The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The goal of CIMPLE is to offer a collection of methods involved with analyzing longitudinal EHR data with possibly informative observational time. These methods are grouped into two classes depending on the inferential task. One group focuses on estimating the effect of an exposure on a longitudinal biomarker while the other group assesses the impact of a longitudinal biomarker on time-to-diagnosis outcomes.
You can install the development version of CIMPLE from GitHub with:
# install.packages("pak")
pak::pak("jiacongD/CIMPLE")A very basic workflow:
library(CIMPLE)
time_var = "time"
id_var = "id"
outcome_var = "Y"
VPM_variables = c("Z", "X")
LM_fixedEffect_variables = c("Z", "X")
LM_randomEffect_variables = "Z"
# Run the standard LME model
fit_standardLME = long_est(
long_data = train_data,
method = "standard_LME",
id_var = id_var,
outcome_var = outcome_var,
LM_fixedEffect_variables = LM_fixedEffect_variables,
time = time_var,
LM_randomEffect_variables = LM_randomEffect_variables,
VPM_variables = VPM_variables
)
#> Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
#> unable to evaluate scaled gradient
#> Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
#> Model failed to converge: degenerate Hessian with 1 negative eigenvalues
# Return the coefficient estimates
fit_standardLME$beta_hat
#> (Intercept) Z X time
#> -2.18544419 -0.47282269 0.59100042 0.09982721These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.