Description Usage Arguments Value References See Also Examples
Fits a semiparametric regression model to estimate the effects on transition probabilities in a (possibly nonMarkov) progressive illnessdeath model for a sequence of time.
1 
formula 
an object of class formula which specifies the covariates. For example

data 
a data.frame of 
link 
a link function for 
s 
the current time for the transition probabilities; default is zero which reports the occupation probabilities. 
t 
the Future time for the transition probabilities; default is 
R 
the number of bootstrap replicates. Default is 199. 
by 
number: increment of the sequence from time 
trans 
the possible transition(s) for a progressive illnessdeath model. For 
ncores 
the number of cores to use for parallel execution. Default is the number of CPU cores on the current host. 
TPreg
returns an object of class TPreg
.
An object of class TPreg
is a list containing at least the following components:
co 
the list of:

call 
the matched call. 
transition 
the transition, this is equal to the 
s 
the current time for the transition probability. 
t 
the future time for the transition probability. 
n.misobs 
the number of missing observations. 
In addition, trans="all"
will have four lists: co11
, co12
, co13
, and co23
instead of co
and will give the information for all possible transitions in the progressive illness death model
Azarang, L. Scheike, TH. and de UnaAlvarez, J. (2017) Direct modeling of regression effects for transition probabilities in the progressive illnessdeath model, Statistics in Medicine 36, 19641976.
print.TPreg
, summary.TPreg
, and plot.TPreg
as generic functions.
1 2 3 4 5 6  data(colonTPreg)
co11 < TPreg( ~ Age + Nodes + treatment, colonTPreg, link = "logit", s = 50, R = 19, t = 200,
trans = "11", ncores = 1)
co11
summary(co11)

Loading required package: survival
Loading required package: foreach
Loading required package: doParallel
Loading required package: iterators
Loading required package: parallel
Warning message:
In TPreg(~Age + Nodes + treatment, colonTPreg, link = "logit", s = 50, :
Nodes variable in 'data' has missing value(s),
Call:
TPreg(formula = ~Age + Nodes + treatment, data = colonTPreg,
link = "logit", s = 50, t = 200, R = 19, trans = "11", ncores = 1)
Transition:
[1] "11"
(s,t):
[1] 50 200
Coefficients:
Estimate St.Err LW.L UP.L P.value
X.Intercept. 2.492609419 0.72041023 1.08060538 3.90461346 0.0005402031
Age 0.005980971 0.01181244 0.02913336 0.01717142 0.6126264027
Nodes 0.074748098 0.02923819 0.13205494 0.01744125 0.0105724084
treatmentLev 0.039682347 0.24418653 0.43892324 0.51828794 0.8709055616
treatmentLev.5FU 0.929566117 0.32475154 0.29305310 1.56607913 0.0042045690
[1] "18 observations deleted due to missingness from 'data'"
Call:
TPreg(formula = ~Age + Nodes + treatment, data = colonTPreg,
link = "logit", s = 50, t = 200, R = 19, trans = "11", ncores = 1)
(s,t):
[1] 50 200
Transition 11 :
Coefficients:
time X.Intercept. Age Nodes treatmentLev treatmentLev.5FU
1 56 58.363354 0.401888881 6.26264521 31.10806842 9.4653733
2 185 2.636107 0.007185251 0.07924264 0.10143607 1.0470649
3 200 2.492609 0.005980971 0.07474810 0.03968235 0.9295661
Standard Errors:
time X.Intercept. Age Nodes treatmentLev treatmentLev.5FU
1 56 491.3913682 4.40435468 16.67490873 370.5809571 368.6372256
2 185 0.6884838 0.01042247 0.02954041 0.2612095 0.4063520
3 200 0.7204102 0.01181244 0.02923819 0.2441865 0.3247515
Lower limit:
time X.Intercept. Age Nodes treatmentLev treatmentLev.5FU
1 56 904.763728 9.03442405 26.4201759 757.4467443 731.9943355
2 185 1.286679 0.02761330 0.1371418 0.4105346 0.2506150
3 200 1.080605 0.02913336 0.1320549 0.4389232 0.2930531
Upper limit:
time X.Intercept. Age Nodes treatmentLev treatmentLev.5FU
1 56 1021.490435 8.23064628 38.94546631 695.2306074 713.063589
2 185 3.985535 0.01324280 0.02134344 0.6134067 1.843515
3 200 3.904613 0.01717142 0.01744125 0.5182879 1.566079
p.value:
time X.Intercept. Age Nodes treatmentLev treatmentLev.5FU
1 56 0.9054562857 0.9272955 0.707234379 0.9331009 0.979515239
2 185 0.0001287392 0.4905717 0.007307052 0.6977702 0.009973593
3 200 0.0005402031 0.6126264 0.010572408 0.8709056 0.004204569
[1] "18 observation(s) deleted due to missingness from 'data'"
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