idm: Fit an illness-death model

In SmoothHazard: Smooth models for interval-censored data with applications to survival and illness-death models

Description

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.

Usage

idm(formula01, formula02, formula12, data, maxiter = 200, eps = c(5, 5, 3),
  n.knots = c(7, 7, 7), knots = "equidistant", CV = FALSE,
  kappa = c(1000000, 500000, 20000), method = "Weib", conf.int = TRUE,
  print.iter = FALSE, subset = NULL, na.action = na.fail)

Arguments

formula01	A formula specifying a regression model for the 0 --> 1 transition from the initial state to the transient state of the illness-death model. The right hand side of the formula specifies the covariate terms, and the left hand side must be an event history object as returned by the function Hist.
formula02	A formula specifying a regression model for the 0 --> 2 transition from the initial state to the absorbing state. The left hand side must be equal to the left hand side of formula01. If missing it is set to formula01.
formula12	A formula specifying a regression model for the 1 --> 2 transition from the transient state to the absorbing state. operator is not required. If missing it is set to formula01.
data	A data frame in which to interpret the variables of formula01, formula02 and formula12.
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 c(5,5,3) which is translated into convergence if the respective values change less then 10^{-5} (for regression parameters and likelihood) and 10^{-3} for the second derivatives between two iterations.
n.knots	For method="Splines" only, a vector of length 3 specifing the number of knots, one for each transition, for the M-splines estimate of the baseline intensities in the order 0 --> 1, 0 --> 2, 1 --> 2. The default is c(7,7,7).
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 TRUE to calculate pointwise confidence intervals for the transition intensities curves, FALSE otherwise. Default is TRUE.
print.iter	boolean parameter. Equals to TRUE to print the likelihood during the iteration process, FALSE otherwise. Default is FALSE. This option is not running on Windows.
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.

Details

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.

Value

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. Hist or Surv object.
responseAbs	model response for the 0 –> 2 transition. Hist or Surv object.
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.

Author(s)

R: Celia Touraine <[e-mail]> Fortran: Pierre Joly <[e-mail]>

References

D. Marquardt (1963). An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics, 431-441.

See Also

print.idm summary.idm

Examples

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)

SmoothHazard documentation built on May 2, 2019, 4:43 p.m.