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