View source: R/fit_survival_model.R
fit_survival_model | R Documentation |
This function is a helper function for fit_LOCF_landmark_model
and fit_LME_landmark_model
.
fit_survival_model( data, individual_id, cv_name = NA, covariates, event_time, event_status, survival_submodel = c("standard_cox", "cause_specific", "fine_gray"), x_hor )
data |
Data frame containing covariates and time-to-event data, one row for each individual. |
individual_id |
Character string specifying the column name in |
cv_name |
Character string specifying the column name in |
covariates |
Vector of character strings specifying the column names in |
event_time |
Character string specifying the column name in |
event_status |
Character string specifying the column name in |
survival_submodel |
Character string specifying which survival submodel to
use. Three options: the standard Cox model i.e. no competing risks ( |
x_hor |
Numeric specifying the horizon time(s) |
For the survival submodel, there are three choices of model:
the standard Cox model, this is a wrapper function for coxph
from the package survival
the cause-specific model, this is a wrapper function for CSC
from package riskRegression
the Fine Gray model, this is a wrapper function for FGR
from package riskRegression
The latter two models estimate the probability of the event of interest in the presence of competing events.
For both the c-index and Brier score calculations, inverse probability censoring weighting (IPCW) is used to create weights which account for the occurrence of censoring. The censoring model assumes for this function is the Kaplan Meier model, i.e. censoring occurs independently of covariates.
List containing data_survival
and model_survival
data_survival
contains the predicted risk of event by the horizon time x_hor
.
model_survival
contains the outputs from the function used to fit the survival submodel, including the estimated parameters of the model.
For a model using cross-validation, model_survival
contains a list of outputs with each
element in the list corresponding to a different cross-validation fold.
Isobel Barrott isobel.barrott@gmail.com
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