A dataset from a simulation study comparing different ways to handle missing covariates when fitting a Cox model (White and Royston, 2009).
One thousand datasets were simulated, each containing normally distributed covariates x and z and time-to-event outcome.
Both covariates have 20\
Each simulated dataset was analysed in three ways.
A Cox model was fit to the complete cases (
Then two methods of multiple imputation using chained equations (van Buuren, Boshuizen, and Knook, 1999) were used.
MI_LOGT method multiply imputes the missing values of x and z with the outcome included as \log (t) and d, where t is the survival time and d is the event indicator.
MI_T method is the same except that \log (t) is replaced by t in the imputation model.
The results are stored in long format.
A data frame with 3,000 rows and 4 variables:
dataset Simulated dataset number.
method Method used (
b Point estimate.
se Standard error of the point estimate.
An object of class
tbl_df (inherits from
data.frame) with 3000 rows and 5 columns.
MIsim2 is a version of the same dataset with the
method column split into two columns,
White, I.R., and P. Royston. 2009. Imputing missing covariate values for the Cox model. Statistics in Medicine 28(15):1982-1998 doi: 10.1002/sim.3618
data("MIsim", package = "rsimsum") data("MIsim2", package = "rsimsum")
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