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 has 20% of their values deleted independently of all other variables so the data became missing completely at random (Little and Rubin, 2002). Each simulated dataset was analysed in three ways. A Cox model was fit to the complete cases (
CC). Then two methods of multiple imputation using chained equations (van Buuren, Boshuizen, and Knook, 1999) were used. The
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. The
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.
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
Little, R.J.A., and D.B. Rubin. 2002. Statistical analysis with missing data. 2nd ed. Hoboken, NJ: Wiley doi: 10.1002/9781119013563
data("MIsim", package = "rsimsum")
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