tprob {survidm} | R Documentation |
Nonparametric estimation of transition probabilities in the illness-death model
Description
This function is used to obtain nonparametric estimates of the transition probabilities in the illness-death model.
Usage
tprob(
formula,
s,
method = "AJ",
conf = FALSE,
conf.level = 0.95,
conf.type = "log",
n.boot = 199,
data,
z.value,
bw = "dpik",
window = "gaussian",
method.weights = "NW",
cluster = FALSE,
ncores = NULL,
na.rm = TRUE
)
Arguments
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. Depends on the
choice of the estimation method of the transition probabilities. For
Aalen-Johansen type estimators ( |
n.boot |
The number of bootstrap replicates to compute the variance of the non-Markovian 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. |
Details
Possible options for argument window are "gaussian"
,
"epanechnikov"
, "tricube"
, "boxcar"
,
"triangular"
, "quartic"
or "cosine"
. The LIDA estimator
was labelled according to the acronym of the Lifetime Data Analysis journal
in which the estimator was described for the first time (Meira-Machado,
U?a-?lvarez and Cadarso-Su?rez, 2006).
Possible methods are:
AJ
Aalen-Johansen estimatorPAJ
Presmoothed Aalen-Johansen estimatorLIDA
LIDA estimatorLM
Landmark approach estimatorPLM
Presmoothed Landmark approach estimatorLMAJ
Landmark approach Aalen-Johansen estimatorPLDAJ
Presmoothed Landmark approach Aalen-Johansen estimatortpIPCW
Inverse Probability of Censoring Weighting for Transition ProbabilitiestpBreslow
Breslow method
Value
An object of class "survIDM"
and one of the following
five classes: "AJ"
, "LIDA"
, "LM"
, "PLM"
,
"LMAJ"
, "PLMAJ"
, "PAJ"
,
"tpIPCW"
and "tpBreslow"
. 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. |
Author(s)
Luis Meira-Machado, Marta Sestelo and Gustavo Soutinho.
References
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.
Meira-Machado L. F., de Una-Alvarez J. and Cadarso-Suarez C. (2006). Nonparametric estimation of transition probabilities in a non-Markov illness-death model. Lifetime Data Anal 12(3), 325–344.
de Una-Alvarez J. and Meira-Machado L. (2015). Nonparametric estimation of transition probabilities in a non-Markov illness-death model: a comparative study. Biometrics 71, 364–375.
Cox, DR (1972). Regression models and life tables (with discussion). Journal of the Royal Statistical Society, Series B 34, 187-200.
Breslow, N. (1972). Discussion of paper by dr cox. Journal of Royal Statistical Society, Series B 34, 216-217.
Examples
## Not run:
# Aalen-Johansen
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 = TRUE, data = colonIDM)
summary(res3, time=365*1:6)
autoplot(res3, interactive = TRUE)
# Conditional transition probabilities
# With factor
res4 <- tprob(survIDM(time1, event1, Stime, event) ~ factor(sex), s = 365,
method = "AJ", conf = TRUE, data = colonIDM)
summary(res4, time=365*1:6)
plot(res4, trans="02", ylim=c(0,0.5))
res5 <- tprob(survIDM(time1, event1, Stime, event) ~ rx, s =365,
method = "breslow", z.value='Lev', conf = TRUE, data =colonIDM)
summary(res5, time=365*1:6)
plot(res5,trans="02", ylim=c(0,0.5))
# with continuous covariate (IPCW and Breslow Method)
res6 <- 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(res6, time=365*1:6)
plot(res6)
res7 <- tprob(survIDM(time1, event1, Stime, event) ~ age, s =365,
method = "breslow", z.value=60, conf = FALSE, data =colonIDM)
summary(res7, time=365*1:6)
autoplot(res7, interactive=TRUE)
res8 <- tprob(survIDM(time1, event1, Stime, event) ~ age, s =365,
method = "breslow", conf.type='bootstrap', z.value=60, conf = TRUE, data =colonIDM)
summary(res8, time=365*1:6)
plot(res8)
res9 <- tprob(survIDM(time1, event1, Stime, event) ~ rx, s =365,
method = "breslow", conf.type='bootstrap', conf = TRUE, data =colonIDM)
summary(res9, time=365*1:6)
plot(res9, trans="02", ylim=c(0,0.5))
# more than a covariate (Breslow Method)
res10<- tprob(survIDM(time1, event1, Stime, event) ~ nodes + factor(rx), s =365,
method = "breslow", conf = TRUE, data =colonIDM)
summary(res10,t=365*1:5)
autoplot(res10)
res11<- tprob(survIDM(time1, event1, Stime, event) ~ nodes + factor(rx), s =365,
method = "breslow", z.value=c(10,'Obs'), conf = TRUE, data =colonIDM)
summary(res11,t=365*1:5)
autoplot(res11)
# more than a covariate for Non Linear Models (Breslow Method)
res12<- tprob(survIDM(time1, event1, Stime, event) ~ pspline(age)+ nodes + factor(rx), s =365,
method = "breslow", conf = TRUE, data =colonIDM)
summary(res12,t=365*1:5)
autoplot(res12)
# Confidence intervals
res13 <- 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(res13, time=365*1:7)
autoplot(res13)
## End(Not run)