Description Usage Arguments Details Value Author(s) References Examples
The 4-state Markov model includes an initial state (X=1), a transient state (X=2) and two absorbing states (X=3 and X=4). The possible transitions are: 1->2, 1->3, 1->4, 2->3 and 2->4.
1 2 3 4 5 6 7 8 9 10 | m4(t1, t2, sequence, weights=NULL, dist,
cuts.12=NULL, cuts.13=NULL, cuts.14=NULL, cuts.23=NULL, cuts.24=NULL,
ini.base.12=NULL, ini.base.13=NULL, ini.base.14=NULL,
ini.base.23=NULL, ini.base.24=NULL,
cov.12=NULL, init.cov.12=NULL, names.12=NULL,
cov.13=NULL, init.cov.13=NULL, names.13=NULL,
cov.14=NULL, init.cov.14=NULL, names.14=NULL,
cov.23=NULL, init.cov.23=NULL, names.23=NULL,
cov.24=NULL, init.cov.24=NULL, names.24=NULL,
conf.int=TRUE, silent=TRUE, precision=10^(-6))
|
t1 |
A numeric vector with the observed times in days from baseline to the first transition (X=2, X=3 or X=4) or to the right-censoring (in X=1 at the last follow-up). |
t2 |
A numeric vector with the observed times in days from baseline to the second transition or to the right censoring (in X=2 at the last follow-up). |
sequence |
a numeric vector with the sequence of observed states. Six possible values are allowed: 1 (individual right-censored in X=1), 12 (individual right-censored in X=2), 13 (individual who directly transited from X=1 to X=3), 14 (individual who directly transited from X=1 to X=4), 123 (individual who transited from X=1 to X=3 through X=2), 124 (individual who transited from X=1 to X=4 through X=2). |
weights |
A numeric vector with the weights for correcting the contribution of each individual. When the vector is completed, the IPW estimator is implemented. Default is |
dist |
A character vector with three arguments describing respectively the distributions of duration time for transitions 1->2, 1->3 and 2->3. Arguments allowed are |
cuts.12 |
A numeric vector indicating the timepoints in days for the piecewise exponential distribution related to the time from X=1 to X=2. Only internal timepoints are allowed: timepoints cannot be |
cuts.13 |
A numeric vector indicating the timepoints in days for the piecewise exponential distribution related to the time from X=1 to X=3. Only internal timepoints are allowed: timepoints cannot be |
cuts.14 |
A numeric vector indicating the timepoints in days for the piecewise exponential distribution related to the time from X=1 to X=4. Only internal timepoints are allowed: timepoints cannot be |
cuts.23 |
A numeric vector indicating the timepoints in days for the piecewise exponential distribution related to the time from X=2 to X=3. Only internal timepoints are allowed: timepoints cannot be |
cuts.24 |
A numeric vector indicating the timepoints in days for the piecewise exponential distribution related to the time from X=2 to X=4. Only internal timepoints are allowed: timepoints cannot be |
ini.base.12 |
A numeric vector of initial values for the distribution from X=1 to X=2. The logarithm of the parameters have to be declared. Default value is 1. |
ini.base.13 |
A numeric vector of initial values for the distribution from X=1 to X=3. The logarithm of the parameters have to be declared. Default value is 1. |
ini.base.14 |
A numeric vector of initial values for the distribution from X=1 to X=4. The logarithm of the parameters have to be declared. Default value is 1. |
ini.base.23 |
A numeric vector of initial values for the distribution from X=2 to X=3. The logarithm of the parameters have to be declared. Default value is 1. |
ini.base.24 |
A numeric vector of initial values for the distribution from X=2 to X=4. The logarithm of the parameters have to be declared. Default value is 1. |
cov.12 |
A matrix (or data frame) with the explicative time-fixed variable(s) related to the time from X=1 to X=2. |
init.cov.12 |
A numeric vector of initial values for regression coefficients (logarithm of the cause-specific hazards ratios) associated to |
names.12 |
An optional character vector with name of explicative variables associated to |
cov.13 |
A numeric matrix (or data frame) with the explicative time-fixed variable(s) related to the time from X=1 to X=3. |
init.cov.13 |
A numeric vector of initial values for regression coefficients (logarithm of the cause-specific hazards ratios) associated to |
names.13 |
An optional character vector with name of explicative variables associated to |
cov.14 |
A numeric matrix (or data frame) with the explicative time-fixed variable(s) related to the time from X=1 to X=4. |
init.cov.14 |
A numeric vector of initial values for regression coefficients (logarithm of the cause-specific hazards ratios) associated to |
names.14 |
An optional character vector with name of explicative variables associated to |
cov.23 |
A numeric matrix (or data frame) with the explicative time-fixed variable(s) related to the time from X=2 to X=3. |
init.cov.23 |
A numeric vector of initial values for regression coefficients (logarithm of the cause-specific hazards ratios) associated to |
names.23 |
An optional character vector with name of explicative variables associated to |
cov.24 |
A numeric matrix (or data frame) with the explicative time-fixed variable(s) related to the time from X=2 to X=4. |
init.cov.24 |
A numeric vector of initial values for regression coefficients (logarithm of the cause-specific hazards ratios) associated to |
names.24 |
An optional character vector with name of explicative variables associated to |
conf.int |
A logical value specifying if the pointwise confidence intervals for parameters and the variance-covariance matrix should be returned. Default is |
silent |
A logical value specifying if the log-likelihood value should be returned at each iteration. Default is |
precision |
A numeric positive value indicating the required precision for the log-likelihood maximization between each iteration. Default is 10^{-6}. |
Hazard functions available are:
Exponential distribution | λ(t)=1/σ |
Weibull distribution | λ(t)=ν(\frac{1}{σ})^{ν}t^{ν-1} |
Generalized Weibull distribution | λ(t)=\frac{1}{θ}≤ft(1+≤ft(\frac{t}{σ}\right)^{ν}\right)^{\frac{1}{θ}-1} ν≤ft(\frac{1}{σ}\right)^{ν} t^{ν-1} |
with σ, ν,and θ>0. The parameter σ varies for each interval when the distribution is piecewise Exponential. We advise to initialize the logarithm of these parameters in ini.base.12
, ini.base.13
and ini.base.23
.
