jointRecCompet: Competing Joint Frailty Model: A single type of recurrent...
In frailtypack: Shared, Joint (Generalized) Frailty Models; Surrogate Endpoints
jointRecCompet R Documentation
Competing Joint Frailty Model: A single type of recurrent event and two
terminal events.
Description
Fit a joint competing frailty model for a single recurrent event
and two terminal events defined as,
\text {Recurrent event:} \quad r_{i j}\left(t \mid w_i,
\boldsymbol{X}_{r, i j}\right)=r_0(t) \exp \left(\boldsymbol{X}_{r, i j} \boldsymbol{\beta}_r+w_i\right)
\text{First terminal event:} \quad \lambda_{1, i}\left(t \mid w_i,
\boldsymbol{X}_{1 i}\right)=\lambda_{1, 0}(t) \exp \left(\boldsymbol{X}_{1, i} \boldsymbol{\beta}_1+\alpha_1 w_i\right)
\text{Second terminal event:} \quad \lambda_{2, i}\left(t \mid w_i,
\boldsymbol{X}_{2, i}\right)=\lambda_{2, 0}(t) \exp \left(\boldsymbol{X}_{2, i} \boldsymbol{\beta}_2+\alpha_2 w_i\right).
where \omega_i \sim \mathcal{N}(0,\theta) is the frailty term and \boldsymbol{X}_{r, i j},\boldsymbol{X}_{1, i} and
\boldsymbol{X}_{2, i} are vectors of baseline covariates (possibly the same). The parameters \alpha_1 and \alpha_2
are power parameters.
Usage
jointRecCompet(formula,
formula.terminalEvent = NULL,
formula.terminalEvent2 = NULL,
data,
initialize = TRUE,
recurrentAG = FALSE,
maxit = 350,
hazard = "Weibull",
n.knots=7,
kappa = rep(10, 3),
crossVal=FALSE,
constraint.frailty = "squared",
GHpoints = 32,
tolerance = rep(10^-3, 3),
init.hazard = NULL,
init.Sigma = 0.5,
init.Alpha1 = 0.1,
init.Alpha2 = -0.1,
init.B = NULL)
Arguments
formula
a formula object, with the response for the first recurrent
event on the left of a \sim operator, and the terms on the right.
The response must be in the format Surv(t0, t1, recurrentevent) for a calendar-time
specification where t0 is the start time for an at-risk period for the recurrent event,
t1 is the end time for an at-risk period for the recurrent event, and
recurrentevent is a numeric indicator for whether an event was observed (1)
or was censored (2).
In a gap-time setting, an object of the format Surv(t, recurrentevent) should be used instead.
Note that to not be confused with a left-truncation setting, when using a calendar-time specification
argument recurrentAG should be set to TRUE.
formula.terminalEvent
a formula object,
empty on the left of a \sim operator,
and the terms on the right. Leave the formula at
the default value (NULL) for a model with no variables.
formula.terminalEvent2
a formula object,
empty on the left of a \sim operator,
and the terms on the right.Leave the formula at
the default value (NULL) for a model with no variables.
data
a 'data.frame' with the variables used in 'formula',
'formula.terminalEvent', and 'formula.terminalEvent2'.
initialize
Logical value to internally initialize regression coefficients and
baseline hazard functions parameters using simpler models from frailtypack.
When initialization is requested, the
program first fits two joint frailty models for the recurrent
events and each terminal event.
When FALSE, parameters are initialized via the arguments
init.hazard, init.Sigma, init.Alpha1, init.Alpha2, init.B.
recurrentAG
Logical value. Is Andersen-Gill model fitted?
If so indicates that recurrent event times with the counting
process approach of Andersen and Gill is used. This formulation can be
used for dealing with time-dependent covariates. The default is FALSE.
maxit
maximum number of iterations for the Marquardt algorithm.
