models_surv_direct {adjustedCurves}

List of supported models in surv_direct

Description

Supported models for the outcome_model argument when using method="direct" in the adjustedsurv function.

Details

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.

Note

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.