST

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High quality data is essential in every field of research for valid research findings. The presence of missing data in a dataset is common and occurs for a variety of reasons such as incomplete responses, equipment malfunction and data entry error. Single and multiple data imputation methods have been developed for data imputation of missing values. This study investigated the performance of single imputation using mean and multiple imputation method using Multivariate Imputation by Chained Equations (MICE) via a simulation study. The MCAR which means missing completely at random were generated randomly for ten levels of missing rates (proportion of missing data): 5% to 50% for different sample sizes. Mean Square Error (MSE) was used to evaluate the performance of the imputation methods. Data imputation method depends on data types. Mean imputation is commonly used to impute missing values for continuous variable while MICE method can handle both continuous and categorical variables. The simulation results indicate that group mean imputation (GMI) performed better compared to overall mean imputation (OMI) and MICE with lowest value of MSE for all sample sizes and missing rates. The MSE of OMI, GMI, and MICE increases when missing rate increases. The MICE method has the lowest performance (i.e. highest MSE) when percentage of missing rates is more than 15%. Overall, GMI is more superior compared to OMI and MICE for all missing rates and sample size for MCAR mechanism. An application to a real dataset confirmed the findings of the simulation results. The findings of this study can provide knowledge to researchers and practitioners on which imputation method is more suitable when the data involves missing data.

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Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness

Cited by 11 publications

References 62 publications

Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview

Mixed-effects models for the design and analysis of stepped wedge cluster randomized trials: An overview

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

Comparison of Single and MICE Imputation Methods for Missing Values: A Simulation Study

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