Default is 350.
hazard
Type of hazard functions. Available options are "Weibull"
for parametric Weibull function, "Splines" for semiparametric
hazard functions using equidistant intervals or "Splines-per" for
percentile intervals. Default is "Weibull".
n.knots
In the case of splines hazard functions, number of knots to be used
in the splines basis. This number should be between 4 and 20. Default is 7.
kappa
In the case of splines hazard functions, a vector of size 3 containing
the values of the smoothing parameters to be used for each baseline hazard function.
Default value is 10 for each function.
crossVal
In the case of splines hazard functions, indicates how
the smoothing parameters are chosen. If set to "TRUE" then those parameters
are chosen automatically using cross-validation on reduced models for each
baseline hazard function. If set to "FALSE" then the parameters are those provided
by the argument kappa. If set to "TRUE" then the argument kappa is
ignored. Default is "TRUE".
constraint.frailty
Type of positivity constraint used for the variance of
of the random effect in the likelihood. Possible values are 'squared' or 'exponential'.
Default is 'squared'. See Details.
GHpoints
Integer. Number of nodes for Gauss-Hermite integration
to marginalize random effects/frailties. Default is 32.
tolerance
Numeric, length 3. Optimizer's tolerance for (1) successive change
in parameter values, (2) log likelihood, and (3) score, respectively.
init.hazard
Numeric. Initialization values for hazard parameters.
If a weibull model is used, the order is:
shapeR, scaleR, shapeTerminal1, scaleTerminal1, shapeTerminal2, scaleTerminal2.
init.Sigma
Numeric,. Initialization value for the standard deviation of the
normally-distributed random effects.
init.Alpha1
Numeric. Initialization value for the parameter alpha that
links the hazard function of the recurrent event to the first terminal event.
init.Alpha2
Numeric. Initialization value for the parameter alpha that
links the hazard function of the recurrent event to the second terminal event.
init.B
Numeric vector of the same length and order
as the three covariate vectors for the recurrent, terminal1,
and terminal2 events (in that order).
Details
Right-censored data are allowed.
Left-truncated data and stratified analysis are not possible.
Prediction options are not yet available.
The constraint.frailty argument defines the positivity constraint
used for the frailty variance in the likelihood. By default it uses the square
so that the absolute value of the parameter is the standard deviation of the frailty
(i.e \theta^2 = \beta^2).
The other parametrization uses the square of the exponential for the variance
so that the parameter is the logarithm of the standard deviation (\theta^2 = (\exp{\beta})^2).
For others parameters in the model needing a positivity constraint (parameters related to the
baseline hazard functions), the parametrization used is the exponential squared.
Value
Parameters estimates of a competing joint frailty model, more
generally a 'jointRecCompet' object. Methods defined for 'jointRecCompet' objects
are provided for print, plot and summary. The following components are
included in a 'jointRecCompet' object.
summary.table
A table describing the estimate, standard error,
confidence interval, and pvalues for each of the parameters in the model.
controls
A vector of named control parameters
k0
For splines baseline hazard functions, vector of penalization terms.
noVarEvent
A vector containing for each event type if there is no covariate used
in the model.
np
Total number of parameters
b
Vector containing the estimated coefficients of the model before any positivity constraint.
The values are in order: the coefficients associated with the baseline hazard functions
(either the splines or the shape and scale parameters for Weibull hazard),
the random effect variance, the coefficients of the frailty (\alpha_1 and \alpha_2)
and the regression coefficients.
H_hessOut
Covariance matrix of the estimated parameters
HIHOut
Covariance matrix of the estimated parameters for the penalized likelihood
in the case of Splines baseline hazard functions.
LCV
The approximated likelihood cross-validation criterion in the spline case
critCV
Convergence criteria
x1
Vector of times for which the hazard function of
the recurrent event is estimated.
