Multiple imputation by chained equations for systematically and sporadically missing multilevel data

Resche‐Rigon, Matthieu; White, Ian R.

doi:10.1177/0962280216666564

Cited by 139 publications

(140 citation statements)

References 42 publications

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“…() who showed that JM‐FJ and JM‐MLMM produce biased estimation of the variance components while the FCS‐LMM‐het approach provided consistent estimates in the context of clustered data. Some of our theoretical results extend the results obtained by Resche‐Rigon and White () who considered an LMM with only a random intercept.…”

Section: Discussionsupporting

confidence: 88%

“…The jomo implementations for JM-MLMM-LN and JM-SMC allow a random covariance matrix and hence are denoted as JM-MLMM-LN-het and JM-SMC-het. Similar efforts have been made to extend both the FCS-LMM and FCS-LMM-het methods to impute categorical data using either generalized LMM (GLMM)-based MI methods (Resche-Rigon & White, 2016;Zhao & Yucel, 2009) or LN variables (FCS-LMM-LN and FCS-LMM-LN-het) (Enders, Keller, & Levy, 2017).…”

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confidence: 99%

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Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model

et al. 2020

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Multiple imputation (MI) is increasingly popular for handling multivariate missing data. Two general approaches are available in standard computer packages: MI based on the posterior distribution of incomplete variables under a multivariate (joint) model, and fully conditional specification (FCS), which imputes missing values using univariate conditional distributions for each incomplete variable given all the others, cycling iteratively through the univariate imputation models. In the context of longitudinal or clustered data, it is not clear whether these approaches result in consistent estimates of regression coefficient and variance component parameters when the analysis model of interest is a linear mixed effects model (LMM) that includes both random intercepts and slopes with either covariates or both covariates and outcome contain missing information. In the current paper, we compared the performance of seven different MI methods for handling missing values in longitudinal and clustered data in the context of fitting LMMs with both random intercepts and slopes. We study the theoretical compatibility between specific imputation models fitted under each of these approaches and the LMM, and also conduct simulation studies in both the longitudinal and clustered data settings. Simulations were motivated by analyses of the association between body mass index (BMI) and quality of life (QoL) in the Longitudinal Study of Australian Children (LSAC). Our findings showed that the relative performance of MI methods vary according to whether the incomplete covariate has fixed or random effects and whether there is missingnesss in the outcome variable. We showed that compatible imputation and analysis models resulted in consistent estimation of both regression parameters and variance components via simulation. We illustrate our findings with the analysis of LSAC data.

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Section: Discussionsupporting

confidence: 88%

mentioning

confidence: 99%

Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model

et al. 2020

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“…Alternatively, researchers can conceptualize this as a missing data problem, with some covariates partially or completely missing at some data-contributing sites, and use methods like multiple imputation by chained equations that appropriately account for between-site heterogeneity to handle the missing data. 68,69 Whenever possible, researchers should specify their primary analysis and perform sensitivity analyses to examine the robustness of their results.…”

Section: Database-specific Confounder Lists Vs Common Confounder Lismentioning

confidence: 99%

Analytic and Data Sharing Options in Real‐World Multidatabase Studies of Comparative Effectiveness and Safety of Medical Products

Toh

2020

Clin Pharma and Therapeutics

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A wide range of analytic and data sharing options are available in nonexperimental multidatabase studies designed to assess the real‐world benefits and risks of medical products. Researchers often consider six scientific domains when choosing among these options—study design, exposure type, outcome type, covariate summarization technique, covariate adjustment method, and data sharing approach. This article reviews available analytic and data sharing options and discusses key scientific and practical considerations when choosing among these options in multidatabase studies of comparative effectiveness and safety of medical products. The scientific considerations must be balanced against what the data‐contributing sites are able or willing to share. While pooling of person‐level data sets remains the most familiar and analytically flexible approach, newer analytic and data sharing approaches that share less granular summary‐level information may be equally valid and preferred in some multidatabase studies, especially when sharing of person‐level data is challenging or infeasible.

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“…Resulting from the extensive simulation studies, the proposed imputation approach also has good statistical properties in terms of bias and coverage rates. Moreover, our method, as opposed to recently proposed one‐stage imputation approach by Resche‐Rigon & White (), no longer requires (typically unreliable) estimates of standard errors around between‐study covariance parameters. Finally, it is freely accessible and is already implemented in the R package MICE (Van Buuren & Groothuis‐Oudshoorn, ).…”

Section: Discussionmentioning

confidence: 99%

“…Van Buuren () applied a Markov chain Monte Carlo (MCMC) method to draw

θ_{k}

. Resche‐Rigon & White () made a draw of

θ_{k}

using their large sample normal approximation (by applying the Fisher's transformation to the variance component). We, in the subsequent section, propose an (approximate) Bayesian approach to achieve step 2.…”

Section: Hierarchical MI Using Micementioning

confidence: 99%

Hierarchical imputation of systematically and sporadically missing data: An approximate Bayesian approach using chained equations

Jolani

2017

Biometrical J

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In health and medical sciences, multiple imputation (MI) is now becoming popular to obtain valid inferences in the presence of missing data. However, MI of clustered data such as multicenter studies and individual participant data meta-analysis requires advanced imputation routines that preserve the hierarchical structure of data. In clustered data, a specific challenge is the presence of systematically missing data, when a variable is completely missing in some clusters, and sporadically missing data, when it is partly missing in some clusters. Unfortunately, little is known about how to perform MI when both types of missing data occur simultaneously. We develop a new class of hierarchical imputation approach based on chained equations methodology that simultaneously imputes systematically and sporadically missing data while allowing for arbitrary patterns of missingness among them. Here, we use a random effect imputation model and adopt a simplification over fully Bayesian techniques such as Gibbs sampler to directly obtain draws of parameters within each step of the chained equations. We justify through theoretical arguments and extensive simulation studies that the proposed imputation methodology has good statistical properties in terms of bias and coverage rates of parameter estimates. An illustration is given in a case study with eight individual participant datasets.

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Multiple imputation by chained equations for systematically and sporadically missing multilevel data

Cited by 139 publications

References 42 publications

Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model

Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model

Analytic and Data Sharing Options in Real‐World Multidatabase Studies of Comparative Effectiveness and Safety of Medical Products

Hierarchical imputation of systematically and sporadically missing data: An approximate Bayesian approach using chained equations

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