obs_calculate function

Calculate Observed Covariate Means and Risk

This internal function calculates the mean observed values of covariates at each time point, as well as mean observed risk.

obs_calculate(
  outcome_name,
  compevent_name,
  compevent2_name,
  censor_name,
  time_name,
  id,
  covnames,
  covtypes,
  comprisk,
  comprisk2,
  censor,
  fitD2,
  fitC,
  outcome_type,
  obs_data,
  ipw_cutoff_quantile,
  ipw_cutoff_value
)

Arguments

• outcome_name: Character string specifying the name of the outcome variable in obs_data.
• compevent_name: Character string specifying the name of the competing event variable in obs_data.
• compevent2_name: Character string specifying the name of the competing event variable in obs_data if competing events are treated as censoring events.
• censor_name: Character string specifying the name of the censoring variable in obs_data.
• time_name: Character string specifying the name of the time variable in obs_data.
• id: Character string specifying the name of the ID variable in obs_data.
• covnames: Vector of character strings specifying the names of the time-varying covariates in obs_data.
• covtypes: Vector of character strings specifying the "type" of each time-varying covariate included in covnames. The possible "types" are: "binary", "normal", "categorical", "bounded normal", "zero-inflated normal", "truncated normal", "absorbing", "categorical time", and "custom".
• comprisk: Logical scalar indicating the presence of a competing event.
• comprisk2: Logical scalar indicating whether competing events are treated as censoring events.
• censor: Logical scalar indicating the presence of a censoring variable in obs_data.
• fitD2: Model fit for the competing event variable if competing events are treated as censoring events.
• fitC: Model fit for the censoring variable.
• outcome_type: Character string specifying the "type" of the outcome. The possible "types" are: "survival", "continuous_eof", and "binary_eof".
• obs_data: Data table containing the observed data.
• ipw_cutoff_quantile: Percentile by which to truncate inverse probability weights.
• ipw_cutoff_value: Cutoff value by which to truncate inverse probability weights.

Returns

A list. Its first entry is a list of mean covariate values at each time point; its second entry is a vector of the mean observed risk (for "survival"

outcome types) or the mean observed outcome (for "continuous_eof" and "binary_eof" outcome types); for "survival" outcome types, its third entry is a vector of mean observed survival.