Description Usage Arguments Details Value Author(s) References Examples
Data are generated from a mixed proportional hazard model, a Clayton copula model (Burzykowski and Cortinas Abrahantes, 2005), or a mixture of half-normal and exponential random variables (Shi et al., 2011).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | simData.re(R2 = 0.6, N = 30, ni = 200,
nifix = TRUE, gammaWei = c(1, 1), censorT, censorA,
kTau= 0.6, baseCorr = 0.5, baseVars = c(0.2, 0.2),
alpha = 0, beta = 0,
alphaVar = 0.1, betaVar = 0.1,
mstS = 4 * 365.25, mstT = 8 * 365.25)
simData.cc(R2 = 0.6, N = 30, ni = 200,
nifix = TRUE, gammaWei = c(1, 1), censorT, censorA,
kTau= 0.6, baseCorr = 0.5, baseVars = c(0.2, 0.2),
alpha = 0, beta = 0,
alphaVar = 0.1, betaVar = 0.1,
mstS = 4 * 365.25, mstT = 8 * 365.25)
simData.mx(R2 = 0.6, N = 30, ni = 200,
nifix = TRUE, gammaWei = c(1, 1), censorT, censorA,
indCorr = TRUE, baseCorr = 0.5, baseVars = c(0.2, 0.2),
alpha = 0, beta = 0,
alphaVar = 0.1, betaVar = 0.1,
mstS = 4 * 365.25, mstT = 8 * 365.25)
|
R2 |
The desired trial-level surrogacy R^2 |
N |
The number of trials |
ni |
The (fixed or average) number of patients per trial |
nifix |
Should all trials have the same size (if |
gammaWei |
The shape parameter(s) of the Weibull distributions. Either one or two values. If one value is provided, it is used for both endpoints |
censorT |
censoring rate for the true endpoint T (before adding administrative censoring) |
censorA |
administrative censoring at time censorA |
kTau |
The desired individual-level dependence between S and T (Kendall's tau) |
indCorr |
Should S and T be correlated or not? (for |
baseCorr |
correlation between baseline hazards (rho_basehaz) |
baseVars |
variances of baseline random effects (S and T) |
alpha |
average treatment effect on S |
beta |
average treatment effect on T |
alphaVar |
variance of a_i (theta_a^2) |
betaVar |
variance of b_i (theta_b^2) |
mstS |
median survival time for S in the control arm |
mstT |
median survival time for T in the control arm |
The function simData.re
generates data from a proportional hazard model
with random effects at individual level and
random effects and random treatment effects at trial level.
Individual dependence can be tuned in terms of Kendall's tau
(kTau
).
The function simData.cc
generates data from a Copula function
as shown by Burzykowski and Cortinas Abrahantes (2005).
Individual dependence can be tuned in terms of Kendall's tau
(kTau
).
The function simData.mx
implements the simulation method by Shi et al. (2011).
This model is based on a mixture of half-normal and exponential random variables.
Under this model, individual dependence can be induced by using the same
half-normal random variable for S and T.
This is obtained by setting indCorr = TRUE
,
but the amount of correlation is not dependent on a single parameter.
A data.frame with columns
trialref |
the trial reference |
trt |
the treatment arm (-0.5 or 0.5) |
id |
the patient id |
timeT |
the value of the true endpoint T |
statusT |
the censoring/event (0/1) indicator of the true endpoint T |
timeS |
the value of the surrogate endpoint S |
statusS |
the censoring/event (0/1) indicator of the surrogate endpoint S |
Federico Rotolo [aut, cre], Xavier Paoletti [ctr], Marc Buyse [ctr], Tomasz Burzykowski [ctr], Stefan Michiels [ctr]
Burzykowski T, Cortinas Abrahantes J (2005). Validation in the case of two failure-time endpoints. In The Evaluation of Surrogate Endpoints (pp. 163-194). Springer, New York.
Shi Q, Renfro LA, Bot BM, Burzykowski T, Buyse M, Sargent DJ. Comparative assessment of trial-level surrogacy measures for candidate time-to-event surrogate endpoints in clinical trials. Computational Statistics & Data Analysis 2011; 55: 2748–2757.
1 2 3 4 | set.seed(1)
simData.re(N = 2, ni = 5)
simData.cc(N = 2, ni = 5)
simData.mx(N = 2, ni = 5)
|
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