# How can I get standard error using filt.mult.impute?

Now I am struggling with obtaining standard error of hazard ratio.

There are some missing values, so that I am trying to use Hmisc::fit.mult.impute to perform multiple imputations.

Here you can see the sample codes.

#mice::nhanes2
library(survival)
library(mice)
library(tidyverse)
library(Hmisc)
library(rms)
data(nhanes)
set.seed(42)

#Create dummy data /  "time to death" and "death" category to apply coxph
time_death<- as.integer(runif(nrow(nhanes), min = 100, max = 1000))
death  <- as.integer((runif(nrow(nhanes), min = 0, max = 2)))
nhanes <- nhanes %>% mutate(death=death , time_death=time_death)

imp <- mice(nhanes, m = 10, seed = 1, print = FALSE)

coximp <- fit.mult.impute (Surv(time_death,death)~age+bmi+chl,cph,
xtrans=imp , x=TRUE, y=TRUE, surv=TRUE, data = nhanes)

coximp

> coximp
Cox Proportional Hazards Model

fit.mult.impute(formula = Surv(time_death, death) ~ age + bmi +
chl, fitter = cph, xtrans = imp, data = nhanes, x = TRUE,
y = TRUE, surv = TRUE)

Model Tests    Discrimination
Indexes
Obs        25    LR chi2      5.32    R2       0.202
Events     15    d.f.            3    Dxy      0.378
Center 1.3391    Pr(> chi2) 0.1594    g        0.807
Score chi2   5.56    gr       2.248
Pr(> chi2) 0.1516

Coef    S.E.   Wald Z Pr(>|Z|)
age -0.6621 0.6167 -1.07  0.2830
bmi  0.0694 0.1100  0.63  0.5283
chl  0.0037 0.0104  0.36  0.7218

#coximp$coefficients  Here, we can get coefficients(Coef) of each variable (#coximp$coefficients), however, How can I get standard error (SE)?