nlp: Example of a simulation study on survival modelling

In rsimsum: Analysis of Simulation Studies Including Monte Carlo Error

Example of a simulation study on survival modelling

Description

A dataset from a simulation study with 150 data-generating mechanisms, useful to illustrate nested loop plots. This simulation study aims to compare the Cox model and flexible parametric models in a variety of scenarios: different baseline hazard functions, sample size, and varying amount of heterogeneity unaccounted for in the model (simulated as white noise with a given variance). A Cox model and a Royston-Parmar model with 5 degrees of freedom are fit to each replication.

Usage

nlp

Format

A data frame with 30,000 rows and 10 variables:

• dgm Data-generating mechanism, 1 to 150.

• i Simulated dataset number.

• model Method used, with 1 the Cox model and 2 the RP(5) model.

• b Point estimate for the log-hazard ratio.

• se Standard error of the point estimate.

• baseline Baseline hazard function of the simulated dataset.

• ss Sample size of the simulated dataset.

• esigma Standard deviation of the white noise.

• pars (Ancillary) Parameters of the baseline hazard function.

Note

Further details on this simulation study can be found in the R script used to generate this dataset, available on GitHub: https://github.com/ellessenne/rsimsum/blob/master/data-raw/nlp-data.R

References

Cox D.R. 1972. Regression models and life-tables. Journal of the Royal Statistical Society, Series B (Methodological) 34(2):187-220. doi: 10.1007/978-1-4612-4380-9_37

Royston, P. and Parmar, M.K. 2002. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine 21(15):2175-2197 doi: 10.1002/sim.1203

Rücker, G. and Schwarzer, G. 2014. Presenting simulation results in a nested loop plot. BMC Medical Research Methodology 14:129 doi: 10.1186/1471-2288-14-129

Examples

data("nlp", package = "rsimsum")

rsimsum documentation built on Aug. 17, 2022, 5:07 p.m.