additivePenal: Fit an Additive Frailty model using a semiparametric...

In frailtypack: Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints

Fit an Additive Frailty model using a semiparametric penalized likelihood estimation or a parametric estimation

Description

Fit an additive frailty model using a semiparametric penalized likelihood estimation or a parametric estimation. The main issue in a meta-analysis study is how to take into account the heterogeneity between trials and between the treatment effects across trials. Additive models are proportional hazard model with two correlated random trial effects that act either multiplicatively on the hazard function or in interaction with the treatment, which allows studying for instance meta-analysis or multicentric datasets. Right-censored data are allowed, but not the left-truncated data. A stratified analysis is possible (maximum number of strata = 2). This approach is different from the shared frailty models.

In an additive model, the hazard function for the j^th subject in the i^th trial with random trial effect u_i as well as the random treatment-by-trial interaction v_i is:

where \lambda₀(0) is the baseline hazard function, \beta_k the fixed effect associated to the covariate X_ijk (k=1,..,p), \beta₁ is the treatment effect and X_ij1 the treatment variable. \rho is the corresponding correlation coefficient for the two frailty terms.

Usage

additivePenal(formula, data, correlation = FALSE, recurrentAG =
FALSE, cross.validation = FALSE, n.knots, kappa, maxit = 350, hazard =
"Splines", nb.int, LIMparam = 1e-4, LIMlogl = 1e-4, LIMderiv = 1e-3,
print.times = TRUE)

Arguments

formula	a formula object, with the response on the left of a \sim operator, and the terms on the right. The response must be a survival object as returned by the 'Surv' function like in survival package. The slope() function is required. Interactions are possible using * or :.
data	a 'data.frame' with the variables used in 'formula'.
correlation	Logical value. Are the random effects correlated? If so, the correlation coefficient is estimated. The default is FALSE.
recurrentAG	Always FALSE for additive models (left-truncated data are not allowed).
cross.validation	Logical value. Is cross validation procedure used for estimating smoothing parameter in the penalized likelihood estimation? If so a search of the smoothing parameter using cross validation is done, with kappa as the seed. The cross validation is not implemented for two strata. The default is FALSE.
n.knots	integer giving the number of knots to use. Value required in the penalized likelihood estimation. It corresponds to the (n.knots+2) splines functions for the approximation of the hazard or the survival functions. Number of knots must be between 4 and 20. (See Note)
kappa	positive smoothing parameter in the penalized likelihood estimation. In a stratified additive model, this argument must be a vector with kappas for both strata. The coefficient kappa of the integral of the squared second derivative of hazard function in the fit. To obtain an initial value for kappa, a solution is to fit the corresponding shared frailty model using cross validation (see cross.validation). We advise the user to identify several possible tuning parameters, note their defaults and look at the sensitivity of the results to varying them. Value required. (See Note)
maxit	maximum number of iterations for the Marquardtt algorithm. Default is 350
hazard	Type of hazard functions: "Splines" for semiparametric hazard functions with the penalized likelihood estimation, "Piecewise-per" for piecewise constant hazards functions using percentile, "Piecewise-equi" for piecewise constant hazard functions using equidistant intervals, "Weibull" for parametric Weibull functions. Default is "Splines".
nb.int	Number of intervals (between 1 and 20) for the parametric hazard functions ("Piecewise-per", "Piecewise-equi").
LIMparam	Convergence threshold of the Marquardt algorithm for the parameters (see Details), 10^{-4} by default.
LIMlogl	Convergence threshold of the Marquardt algorithm for the log-likelihood (see Details), 10^{-4} by default.
LIMderiv	Convergence threshold of the Marquardt algorithm for the gradient (see Details), 10^{-3} by default.
print.times	a logical parameter to print iteration process. Default is TRUE.

Details

The estimated parameter are obtained by maximizing the penalized log-likelihood or by a simple log-likelihood (in the parametric case) using the robust Marquardtt algorithm (Marquardtt,1963). The parameters are initialized with values obtained with Cox proportional hazard model. The iterations are stopped when the difference between two consecutive loglikelhoods was small (<10^{-4}), the estimated coefficients were stable (consecutive values (<10^{-4}), and the gradient small enough (<10^{-3}). To be sure of having a positive function at all stages of the algorithm, the spline coefficients were reparametrized to be positive at each stage. The variance space of the two random effects is reduced, so the variances are positive, and the correlation coefficient values are constrained to be between -1 and 1. The marginal log-likelihood depends on integrations that are approximated by using the Laplace integration technique with a first order approximation. The smoothing parameter can be fixed or estimated by maximizing likelihood cross-validation criterion. The usual squared Wald statistic was modified to a mixture of two \chi^2 distribution to get significance test for the variance of the random effects.

INITIAL VALUES

The splines and the regression coefficients are initialized to 0.1. An adjusted Cox model is fitted, it provides new initial values for the splines coefficients and the regression coefficients. The variances of the frailties are initialized to 0.1. Then an additive frailty model with independent frailties is fitted. At last, an additive frailty model with correlated frailties is fitted.

