Description Usage Arguments Details Value Author(s) References Examples
The function surrosurv
fits (all or a subset of) statistical
models to evaluate a surrogate endpoint S for a given true endpoint T,
using individual data from a meta-analysis of randomized controlled trials.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | surrosurv(data,
models = c('Clayton', 'Plackett', 'Hougaard',
'Poisson I', 'Poisson T', 'Poisson TI', 'Poisson TIa'),
intWidth = NULL, nInts = 8,
cop.OPTIMIZER = "bobyqa",
poi.OPTIMIZER = "bobyqa",
verbose = TRUE,
twoStage = FALSE,
keep.data = TRUE)
## S3 method for class 'surrosurv'
predict(object, models = names(object), exact.models, ...)
## S3 method for class 'surrosurv'
print(x, silent = FALSE,
digits = 2, na.print = "-.--", ...)
## S3 method for class 'predictSurrosurv'
print(x, n = 6, ...)
## S3 method for class 'surrosurv'
plot(x, ...)
## S3 method for class 'predictSurrosurv'
plot(x, models = names(x), exact.models,
pred.ints = TRUE,
show.ste = TRUE,
surro.stats = TRUE,
xlab, ylab,
xlim, ylim, mfrow, main, ...)
|
data |
A data.frame with columns
|
models |
For |
exact.models |
If |
intWidth |
the width of time intervals for data Poissonization (see poissonize) |
nInts |
the number of time intervals for data Poissonization (see poissonize) |
cop.OPTIMIZER |
the optimizer for copula models (see |
poi.OPTIMIZER |
the optimizer for Poisson models (see |
verbose |
should the function print out the model being fitted |
twoStage |
should the parameters of the baseline hazard functions fixed to their marginal estimates (Shih and Louis, 1995) |
keep.data |
should the data object be kept as attribute of the returned results?
(this is needed for |
x, object |
The fitted models, an object of class |
silent |
Should the results be return for storing without printing them? |
digits, na.print, xlab, ylab, xlim, ylim, main, ... |
other parameters for |
mfrow |
the number of rows and columns for displaying the plots
(see |
n |
the number of rows to print |
pred.ints |
Should the prediction intervals be plotted? |
show.ste |
Should the surrogate threshold effect be showed? |
surro.stats |
Should the surrogacy statistics be showed? |
Three copula models can be fit: Clayton (1978), Plackett (1965), and Hougaard (1986). For all of them the linear regression at the second step is computed both via simple LS regression and via a linear model adjusted for measurement error of the log-hazard ratios estimated at the first step. This adjusted model is the one described by Burzykowski et al. (2001), which relies on the results by van Houwelingen et al. (2002).
The moxed Poisson models that can be fit are used to estimate parameters of mixed proportional hazard models, as described for instance by Crowther et al (2014). The statistical details are provided in Rotolo et al (WP).
The function predict()
returns the estimated values
of the log-hazard ratios on the true and the surrogate endpoints.
The list of the prediction functions (for all the models
)
is available as attr(predict.surrosurv(...), 'predf')
.
The fitted models, an object of class surrosurv
.
Federico Rotolo [aut, cre], Xavier Paoletti [ctr], Marc Buyse [ctr], Tomasz Burzykowski [ctr], Stefan Michiels [ctr]
Burzykowski T, Molenberghs G, Buyse M et al. Validation of surrogate end points in multiple randomized clinical trials with failure time end points. Journal of the Royal Statistical Society C 2001; 50:405–422. doi: 10.1111/1467-9876.00244
Clayton DG. A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika 1978; 65:141–151. doi: 10.1093/biomet/65.1.141
Crowther MJ, Riley RD, Staessen JA, Wang J, Gueyffier F, Lambert PC. Individual patient data meta-analysis of survival data using Poisson regression models. BMC Medical Research Methodology 2012; 12:34. doi: 10.1186/1471-2288-12-34.
Gasparrini A, Armstrong B, Kenward MG. Multivariate meta-analysis for non-linear and other multi-parameter associations. Statistics in Medicine 2012; 31:3821–39. doi: 10.1002/sim.5471
Hougaard P. A class of multivariate failure time distributions. Biometrika 1986; 73:671–678. doi: 10.1093/biomet/73.3.671
Plackett RL. A class of bivariate distributions. Journal of the America Statistical Association 1965; 60:516–522. doi: 10.1080/01621459.1965.10480807
Rotolo F, Paoletti X, Burzykowski T, Buyse M, Michiels S. Evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials. A Poisson approach. Working paper.
Shih JH, Louis TA. Inferences on the Association Parameter in Copula Models for Bivariate Survival Data. Biometrics 1995; 51:1384–1399. doi: 10.2307/2533269
van Houwelingen HC, Arends LR, Stijnen T. Advanced methods in meta-analysis: multivariate approach and meta-regression. Statistics in Medicine 2002; 21:589–624. doi: 10.1002/sim.1040
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | set.seed(150)
data <- simData.re(N = 20, ni = 250,
R2 = 0.8, kTau = 0.4,
alpha = log(0.95), beta = log(0.85),
censorA = 15 * 365.25)
library(survival)
par(mfrow = 1:2)
plot(survfit(Surv(timeS, statusS) ~ trt, data = data), lty = 1:2,
xscale = 365.25, main = 'Progression-Free Survival\n(S)', col = 2)
plot(survfit(Surv(timeT, statusT) ~ trt, data = data), lty = 1:2,
xscale = 365.25, main = 'Overall Survival\n(T)')
# Long computation time!
surrores <- surrosurv(data, verbose = TRUE)
convergence(surrores)
surrores
# Advanced GASTRIC data
# Long computation time!
data('gastadv')
allSurroRes <- surrosurv(gastadv, c('Clayton', 'Poisson'), verbose = TRUE)
convergence(allSurroRes)
allSurroRes
predict(allSurroRes)
plot(allSurroRes)
|
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