Fit a survival sub-model as part of a landmarking analysis

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
)

Arguments

data	Data frame containing covariates and time-to-event data, one row for each individual.
individual_id	Character string specifying the column name in data which contains the individual identifiers
cv_name	Character string specifying the column name in data that indicates cross-validation fold. If no cross-validation is needed, set this parameter to NA.
covariates	Vector of character strings specifying the column names in data_long which correspond to the covariates
event_time	Character string specifying the column name in data which contains the event time
event_status	Character string specifying the column name in data which contains the event status (where 0=censoring, 1=event of interest, if there are competing events these are labelled 2 or above). Events at time x_hor should be labelled censored.
survival_submodel	Character string specifying which survival submodel to use. Three options: the standard Cox model i.e. no competing risks ("standard_cox"), the cause-specific regression model ("cause_specific"), or the Fine Gray regression model ("fine_gray")
x_hor	Numeric specifying the horizon time(s)

Value

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.

Details

This function fits the survival model from the landmark model framework. The individuals are censored at the time horizon x_hor and the survival model is fitted with covariates specified in parameter covariates.

For the survival model, 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.

Author

Isobel Barrott isobel.barrott@gmail.com