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With ICE the imputation model of a single variable uses all the other variables as predictors by appropriate regression models (i.e. linear, logistic or multinomial if imputed variable is continuous, dichotomous or categorical).
Otherwise, we shall impute missing summary scale and subscale scores by appropriate regression models from all available values of that score at other data points and the allocated treatment group.
With this procedure, the imputation model of a single missing variable used the information from all the other variables considered as predictors by appropriate regression models (i.e. linear or logistic if variable to be imputed was continuous or dichotomous, respectively).
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The material will be checked for any inequality in the distribution of important prognostic variables between the two arms, and if such are present, they will be adjusted for by including these in appropriate regression models.
We provide a general framework for association testing with rare variants by combining mutation information across multiple variant sites within a gene and relating the enriched genetic information to disease phenotypes through appropriate regression models.
Appropriate regression models will be employed controlling for co-variants.
Multivariate analysis of the study outcomes was conducted using appropriate regression models.
The secondary outcomes will be analysed using appropriate regression models to compare the four treatment groups.
In case of need for multivariate analysis appropriate regression models e.g. logistic regression model, Cox proportional hazard model will be adopted.
Therefore, the conditional expectation of the binary random number given a set of covariates could be modelled by an appropriate regression model for binary data, for example using a generalized linear model [ 33].
It can be accomplished either via appropriate regression modeling or, alternatively, by conducting a matched pairs study.
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