By default seq(0,max(time),length=99),
where time is the vector of survival times.
lam1
Matrix of hazard estimates and confidence bands for the recurrent
event.
xSu1
Vector of times for the survival function of
the recurrent event.
surv1
Matrix of baseline survival
estimates and confidence bands for recurrent event.
x2
Vector of times for the first terminal event (see x1 value).
lam2
Matrix of hazard estimates and confidence bands for the
first terminal event.
xSu2
Vector of times for the survival function of the first terminal event.
surv2
Vector of the survival function of the first terminal event evaluated at xSu2.
x3
Vector of times for the second terminal event (see x1 value).
lam3
Matrix of hazard estimates and confidence bands for the
second terminal event.
xSu3
Vector of times for the survival function of the second terminal event.
surv3
Vector of the survival function of the second terminal event evaluated at xSu3.
ni
Number of iterations needed to converge.
constraintfrailty
Positivity constraint used for the variance of the random effect
ziOut1
In the spline case, vector of knots used in the spline basis for the recurrent event
ziOutdc
In the spline case, vector of knots used in the spline basis for the terminal events
ghnodes
Nodes used for the Gauss-Hermite quadrature.
ghweights
Weights used for the Gauss-Hermite quadrature.
tolerance
Numeric, length 3. Optimizer's tolerance for (1) successive change
in parameter values, (2) log likelihood, and (3) score, respectively.
call
Call of the function.
loglikPenal
Estimated penalized log-likelihood in the spline case
logLik
Estimated log-likelihood in the Weibull case
AIC
For the Weibull case, Akaike Information criterion
n
Total number of subjects
nevts
Number of events for each event type.
See Also
terminal
Examples
set.seed(1)
data=simulatejointRecCompet(n=500,
par0=c(shapeR = 1.5, scaleR = 10,
shapeM = 1.75, scaleM = 16, shapeD = 1.75, scaleD = 16, sigma = 0.5,
alphaM = 1, alphaD = 1, betaR = -0.5, betaM = -0.5, betaD = 0) )
mod <-jointRecCompet(formula = Surv(tstart, tstop, event)~cluster(id)+treatment+
terminal(terminal1)+terminal2(terminal2),
formula.terminalEvent = ~treatment,
formula.terminalEvent2 = ~treatment,
data = data,
recurrentAG = TRUE,
initialize = TRUE,
n.knots=7,
crossVal=TRUE,
hazard = "Splines",
maxit = 350)
#This example uses an extract of 500 patients of the REDUCE trial
data(reduce)
mod_reduce <-jointRecCompet(formula = Surv(t.start,t.stop, del)~cluster(id)+
treatment+terminal(death)+terminal2(discharge),
formula.terminalEvent = ~treatment,
formula.terminalEvent2 = ~treatment,
data = reduce,
initialize = TRUE,
recurrentAG = TRUE,
hazard = "Weibull",
constraint.frailty = "exponential",
maxit = 350)
print(mod_reduce)
frailtypack documentation built on June 27, 2024, 5:08 p.m.
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| jointRecCompet | R Documentation |
Competing Joint Frailty Model: A single type of recurrent event and two terminal events.
Description
Fit a joint competing frailty model for a single recurrent event and two terminal events defined as,
\text {Recurrent event:} \quad r_{i j}\left(t \mid w_i,
\boldsymbol{X}_{r, i j}\right)=r_0(t) \exp \left(\boldsymbol{X}_{r, i j} \boldsymbol{\beta}_r+w_i\right)
\text{First terminal event:} \quad \lambda_{1, i}\left(t \mid w_i,
\boldsymbol{X}_{1 i}\right)=\lambda_{1, 0}(t) \exp \left(\boldsymbol{X}_{1, i} \boldsymbol{\beta}_1+\alpha_1 w_i\right)
\text{Second terminal event:} \quad \lambda_{2, i}\left(t \mid w_i,
\boldsymbol{X}_{2, i}\right)=\lambda_{2, 0}(t) \exp \left(\boldsymbol{X}_{2, i} \boldsymbol{\beta}_2+\alpha_2 w_i\right).
where \omega_i \sim \mathcal{N}(0,\theta) is the frailty term and \boldsymbol{X}_{r, i j},\boldsymbol{X}_{1, i} and
\boldsymbol{X}_{2, i} are vectors of baseline covariates (possibly the same). The parameters \alpha_1 and \alpha_2
are power parameters.
