psfmi_coxr_fw function

Forward selection of Cox regression models across multiply imputed data.

psfmi_coxr_fw Forward selection of Cox regression models across multiply imputed data using selection methods RR, D1, D2 and MPR. Function is called by psfmi_coxr.

psfmi_coxr_fw(
  data,
  nimp,
  impvar,
  status,
  time,
  p.crit,
  P,
  keep.P,
  method,
  strata.P
)

Arguments

• data: Data frame with stacked multiple imputed datasets. The original dataset that contains missing values must be excluded from the dataset. The imputed datasets must be distinguished by an imputation variable, specified under impvar, and starting by 1.
• nimp: A numerical scalar. Number of imputed datasets. Default is 5.
• impvar: A character vector. Name of the variable that distinguishes the imputed datasets.
• status: The status variable, normally 0=censoring, 1=event.
• time: Follow up time.
• p.crit: A numerical scalar. P-value selection criterium. A value of 1 provides the pooled model without selection.
• P: Character vector with the names of the predictor variables. At least one predictor variable has to be defined. Give predictors unique names and do not use predictor name combinations with numbers as, age2, BMI10, etc.
• keep.P: A single string or a vector of strings including the variables that are forced in the model during predictor selection. All type of variables are allowed.
• method: A character vector to indicate the pooling method for p-values to pool the total model or used during predictor selection. This can be "RR", D1", "D2" or "MPR". See details for more information. Default is "RR".
• strata.P: A single string including the strata variable.

Author(s)

Martijn Heymans, 2020