To estimate the marginal effect of a binary exposure, the weights
may be equal to 1/p
, where p
is the estimated probability that the individual belongs to his or her own observed group of exposure. The probabilities p
are often estimated by a logistic regression in which the dependent binary variable is the exposure. The possible confounding factors are the explanatory variables of this logistic model.
object |
A character string indicating the estimated model: "m4 (4-state time-inhomogeneous markov model)". |
dist |
A character vector with two arguments describing respectively the distributions of duration time for transitions 1->2, 1->3, 1->4, 2->3, and 2->4. |
cuts.12 |
A numeric vector indicating the timepoints in days for the piecewise exponential distribution related to the time from X=1 to X=2. |
cuts.13 |
A numeric vector indicating the timepoints in days for the piecewise exponential distribution related to the time from X=1 to X=3. |
cuts.14 |
A numeric vector indicating the timepoints in days for the piecewise exponential distribution related to the time from X=1 to X=4. |
cuts.23 |
A numeric vector indicating the timepoints in days for the piecewise exponential distribution related to the time from X=2 to X=3. |
cuts.24 |
A numeric vector indicating the timepoints in days for the piecewise exponential distribution related to the time from X=2 to X=4. |
covariates |
A numeric vector indicating the numbers of covariates respectively related to the transition 1->2, 1->3, 1->4, 2->3, and 2->4. |
table |
A data frame containing the estimated parameters of the model ( |
cov.matrix |
A data frame corresponding to variance-covariance matrix of the parameters. |
LogLik |
A numeric value corresponding to the log-likelihood of the estimated model. |
AIC |
A numeric value corresponding to the Akaike Information Criterion of the estimated model. |
Yohann Foucher <Yohann.Foucher@univ-nantes.fr> and
Florence Gillaizeau <Florence.Gillaizeau@univ-nantes.fr>
Huszti E, Abrahamowicz M, Alioum A, Binquet C, Quantin C. Relative survival multistate Markov model. Stat Med. 2012 Feb 10;31(3):269-86. <DOI: 10.1002/sim.4392>
Pohar M, Stare J. Relative survival analysis in R. Computer Methods and Programs in Biomedicine 2016; 81: 272-278. <DOI: 10.1016/j.cmpb.2006.01.004>
Gillaizeau F, Senage T, Le Borgne F, Le Tourneau T, Roussel JC, Leffondre K, Porcher R, Giraudeau B, Dantan E, Foucher Y. Inverse Probability Weighting to control confounding in an illness-death model for interval-censored data. Manuscript submitted. 2016.
Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res May 2011; 46(3): 399-424. <DOI: 10.1080/ 00273171.2011.568786>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | # import the observed data
# (X=1 corresponds to initial state with a functioning graft, X=2 to acute rejection episode,
# X=3 to return to dialysis, X=4 to death with a functioning graft)
data(dataDIVAT)
# A subgroup analysis to reduce the time needed for this example
dataDIVAT$id<-c(1:nrow(dataDIVAT))
set.seed(2)
d4<-dataDIVAT[dataDIVAT$id %in% sample(dataDIVAT$id, 200, replace = FALSE),]
# 4-state parametric Markov model including one explicative variable ('z')
# on the trainsition from X=1 to X=2. We only reduced
# the precision and the number of iteration to save time in this example,
# prefer the default values.
m4(t1=d4$time1, t2=d4$time2, sequence=d4$trajectory, dist=c("E","E","E","E","E"),
ini.base.12=c(8.31), ini.base.13=c(10.46), ini.base.14=c(10.83),
ini.base.23=c(9.01), ini.base.24=c(10.81),
cov.12=d4$z, init.cov.12=c(-0.02), names.12=c("beta12_z") ,
conf.int=TRUE, silent=FALSE, precision=0.001)$table
|
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