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.
nlpA 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.
Cox D.R. 1972. Regression models and life-tables. Journal of the Royal Statistical Society, Series B (Methodological) 34(2):187-220. tools:::Rd_expr_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 tools:::Rd_expr_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 tools:::Rd_expr_doi("10.1186/1471-2288-14-129")
data("nlp", package = "rsimsum")
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