Description Usage Arguments Details Value Author(s) References Examples
This function is used to obtain nonparametric estimates of the transition probabilities in the illnessdeath model.
1 2 3 4 
formula 
A 
s 
The first time for obtaining estimates for the transition probabilities. If missing, 0 will be used. 
method 
The method used to compute the transition probabilities.
Possible options are 
conf 
Provides pointwise confidence bands. Defaults to 
conf.level 
Level of confidence. Defaults to 0.95 (corresponding to 95%). 
conf.type 
Method to compute the confidence intervals.
Transformation applied to compute confidence intervals. Possible choices
are 
n.boot 
The number of bootstrap replicates to compute the variance of the nonMarkovian estimator. Default is 199. 
data 
A data.frame including at least four columns named

z.value 
The value of the covariate on the right hand side of formula at which the transition probabilities are computed. For quantitative covariates, i.e. of class integer and numeric. 
bw 
A single numeric value to compute a kernel density bandwidth.
Use 
window 
A character string specifying the desired kernel.
See details below for possible options. Defaults to 
method.weights 
A character string specifying the desired weights method.
Possible options are 
cluster 
A logical value. If 
ncores 
An integer value specifying the number of cores to be used in
the parallelized procedure. If 
na.rm 
A logical value indicating whether NA values should be stripped in the computation. 
Possible options for argument window are "gaussian"
,
"epanechnikov"
, "tricube"
, "boxcar"
,
"triangular"
, "quartic"
or "cosine"
.
Possible methods are:
AJ
AalenJohansen estimator
PAJ
Presmoothed AalenJohansen estimator
LIDA
LIDA estimator
LM
Landmark approach estimator
PLM
Presmoothed Landmark approach estimator
LMAJ
Landmark approach AalenJohansen estimator
PLDAJ
Presmoothed Landmark approach AalenJohansen estimator
tpIPCW
Inverse Probability of Censoring Weighting for Transition Probabilities
An object of class "survIDM"
and one of the following
five classes: "AJ"
, "LIDA"
, "LM"
, "PLM"
,
"LMAJ"
, "PLMAJ"
, "PAJ"
and
"tpIPCW"
. Objects are implemented as a list with elements:
est 
data.frame with estimates of the transition probabilities. 
CI 
data.frame with the confidence intervals of the transition probabilities. 
conf.level 
Level of confidence. 
s 
The first time for obtaining estimates for the transition probabilities. 
t 
The time for obtaining the estimates of transition probabilities. 
conf 
logical; if 
conf.type 
Type of the confidence interval. 
callp 
The expression of the estimated probability. 
Nlevels 
The number of levels of the covariate. Provides important information when the covariate at the right hand side of formula is of class factor. 
levels 
The levels of the qualitative covariate (if it is of class factor) on the right hand side of formula. 
formula 
A formula object. 
call 
A call object. 
Luis MeiraMachado and Marta Sestelo.
Aalen O. O., Johansen S. (1978) An Empirical Transition Matrix for Nonhomogeneous Markov Chains Based on Censored Observations. Scandinavian Journal of Statistics 5(3), 141–150.
MeiraMachado L. F., de UnaAlvarez J. and CadarsoSuarez C. (2006). Nonparametric estimation of transition probabilities in a nonMarkov illnessdeath model. Lifetime Data Anal 12(3), 325–344.
de UnaAlvarez J. and MeiraMachado L. (2015). Nonparametric estimation of transition probabilities in a nonMarkov illnessdeath model: a comparative study. Biometrics 71, 364–375.
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 50 51 52 53 54 55 56 57 58 59 60 61  # AalenJohansen
# Occupation Probabilities Pj(t)=Pij(0,t)
res < tprob(survIDM(time1, event1, Stime, event) ~ 1, s = 0,
method = "AJ", conf = FALSE, data = colonIDM)
summary(res, time=365*1:6)
plot(res)
# Transition Probabilities Pij(t)=Pij(365,t)
# LIDA
res1 < tprob(survIDM(time1, event1, Stime, event) ~ 1, s = 365,
method = "LIDA", conf = FALSE, data = colonIDM)
summary(res1, time=365*1:6)
plot(res1)
plot(res1, trans="01", ylim=c(0,0.15))
# Landmark (LM)
res2 < tprob(survIDM(time1, event1, Stime, event) ~ 1, s = 365,
method = "LM", conf = FALSE, data = colonIDM)
summary(res2, time=365*1:6)
plot(res2)
# Presmoothed LM
res3 < tprob(survIDM(time1, event1, Stime, event) ~ 1, s = 365,
method = "PLM", conf = FALSE, data = colonIDM)
summary(res3, time=365*1:6)
plot(res3)
# Conditional transition probabilities
#with factor
res4 < tprob(survIDM(time1, event1, Stime, event) ~ factor(sex), s = 365,
method = "AJ", conf = FALSE, data = colonIDM)
summary(res4, time=365*1:6)
plot(res4, trans="02", ylim=c(0,0.5))
# with continuous covariate (IPCW)
res5 < tprob(survIDM(time1, event1, Stime, event) ~ age, s = 365,
method = "IPCW", z.value = 48, conf = FALSE, data = colonIDM,
bw = "dpik", window = "gaussian", method.weights = "NW")
summary(res5, time=365*1:6)
plot(res5)
# Confidence intervals
#res6 < tprob(survIDM(time1, event1, Stime, event) ~ 1, s = 365,
#method = "AJ", conf = TRUE, n.boot = 5, conf.level = 0.95,
#conf.type = "log", data = colonIDM)
#summary(res6, time=365*1:7)
#plot(res6)

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