relhaz: 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 assessing the impact of misspecifying the baseline hazard in survival models on regression coefficients. One thousand datasets were simulated, each containing a binary treatment variable with a log-hazard ratio of -0.50. Survival data was simulated for two different sample sizes, 50 and 250 individuals, and under two different baseline hazard functions, exponential and Weibull. Consequently, a Cox model (Cox, 1972), a fully parametric exponential model, and a Royston-Parmar (Royston and Parmar, 2002) model with two degrees of freedom were fit to each simulated dataset. See vignette("B-relhaz", package = "rsimsum") for more information.

Usage

relhaz

Format

A data frame with 1,200 rows and 6 variables:

• dataset Simulated dataset number.

• n Sample size of the simulate dataset.

• baseline Baseline hazard function of the simulated dataset.

• model Method used (Cox, Exp, or RP(2)).

• theta Point estimate for the log-hazard ratio.

• se Standard error of the point estimate.

References

Cox D.R. 1972. Regression models and life-tables. Journal of the Royal Statistical Society, Series B (Methodological) 34(2):187-220. \Sexpr[results=rd]{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 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.1203")}

Examples

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

rsimsum documentation built on May 29, 2024, 2:18 a.m.