Joint distribution properties of fully conditional specification under the normal linear model with normal inverse-gamma priors. [PDF]
Sci Rep, 2023 AbstractFully conditional specification (FCS) is a convenient and flexible multiple imputation approach. It specifies a sequence of simple regression models instead of a potential complex joint density for missing variables. However, FCS may not converge to a stationary distribution.
Cai M, van Buuren S, Vink G.
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Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model [PDF]
Statistical Methods in Medical Research, 2015 Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially observed covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g.
Jonathan W Bartlett +2 more
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On the use of the not‐at‐random fully conditional specification (NARFCS) procedure in practice [PDF]
Statistics in Medicine, 2018 The not‐at‐random fully conditional specification (NARFCS) procedure provides a flexible means for the imputation of multivariable missing data under missing‐not‐at‐random conditions. Recent work has outlined difficulties with eliciting the sensitivity parameters of the procedure from expert opinion due to their conditional nature.
Daniel Mark Tompsett +2 more
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Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study [PDF]
International Journal of Statistics in Medical Research, 2015 Missing data commonly occur in large epidemiologic studies. Ignoring incompleteness or handling the data inappropriately may bias study results, reduce power and efficiency, and alter important risk/benefit relationships. Standard ways of dealing with missing values, such as complete case analysis (CCA), are generally inappropriate due to the loss of ...
Liu Y, De A.
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Application of multiple imputation using the two-fold fully conditional specification algorithm in longitudinal clinical data. [PDF]
Stata J, 2014 Electronic health records of longitudinal clinical data are a valuable resource for health care research. One obstacle of using databases of health records in epidemiological analyses is that general practitioners mainly record data if they are clinically relevant.
Welch C, Bartlett J, Petersen I.
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Evaluation of two-fold fully conditional specification multiple imputation for longitudinal electronic health record data. [PDF]
Stat Med, 2014 Most implementations of multiple imputation (MI) of missing data are designed for simple rectangular data structures ignoring temporal ordering of data. Therefore, when applying MI to longitudinal data with intermittent patterns of missing data, some alternative strategies must be considered.
Welch CA +8 more
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Multiple Imputation for Missing Data: Fully Conditional Specification Versus Multivariate Normal Imputation [PDF]
American Journal of Epidemiology, 2010 Statistical analysis in epidemiologic studies is often hindered by missing data, and multiple imputation is increasingly being used to handle this problem. In a simulation study, the authors compared 2 methods for imputation that are widely available in standard software: fully conditional specification (FCS) or "chained equations" and multivariate ...
Katherine J Lee, John B Carlin
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Fully conditional specification in multivariate imputation
Journal of Statistical Computation and Simulation, 2006 The use of the Gibbs sampler with fully conditionally specified models, where the distribution of each variable given the other variables is the starting point, has become a popular method to create imputations in incomplete multivariate data. The theoretical weakness of this approach is that the specified conditional densities can be incompatible, and
Stef van Buuren
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Multiple imputation of multilevel data with single-level models: A fully conditional specification approach using adjusted group means. [PDF]
Behav Res MethodsAbstract Missing data are a common challenge in multilevel designs, and multiple imputation (MI) is often used for handling them. Past research has shown that multilevel MI provides an effective treatment of missing data, so long as the imputation model takes the multilevel structure and the intended analyses into account, and modern ...
Grund S, Lüdtke O, Robitzsch A.
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Multivariate Behavioral Research, 2018 Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and “reverse random coefficient” imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random within-cluster ...
Craig Enders, Han Du
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