Usage
jointRecCompet(formula,
formula.terminalEvent = NULL,
formula.terminalEvent2 = NULL,
data,
initialize = TRUE,
recurrentAG = FALSE,
maxit = 350,
hazard = "Weibull",
n.knots=7,
kappa = rep(10, 3),
crossVal=FALSE,
constraint.frailty = "squared",
GHpoints = 32,
tolerance = rep(10^-3, 3),
init.hazard = NULL,
init.Sigma = 0.5,
init.Alpha1 = 0.1,
init.Alpha2 = -0.1,
init.B = NULL)
Arguments
formula |
a formula object, with the response for the first recurrent
event on the left of a |
formula.terminalEvent |
a formula object,
empty on the left of a |
formula.terminalEvent2 |
a formula object,
empty on the left of a |
data |
a 'data.frame' with the variables used in 'formula', 'formula.terminalEvent', and 'formula.terminalEvent2'. |
initialize |
Logical value to internally initialize regression coefficients and baseline hazard functions parameters using simpler models from frailtypack. When initialization is requested, the program first fits two joint frailty models for the recurrent events and each terminal event. When FALSE, parameters are initialized via the arguments init.hazard, init.Sigma, init.Alpha1, init.Alpha2, init.B. |
recurrentAG |
Logical value. Is Andersen-Gill model fitted? If so indicates that recurrent event times with the counting process approach of Andersen and Gill is used. This formulation can be used for dealing with time-dependent covariates. The default is FALSE. |
maxit |
maximum number of iterations for the Marquardt algorithm. Default is 350. |
hazard |
Type of hazard functions. Available options are |
n.knots |
In the case of splines hazard functions, number of knots to be used in the splines basis. This number should be between 4 and 20. Default is 7. |
kappa |
In the case of splines hazard functions, a vector of size 3 containing the values of the smoothing parameters to be used for each baseline hazard function. Default value is 10 for each function. |
crossVal |
In the case of splines hazard functions, indicates how
the smoothing parameters are chosen. If set to "TRUE" then those parameters
are chosen automatically using cross-validation on reduced models for each
baseline hazard function. If set to "FALSE" then the parameters are those provided
by the argument |
constraint.frailty |
Type of positivity constraint used for the variance of of the random effect in the likelihood. Possible values are 'squared' or 'exponential'. Default is 'squared'. See Details. |
GHpoints |
Integer. Number of nodes for Gauss-Hermite integration to marginalize random effects/frailties. Default is 32. |
tolerance |
Numeric, length 3. Optimizer's tolerance for (1) successive change in parameter values, (2) log likelihood, and (3) score, respectively. |
init.hazard |
Numeric. Initialization values for hazard parameters. If a weibull model is used, the order is: shapeR, scaleR, shapeTerminal1, scaleTerminal1, shapeTerminal2, scaleTerminal2. |
init.Sigma |
Numeric,. Initialization value for the standard deviation of the normally-distributed random effects. |
init.Alpha1 |
Numeric. Initialization value for the parameter alpha that links the hazard function of the recurrent event to the first terminal event. |
init.Alpha2 |
Numeric. Initialization value for the parameter alpha that links the hazard function of the recurrent event to the second terminal event. |
init.B |
Numeric vector of the same length and order as the three covariate vectors for the recurrent, terminal1, and terminal2 events (in that order). |
Details
Right-censored data are allowed.
Left-truncated data and stratified analysis are not possible.
Prediction options are not yet available.
The constraint.frailty argument defines the positivity constraint
used for the frailty variance in the likelihood. By default it uses the square
so that the absolute value of the parameter is the standard deviation of the frailty
(i.e \theta^2 = \beta^2).
The other parametrization uses the square of the exponential for the variance
so that the parameter is the logarithm of the standard deviation (\theta^2 = (\exp{\beta})^2).
For others parameters in the model needing a positivity constraint (parameters related to the
baseline hazard functions), the parametrization used is the exponential squared.
