This function takes a single existing (previously estimated)
prediction model, and apply various model discrete model updating methods
(see Su et al. 2018) to tailor the model to a new dataset.
The type of updating method is selected with the update_type
parameter, with options: "intercept_update", "recalibration" and "refit".
"intercept_update" corrects the overall calibration-in-the-large of the
model, through altering the model intercept (or baseline hazard) to suit
the new dataset. This is achieved by fitting a logistic model (if the
existing model is of type logistic) or time-to-event model (if the existing
model if of type survival) to the new dataset, with the linear predictor as
the only covariate, with the coefficient fixed at unity (i.e. as an
offset). "recalibration" corrects the calibration-in-the-large and any
under/over-fitting, by fitting a logistic model (if the existing model is
of type logistic) or time-to-event model (if the existing model if of type
survival) to the new dataset, with the linear predictor as the only
covariate. Finally, "refit" takes the original model structure and
re-estimates all coefficients; this has the effect as re-developing the
original model in the new data.
new_data should be a data.frame, where each row should be an
observation (e.g. patient) and each variable/column should be a predictor
variable. The predictor variables need to include (as a minimum) all of the
predictor variables that are included in the existing prediction model
(i.e., each of the variable names supplied to
pred_input_info, through the model_info parameter,
must match the name of a variables in new_data).
Any factor variables within new_data must be converted to dummy
(0/1) variables before calling this function. dummy_vars can
help with this. See pred_predict for examples.
binary_outcome, survival_time and event_indicator are
used to specify the outcome variable(s) within new_data (use
binary_outcome if x$model_type = "logistic", or use
survival_time and event_indicator if x$model_type =
"survival").