Description Usage Arguments Details Value Author(s) References See Also Examples
Fit an illnessdeath model using either a semiparametric approach (penalized likelihood with an approximation of the transition intensity functions by linear combination of Msplines) or a parametric approach (specifying Weibull distributions on the transition intensities). Lefttruncated, rightcensored, and intervalcensored 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

formula02 
A formula specifying a regression model for the

formula12 
A formula specifying a regression model for the

data 
A data frame in which to interpret the variables of

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 
Argument only active for the penalized likelihood approach
The algorithm needs at least 5 knots and allows no more than 20 knots. 
CV 
Binary variable equals to 1 when search (by approximated
cross validation) of the smoothing parameters 
kappa 
Argument only active for the penalized likelihood approach 
method 
type of estimation method: "Splines" for a penalized likelihood approach with approximation of the transition intensities by Msplines, "Weib" for a parametric approach with a Weibull distribution on the transition intensities. Default is "Weib". 
conf.int 
Level of confidence pointwise confidence intervals of the transition intensities, i.e.,
a value between 0 and 1, the default is 
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 'factoryfresh' 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 NewtonRaphson algorithm and a steepest descent algorithm.
call 
the call that produced the result. 
coef 
regression parameters. 
loglik 
vector containing the loglikelihood 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 
variancecovariance matrix derived from the Hessian of the loglikelihood if using method="Weib" or, from the Hessian of the penalized loglikelihood if using method="Splines". 
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 Msplines the intensity of the 0 –> 1 transition. 
knots02 
knots to approximate by Msplines the intensity of the 0 –> 2 transition. 
knots12 
knots to approximate by Msplines 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 crossvalidation, 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.ubordeaux2.fr> Fortran: Pierre Joly <Pierre.Joly@isped.ubordeaux2.fr>
D. Marquardt (1963). An algorithm for leastsquares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics, 431441.
print.idm
summary.idm
predict.idm
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 44 45 46 47 48 49  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
## Not run:
set.seed(17)
d < simulateIDM(300)
fitRC.splines < 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)~1,data=d,
conf.int=FALSE,method="splines")
## End(Not run)
# 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)
# Illnessdeath 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)
# Illnessdeath 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)
fit.weib
summary(fit.splines)
## End(Not run)

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