Value
Parameters estimates of a competing joint frailty model, more generally a 'jointRecCompet' object. Methods defined for 'jointRecCompet' objects are provided for print, plot and summary. The following components are included in a 'jointRecCompet' object.
summary.table |
A table describing the estimate, standard error, confidence interval, and pvalues for each of the parameters in the model. |
controls |
A vector of named control parameters |
k0 |
For splines baseline hazard functions, vector of penalization terms. |
noVarEvent |
A vector containing for each event type if there is no covariate used in the model. |
np |
Total number of parameters |
b |
Vector containing the estimated coefficients of the model before any positivity constraint.
The values are in order: the coefficients associated with the baseline hazard functions
(either the splines or the shape and scale parameters for Weibull hazard),
the random effect variance, the coefficients of the frailty ( |
H_hessOut |
Covariance matrix of the estimated parameters |
HIHOut |
Covariance matrix of the estimated parameters for the penalized likelihood in the case of Splines baseline hazard functions. |
LCV |
The approximated likelihood cross-validation criterion in the spline case |
critCV |
Convergence criteria |
x1 |
Vector of times for which the hazard function of the recurrent event is estimated. By default seq(0,max(time),length=99), where time is the vector of survival times. |
lam1 |
Matrix of hazard estimates and confidence bands for the recurrent event. |
xSu1 |
Vector of times for the survival function of the recurrent event. |
surv1 |
Matrix of baseline survival estimates and confidence bands for recurrent event. |
x2 |
Vector of times for the first terminal event (see x1 value). |
lam2 |
Matrix of hazard estimates and confidence bands for the first terminal event. |
xSu2 |
Vector of times for the survival function of the first terminal event. |
surv2 |
Vector of the survival function of the first terminal event evaluated at xSu2. |
x3 |
Vector of times for the second terminal event (see x1 value). |
lam3 |
Matrix of hazard estimates and confidence bands for the second terminal event. |
xSu3 |
Vector of times for the survival function of the second terminal event. |
surv3 |
Vector of the survival function of the second terminal event evaluated at xSu3. |
ni |
Number of iterations needed to converge. |
constraintfrailty |
Positivity constraint used for the variance of the random effect |
ziOut1 |
In the spline case, vector of knots used in the spline basis for the recurrent event |
ziOutdc |
In the spline case, vector of knots used in the spline basis for the terminal events |
ghnodes |
Nodes used for the Gauss-Hermite quadrature. |
ghweights |
Weights used for the Gauss-Hermite quadrature. |
tolerance |
Numeric, length 3. Optimizer's tolerance for (1) successive change in parameter values, (2) log likelihood, and (3) score, respectively. |
call |
Call of the function. |
loglikPenal |
Estimated penalized log-likelihood in the spline case |
logLik |
Estimated log-likelihood in the Weibull case |
AIC |
For the Weibull case, Akaike Information criterion |
n |
Total number of subjects |
nevts |
Number of events for each event type. |
See Also
terminal
Examples
set.seed(1)
data=simulatejointRecCompet(n=500,
par0=c(shapeR = 1.5, scaleR = 10,
shapeM = 1.75, scaleM = 16, shapeD = 1.75, scaleD = 16, sigma = 0.5,
alphaM = 1, alphaD = 1, betaR = -0.5, betaM = -0.5, betaD = 0) )
mod <-jointRecCompet(formula = Surv(tstart, tstop, event)~cluster(id)+treatment+
terminal(terminal1)+terminal2(terminal2),
formula.terminalEvent = ~treatment,
formula.terminalEvent2 = ~treatment,
data = data,
recurrentAG = TRUE,
initialize = TRUE,
n.knots=7,
crossVal=TRUE,
hazard = "Splines",
maxit = 350)
#This example uses an extract of 500 patients of the REDUCE trial
data(reduce)
mod_reduce <-jointRecCompet(formula = Surv(t.start,t.stop, del)~cluster(id)+
treatment+terminal(death)+terminal2(discharge),
formula.terminalEvent = ~treatment,
formula.terminalEvent2 = ~treatment,
data = reduce,
initialize = TRUE,
recurrentAG = TRUE,
hazard = "Weibull",
constraint.frailty = "exponential",
maxit = 350)
print(mod_reduce)
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