27 33 The rpart model handles missing values by using surrogate splits: when a value for a variable is missing, and that variable needs to be used for a split, an alternative variable with a similar splitting property is used to determine the direction of the split.
The proposed approach makes use of hybrid models to handle missing mechanistic knowledge and an optimal experimental design technique was applied to generate the most discriminative data.
Linear mixed effects models can handle missing data based on the untestable assumption that unobserved values are missing at random, though efforts were made to minimize missing data during the trial.
Five imputed datasets and their pooled estimates were generated using a fully conditional specified model to handle missing values.
Of those who reported some missing data, we will calculate the proportion of trials that carried out complete case analysis, single imputation, MI, GEE or a mixed model to handle missing data in the primary analysis.
Canny [11] also proposes a new solution based on a probabilistic factor analysis model that can handle missing data, and provides privacy through a peer-to-peer protocol.
The BBC model can easily handle missing data via Monte Carlo imputation and has the potential to be extended to integrated study of gene transcription networks.
Because it is a full Bayesian model, the BBC can handle missing data extremely easily, and can also incorporate likelihood-based criterion, such as AIC, BIC, maximum likelihood, Bayes factors, etc., for model evaluations and comparisons.
These models have the ability to handle missing data and therefore the intention-to-treat principle was adhered to [ 27].
In order to adhere to the intention-to-treat principle, the outcome variables will be analysed using mixed effects models, given their ability to handle missing data.
