models_surv_direct | R Documentation |
surv_direct
Supported models for the outcome_model
argument when using method="direct"
in the adjustedsurv
function.
The following models are directly supported in the outcome_model
in the surv_direct
function. The first letter in parentheses after the object name is a group indicator. Below the list there are more information for each group.
coxph
[A, Required Packages: survival, riskRegression]
cph
[A, Required Packages: rms, survival, riskRegression]
aalen
[B, Required Packages: timereg, pec]
cox.aalen
[B, Required Packages: timereg, pec]
selectCox
[B, Required Packages: riskRegression, pec]
pecCforest
[B, Required Packages: pec]
pecRpart
[B, Required Packages: pec, Bootstrapping not allowed.]
riskRegression
[C, Required Packages: riskRegression]
prodlim
[C, Required Packages: prodlim, riskRegression]
psm
[C, Required Packages: rms, riskRegression]
flexsurvreg
[C, Required Packages: flexsurv, riskRegression]
flexsurvspline
[C, Required Packages: flexsurv, riskRegression]
ranger
[C, Required Packages: ranger, riskRegression]
rfsrc
[C, Required Packages: randomForestSRC, riskRegression]
ARR
[C, Required Packages: riskRegression]
penalizedS3
[C, Required Packages: penalized, riskRegression]
gbm
[C, Required Packages: gbm, riskRegression]
fit_hal
[C, Required Packages: hal9001, riskRegression]
fitSmoothHazard
[C, Required Packages: casebase, riskRegression]
glm
[D, Required Packages: stats, pec]
ols
[D, Required Packages: rms, pec]
randomForest
[D, Required Packages: randomForest, pec]
mexhaz
[E, Required Packages: mexhaz]
Any model with a fitting S3 prediction method or a valid predict_fun
can be used as well. See below.
Group A: The direct adjusted survival probabilities are estimated directly using the ate
function. Additional arguments supplied using the ...
syntax are passed to the ate
function. Note that Surv()
calls required to fit the model should be made inside the formula, not saved elsewhere.
Group B: Predicted survival probabilities are obtained using the predictSurvProb
function. The G-Computation is carried out using those. Additional arguments supplied using the ...
syntax are passed to the predictSurvProb
function.
Group C: The predictRisk
function is used to obtain predicted cumulative incidences, which are then transformed to survival probabilities. Additional arguments supplied using the ...
syntax are passed to the predictRisk
function.
Group D: These models are only allowed if there is no censoring. Predicted survival probabilities are obtained using the predictProb
function from the pec package. Additional arguments supplied using the ...
syntax are passed to the predictProb
function.
Group E: Custom code is used to obtain predicted survival probabilities. Additional arguments are not used.
It is sometimes possible to use models even if they are not listed here. There are two ways to make this work. The first one is to use the models S3 predict
method. This works if the predict
function contains the arguments object
, newdata
and times
and returns a matrix of predicted survival probabilities. The matrix should be of size nrow(data) * length(times)
, where each row corresponds to a row in the original dataset and each column to one point in time. The matrix should contain the survival probabilities predicted by the model given covariates. If no such predict
method exists the only option left is to write your own function which produces the output described above and supply this function to the predict_fun
argument.
If you think that some important models are missing from this list, please file an issue on the official github page with a specific feature request (URL can be found in the DESCRIPTION file) or contact the package maintainer directly using the given e-mail address.
When using outcome models which are not directly supported (either through the default predict method or a custom predict_fun
) it might be necessary to set the clean_data
argument of the adjustedsurv
function to FALSE
.
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