Description Usage Arguments Details Value Author(s) References See Also Examples
Fit an illness-death model using either a semi-parametric approach (penalized likelihood with an approximation of the transition intensity functions by linear combination of M-splines) or a parametric approach (specifying Weibull distributions on the transition intensities). Left-truncated, right-censored, and interval-censored data are allowed. State 0 corresponds to the initial state, state 1 to the transient one, state 2 to the absorbant one. The allowed transitions are: 0 –> 1, 0 –> 2 and 1 –> 2.
1 2 3 4 |
formula01 |
A formula specifying a regression model for the
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formula02 |
A formula specifying a regression model for the
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formula12 |
A formula specifying a regression model for the
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data |
A data frame in which to interpret the variables of
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maxiter |
Maximum number of iterations. The default is 200. |
eps |
A vector of 3 integers >0 used to define the power of
three convergence criteria: 1. for the regression parameters,
2. for the likelihood, 3. for the second derivatives. The default
is |
n.knots |
For |
knots |
List of length 3 containing the placements (timepoints) of the knots for the M-spline of the three transitions. |
CV |
Binary variable equals to 1 when search (by approximated cross validation) of the smoothing parameters kappa and 0 otherwise. Argument for the penalized likelihood approach. The default is 0. |
kappa |
a vector of length 3. If CV=FALSE, smoothing parameters for the transition 0 –> 1, 0 –> 2 and 1 –> 2. If CV=TRUE, initial values of the smoothing parameters for the cross validation search. Argument for the penalized likelihood approach. |
method |
type of estimation method: "Splines" for a penalized likelihood approach with approximation of the transition intensities by M-splines, "Weib" for a parametric approach with a Weibull distribution on the transition intensities. Default is "Weib". |
conf.int |
Boolean parameter. Equals to |
print.iter |
boolean parameter. Equals to |
subset |
expression indicating the subset of the rows of data to be used in the fit. All observations are included by default. |
na.action |
how NAs are treated. The default is first, any na.action attribute of data, second a na.action setting of options, and third 'na.fail' if that is unset. The 'factory-fresh' default is na.omit. Another possible value is NULL. |
The estimated parameters are obtained using the robust Marquardt algorithm (Marquardt, 1963) which is a combination between a Newton-Raphson algorithm and a steepest descent algorithm.
call |
the call that produced the result. |
coef |
regression parameters. |
loglik |
vector containing the log-likelihood without and with covariate. |
cv |
vector containing the convergence criteria. |
niter |
number of iterations. |
converged |
integer equal to 1 when the model converged, 2, 3 or 4 otherwise. |
modelPar |
Weibull parameters. |
N |
number of subjects. |
events1 |
number of events 0 –> 1. |
events2 |
number of events 0 –> 2 or 0 –> 1 –> 2. |
NC |
vector containing the number of covariates on transitions 0 –> 1, 0 –> 2, 1 –> 2. |
responseTrans |
model response for the 0 –> 1
transition. |
responseAbs |
model
response for the 0 –> 2 transition. |
time |
times for which transition intensities have been evaluated for plotting. Vector in the Weibull approach. Matrix in the penalized likelihhod approach for which the colums corresponds to the transitions 0 –> 1, 1 –> 2, 0 –> 2. |
intensity01 |
matched values of the intensities for transition 0 –> 1. |
lowerIntensity01 |
lower confidence intervals for the values of the intensities for transition 0 –> 1. |
upperIntensity01 |
upper confidence intervals for the values of the intensities for transition 0 –> 1. |
intensity02 |
matched values of the intensities for transition 0 –> 2. |
lowerIntensity02 |
lower confidence intervals for the values of the intensities for transition 0 –> 2. |
upperIntensity02 |
upper confidence intervals for the values of the intensities for transition 0 –> 2. |
intensity12 |
matched values of the intensities for transition 1 –> 2. |
lowerIntensity12 |
lower confidence intervals for the values of the intensities for transition 1 –> 2. |
upperIntensity12 |
upper confidence intervals for the values of the intensities for transition 1 –> 2. |
RR |
vector of relative risks. |
V |
variance-covariance matrix. |
se |
standart errors of the regression parameters. |
Xnames01 |
names of covariates on 0 –> 1. |
Xnames02 |
names of covariates on 0 –> 2. |
Xnames12 |
names of covariates on 1 –> 2. |
knots01 |
knots to approximate by M-splines the intensity of the 0 –> 1 transition. |
knots02 |
knots to approximate by M-splines the intensity of the 0 –> 2 transition. |
knots12 |
knots to approximate by M-splines the intensity of the 1 –> 2 transition. |
nknots01 |
number of knots on transition 0 –> 1. |
nknots02 |
number of knots on transition 0 –> 2. |
nknots12 |
number of knots on transition 1 –> 2. |
theta01 |
square root of splines coefficients for transition 0 –> 1. |
theta02 |
square root of splines coefficients for transition 0 –> 2. |
theta12 |
square root of splines coefficients for transition 1 –> 2. |
CV |
a binary variable equals to 1 when search of the smoothing parameters kappa by approximated cross-validation, 1 otherwise. The default is 0. |
kappa |
vector containing the smoothing parameters for transition 0 –> 1, 0 –> 2, 1 –> 2 used to estimate the model by the penalized likelihood approach. |
CVcrit |
cross validation criteria. |
DoF |
degrees of freedom of the model. |
na.action |
observations deleted if missing values. |
R: Celia Touraine <Celia.Touraine@isped.u-bordeaux2.fr> Fortran: Pierre Joly <Pierre.Joly@isped.u-bordeaux2.fr>
D. Marquardt (1963). An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics, 431-441.
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 31 32 33 34 35 36 37 38 39 40 41 42 43 | library(lava)
library(prodlim)
set.seed(17)
d <- simulateIDM(100)
# right censored data
fitRC <- idm(formula01=Hist(time=observed.illtime,event=seen.ill)~X1+X2,
formula02=Hist(time=observed.lifetime,event=seen.exit)~X1+X2,
formula12=Hist(time=observed.lifetime,event=seen.exit)~X1+X2,data=d,
conf.int=FALSE)
fitRC
# interval censored data
fitIC <- idm(formula01=Hist(time=list(L,R),event=seen.ill)~X1+X2,
formula02=Hist(time=observed.lifetime,event=seen.exit)~X1+X2,
formula12=Hist(time=observed.lifetime,event=seen.exit)~X1+X2,data=d,
conf.int=FALSE)
fitIC
## Not run:
data(Paq1000)
# Illness-death model with certif on the 3 transitions
# Weibull parametrization and likelihood maximization
fit.weib <- idm(formula02=Hist(time=t,event=death,entry=e)~certif,
formula01=Hist(time=list(l,r),event=dementia)~certif,
data=Paq1000)
# Illness-death model with certif on transitions 01 and 02
# Splines parametrization and penalized likelihood maximization
fit.splines <- idm(formula02=Hist(time=t,event=death,entry=e)~certif,
formula01=Hist(time=list(l,r),event=dementia)~certif,
formula12=~1,
method="Splines",
data=Paq1000)
## to print
fit.weib
## to summary
summary(fit.splines)
## End(Not run)
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