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Creates a cox model.

Source: R/mv_model_cox.R
mv_model_cox.Rd

Creates a Cox model with all the variables needed to be used in the functions along this package. The Cox model follows

$$S_0(t)^{exp(\beta \cdot X)}$$

so we need to fullfill all the requirements of the model. When we are predicting the survival/risk for a \(X\) we have to center the values with the meas on the derivation data, normally reported inside the articles.

Usage

mv_model_cox(formula, S0 = NULL)

Arguments

formula

Formula of the model to calculate the \(\beta \cdot X\) values, including coefficients and means if needed.

S0

Value of the \(S_0(t)\) function for the time of study.

Value

A model to be used along the package with the next characteristics that could be empty and will be generated with some other functions in the package.

  • formula: Formula of how the \(\beta \cdot X\) will be calculated.

  • S0: Value of the \(S_0(t)\) function for the time of study.

  • predictions_imp: tibble with the predictions for each of the imputed datasets.

  • predictions_agg: tibble with the aggregated predictions for each patient.

  • recal_parameters: tibble with the recalibration parameters needed.

  • results_imp: tibble with the results in each of the imputations.

  • results_agg: tibble with the aggregated results.

Examples

model <- mv_model_cox(
  formula = event ~ 0.5 * (x - 3) + 0.3 * (z - 0.2),
  S0 = 0.98765
)