Value

An additive model or more generally an object of class 'additivePenal'. Methods defined for 'additivePenal' objects are provided for print, plot and summary.

b	sequence of the corresponding estimation of the splines coefficients, the random effects variances and the regression coefficients.
call	The code used for fitting the model.
coef	the regression coefficients.
cov	covariance between the two frailty terms (\bold{cov}(u_i,v_i))
cross.Val	Logical value. Is cross validation procedure used for estimating the smoothing parameters in the penalized likelihood estimation?
correlation	Logical value. Are the random effects correlated?
DoF	degrees of freedom associated with the "kappa".
formula	the formula part of the code used for the model.
groups	the maximum number of groups used in the fit.
kappa	A vector with the smoothing parameters in the penalized likelihood estimation corresponding to each baseline function as components.
loglikPenal	the complete marginal penalized log-likelihood in the semiparametric case.
loglik	the marginal log-likelihood in the parametric case.
n	the number of observations used in the fit.
n.events	the number of events observed in the fit.
n.iter	number of iterations needed to converge.
n.knots	number of knots for estimating the baseline functions.
n.strat	number of stratum.
rho	the corresponding correlation coefficient for the two frailty terms.
sigma2	Variance for the random intercept (the random effect associated to the baseline hazard functions).
tau2	Variance for the random slope (the random effect associated to the treatment effect across trials).
varH	the variance matrix of all parameters before positivity constraint transformation (Sigma2, Tau2, the regression coefficients and the spline coefficients). Then after, the delta method is needed to obtain the estimated variance parameters.
varHIH	the robust estimation of the variance matrix of all parameters (Sigma2, Tau2, the regression coefficients and the spline coefficients).
varSigma2	The variance of the estimates of "sigma2".
varTau2	The variance of the estimates of "tau2".
varcov	Variance of the estimates of "cov".
x	matrix of times where both survival and hazard functions are estimated. By default seq(0,max(time),length=99), where time is the vector of survival times.
lam	array (dim=3) of hazard estimates and confidence bands.
surv	array (dim=3) of baseline survival estimates and confidence bands.
median	The value of the median survival and its confidence bands. If there are two stratas or more, the first value corresponds to the value for the first strata, etc.
type.of.hazard	Type of hazard functions (0:"Splines", "1:Piecewise", "2:Weibull").
type.of.Piecewise	Type of Piecewise hazard functions (1:"percentile", 0:"equidistant").
nbintervR	Number of intervals (between 1 and 20) for the parametric hazard functions ("Piecewise-per", "Piecewise-equi").
npar	number of parameters.
nvar	number of explanatory variables.
noVar	indicator of explanatory variable.
LCV	the approximated likelihood cross-validation criterion in the semiparametric case (with H minus the converged Hessian matrix, and l(.) the full log-likelihood). LCV=\frac{1}{n}(trace(H^{-1}_{pl}H) - l(.))
AIC	the Akaike information Criterion for the parametric case. AIC=\frac{1}{n}(np - l(.))
n.knots.temp	initial value for the number of knots.
shape.weib	shape parameter for the Weibull hazard function.
scale.weib	scale parameter for the Weibull hazard function.
martingale.res	martingale residuals for each cluster.
frailty.pred	empirical Bayes prediction of the first frailty term.
frailty.pred2	empirical Bayes prediction of the second frailty term.
linear.pred	linear predictor: uses simply "Beta'X + u_i + v_i * X_1" in the additive Frailty models.
global_chisq	a vector with the values of each multivariate Wald test.
dof_chisq	a vector with the degree of freedom for each multivariate Wald test.
global_chisq.test	a binary variable equals to 0 when no multivariate Wald is given, 1 otherwise.
p.global_chisq	a vector with the p_values for each global multivariate Wald test.
names.factor	Names of the "as.factor" variables.
Xlevels	vector of the values that factor might have taken.
contrasts	type of contrast for factor variable.
beta_p.value	p-values of the Wald test for the estimated regression coefficients.

Note

"kappa" and "n.knots" are the arguments that the user have to change if the fitted model does not converge. "n.knots" takes integer values between 4 and 20. But with n.knots=20, the model would take a long time to converge. So, usually, begin first with n.knots=7, and increase it step by step until it converges. "kappa" only takes positive values. So, choose a value for kappa (for instance 10000), and if it does not converge, multiply or divide this value by 10 or 5 until it converges.

References

V. Rondeau, Y. Mazroui and J. R. Gonzalez (2012). Frailtypack: An R package for the analysis of correlated survival data with frailty models using penalized likelihood estimation or parametric estimation. Journal of Statistical Software 47, 1-28.

V. Rondeau, S. Michiels, B. Liquet, and J. P. Pignon (2008). Investigating trial and treatment heterogeneity in an individual patient data meta-analysis of survival data by mean of the maximum penalized likelihood approach. Statistics in Medecine, 27, 1894-1910.

See Also

slope

Examples

###--- Additive model with 1 covariate ---###

data(dataAdditive)

modAdd <- additivePenal(Surv(t1,t2,event)~cluster(group)+
var1+slope(var1),correlation=TRUE,data=dataAdditive,
n.knots=8,kappa=10000)

#-- Var1 is boolean as a treatment variable

frailtypack documentation built on Oct. 20, 2024, 1:08 a.m.