simdata: Generation of data sets with survival outcome

In biospear: Biomarker Selection in Penalized Regression Models

Description

This function simulates a data set with survival outcome with given active biomarkers (prognostic and/or interacting with the treatment).

Usage

simdata(n, p, q.main, q.inter, prob.tt, m0, alpha.tt, beta.main,
  beta.inter, b.corr, b.corr.by, wei.shape, recr, fu, timefactor,
  active.main, active.inter)

simdataV(traindata, Nvalid)

Arguments

n	the sample size.
p	the number of biomarkers.
q.main	the number of true prognostic biomarkers.
q.inter	the number of true biomarkers interacting with the treatement.
prob.tt	the treatement assignement probability.
m0	the baseline median survival time.
alpha.tt	the effect of the treatment (in log-scale).
beta.main	the effect of the prognostic biomarkers (in log-scale).
beta.inter	the effect of the biomarkers interacting with the treatment (in log-scale).
b.corr	the correlation between biomarker blocks.
b.corr.by	the size of the blocks of correlated biomarkers.
wei.shape	the shape parameter of the Weibull distribution.
recr	the recruitment period duration.
fu	the follow-up period duration.
timefactor	the scale multiplicative factor for times (i.e. 1 = times in years).
active.main	the list of the prognostic biomarkers (not mandatory).
active.inter	the list of the biomarkers interacting with the treatment (not mandatory).
traindata	the training set returned by simdata, used to generate the new validation data set with the same characteristics.
Nvalid	the sample size of the new validation data set.

Details

The simdata function generates p Gaussian unit-variance (σ = 1) biomarkers including autoregressive correlation (σ_ij = b.corr^|i-j|) within b.corr.by-biomarker blocks. The number of active biomarkers and their effect sizes (in log-scale) can be specified using q.main and beta.main for the true prognostic biomarkers and using q.inter and beta.inter for the true treatment-effect modifiers. A total of n patients is generated and randomly assigned to the experimental (coded as +0.5, with probability prob.tt) and control treatment (coded as -0.5). The treatment effect is specified using alpha.tt (in log-scale). Survival times are generated using a Weibull with shape wei.shape (i.e. 1 = exponential distribution) and patient-specific scale depending on the baseline median survival time m0 and the biomarkers values of the patient. Censor status is generated by considering independant censoring from a U(fu, fu + recr) distribution, reflecting a trial with recr years of accrual and fu years of follow-up. Another data set with the same characteristics as the one generated by simdata can be obtained by using the simdataV function.

Value

A simulated data.frame object.

Author(s)

Nils Ternes, Federico Rotolo, and Stefan Michiels
Maintainer: Nils Ternes [e-mail]

Examples

  set.seed(123456)
  sdata <- simdata(
    n = 500, p = 100, q.main = 5, q.inter = 5,
    prob.tt = 0.5, alpha.tt = -0.5,
    beta.main = c(-0.5, -0.2), beta.inter = c(-0.7, -0.4),
    b.corr = 0.6, b.corr.by = 10,
    m0 = 5, wei.shape = 1, recr = 4, fu = 2,
    timefactor = 1,
    active.inter = c("bm003", "bm021", "bm044", "bm049", "bm097"))

  newdata <- simdataV(
    traindata = sdata,
    Nvalid = 500)

Example output

Loading required package: pkgconfig
Warning message:
In file(con, "r") : cannot open file '/proc/stat': Permission denied

biospear documentation built on May 2, 2019, 2:16 a.m.