Fit semi-parametric Cox or additive hazards regression models with time- fixed covariates of any type and multiple continuous time-dependent covariates subject to missing data, measurement error or simply observed in discrete time: all these issues are handled with the two-stage Multiple Imputation for Joint Modeling (MIJM) approach developed by Moreno-Betancur et al. (2017). The package also provides easy-to-use implementations of an unadapted version (unMIJM); a simple two-stage approach (simple2S), based on single imputation of the continuous markers from linear mixed models; and the last observation carried forward (LOCF) approach for time-dependent covariates of any type, which is the traditional method to incoporate these in hazard models. Data simulation functions also included.
|Author||Margarita Moreno-Betancur [cre, aut], Samuel L Brilleman [ctb]|
|Maintainer||Margarita Moreno-Betancur <[email protected]>|
|License||GPL (>= 3)|
|Package repository||View on GitHub|
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