
Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2, 
    n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age + 
    sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
      age sexfemale 
        0         0 

Call:
coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age + 
    sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian proportional hazards model for "Surv(futime, I(status != "censored"))" 


Coefficients:
      age sexfemale 
        0         0 

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper, 
    data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
copper 
     0 

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper + 
    sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, 
        NA)))

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
           copper         sexfemale               age abs(age - copper) 
                0                 0                 0                 0 
        log(trig) 
                0 

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper + 
    sex + age + abs(age - copper) + log(trig) + (1 | center), 
    data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE, trunc = list(trig = c(1e-04, NA)))

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
           copper         sexfemale               age abs(age - copper) 
                0                 0                 0                 0 
        log(trig) 
                0 

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age + 
    sex + trt + albumin + platelet + stage + (1 | id), data = PBC, 
    n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, 
    timevar = "day")

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
       age  sexfemale trtplacebo    albumin   platelet    stage.L    stage.Q 
         0          0          0          0          0          0          0 
   stage.C 
         0 

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age + 
    sex * trt + albumin + log(platelet) + (1 | id), data = PBC, 
    n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, 
    timevar = "day")

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
                 age            sexfemale           trtplacebo 
                   0                    0                    0 
sexfemale:trtplacebo              albumin        log(platelet) 
                   0                    0                    0 

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age + 
    sex + albumin + log(platelet) + (1 | id) + (1 | center), 
    data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE, timevar = "day")

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
          age     sexfemale       albumin log(platelet) 
            0             0             0             0 

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age + 
    sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center), 
    data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE, timevar = "day")

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
                  age             sexfemale               albumin 
                    0                     0                     0 
ns(platelet, df = 2)1 ns(platelet, df = 2)2 
                    0                     0 
$m0a

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ 1, data = PBC2, 
    n.adapt = 1, n.iter = 4, seed = 2020, warn = FALSE, mess = FALSE)

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 

$m1a

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age + 
    sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
      age sexfemale 
        0         0 

$m1b

Call:
coxph_imp(formula = Surv(futime, I(status != "censored")) ~ age + 
    sex, data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian proportional hazards model for "Surv(futime, I(status != "censored"))" 


Coefficients:
      age sexfemale 
        0         0 

$m2a

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper, 
    data = PBC2, n.adapt = 2, n.iter = 4, seed = 2020, warn = FALSE, 
    mess = FALSE)

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
copper 
     0 

$m3a

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper + 
    sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 2, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, 
        NA)))

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
           copper         sexfemale               age abs(age - copper) 
                0                 0                 0                 0 
        log(trig) 
                0 

$m3b

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ copper + 
    sex + age + abs(age - copper) + log(trig) + (1 | center), 
    data = PBC2, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE, trunc = list(trig = c(1e-04, NA)))

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
           copper         sexfemale               age abs(age - copper) 
                0                 0                 0                 0 
        log(trig) 
                0 

$m4a

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age + 
    sex + trt + albumin + platelet + stage + (1 | id), data = PBC, 
    n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, 
    timevar = "day")

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
       age  sexfemale trtplacebo    albumin   platelet    stage.L    stage.Q 
         0          0          0          0          0          0          0 
   stage.C 
         0 

$m4b

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age + 
    sex * trt + albumin + log(platelet) + (1 | id), data = PBC, 
    n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, 
    timevar = "day")

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
                 age            sexfemale           trtplacebo 
                   0                    0                    0 
sexfemale:trtplacebo              albumin        log(platelet) 
                   0                    0                    0 

$m4c

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age + 
    sex + albumin + log(platelet) + (1 | id) + (1 | center), 
    data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE, timevar = "day")

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
          age     sexfemale       albumin log(platelet) 
            0             0             0             0 

$m4d

Call:
coxph_imp(formula = Surv(futime, status != "censored") ~ age + 
    sex + albumin + ns(platelet, df = 2) + (1 | id) + (1 | center), 
    data = PBC, n.adapt = 2, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE, timevar = "day")

 Bayesian proportional hazards model for "Surv(futime, status != "censored")" 


Coefficients:
                  age             sexfemale               albumin 
                    0                     0                     0 
ns(platelet, df = 2)1 ns(platelet, df = 2)2 
                    0                     0 

