basecamb (version 1.1.2)

cox.zph.mids: Test cox proportional odds assumption on models using multiple imputation.

Description

Constructs a model and conducts a cox.zph test for each imputation of the data set.

Usage

cox.zph.mids(
  model,
  imputations,
  p_level = 0.05,
  global_only = TRUE,
  return_raw = FALSE,
  p_only = TRUE,
  verbose = TRUE
)

Value

depending on specified options, this function can return

• default: A vector of global p-values

• global_only = FALSE: a data.frame with p-values for all variables plus the global

• return_raw = TRUE: list of cox.zph objects

Arguments

model

cox proportional model to be evaluated

imputations

mids object containing imputations

p_level

value below which violation of proportional odds assumption is assumed. Defaults to .05

global_only

return global p-value only. Implies p_only to be TRUE

return_raw

return cox.zph objects in a list. If TRUE, function will not return anything else

p_only

returns p-values of test only. If FALSE returns Chi² and degrees of freedom as well

verbose

Set to FALSE to deactivate messages

Author

J. Peter Marquardt

Examples

data <- data.frame(time = 101:200, status = rep(c(0,1), 50), pred = rep(c(1:9, NA), 10))
imputed_data <- mice::mice(data)
cox_mod <- Hmisc::fit.mult.impute(survival::Surv(time, status) ~ pred,
fitter = rms::cph, xtrans = imputed_data)
cox.zph.mids(cox_mod, imputed_data)