$m0a

Bayesian proportional hazards model fitted with JointAI

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


Number of events: 169 

Posterior summary:
     Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD

Posterior summary of other parameters:
                            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[2]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[3]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[4]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[5]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[6]    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 2:5
Sample size per chain = 4 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 312 

$m1a

Bayesian proportional hazards model fitted with JointAI

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


Number of events: 169 

Posterior summary:
          Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age          0  0    0     0          0     NaN    NaN
sexfemale    0  0    0     0          0     NaN    NaN

Posterior summary of other parameters:
                            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[2]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[3]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[4]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[5]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[6]    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 3:6
Sample size per chain = 4 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 312 

$m1b

Bayesian proportional hazards model fitted with JointAI

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)


Number of events: 169 

Posterior summary:
          Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age          0  0    0     0          0     NaN    NaN
sexfemale    0  0    0     0          0     NaN    NaN

Posterior summary of other parameters:
                            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[2]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[3]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[4]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[5]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[6]    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 3:6
Sample size per chain = 4 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 312 

$m2a

Bayesian proportional hazards model fitted with JointAI

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


Number of events: 169 

Posterior summary:
       Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper    0  0    0     0          0     NaN    NaN

Posterior summary of other parameters:
                            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[2]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[3]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[4]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[5]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[6]    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 3:6
Sample size per chain = 4 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 312 

$m3a

Bayesian proportional hazards model fitted with JointAI

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


Number of events: 169 

Posterior summary:
                  Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper               0  0    0     0          0     NaN    NaN
sexfemale            0  0    0     0          0     NaN    NaN
age                  0  0    0     0          0     NaN    NaN
abs(age - copper)    0  0    0     0          0     NaN    NaN
log(trig)            0  0    0     0          0     NaN    NaN

Posterior summary of other parameters:
                            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[2]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[3]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[4]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[5]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[6]    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 3:12
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 312 

$m3b

Bayesian proportional hazards model fitted with JointAI

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, trunc = list(trig = c(1e-04, 
        NA)), seed = 2020, warn = FALSE, mess = FALSE)


Number of events: 169 

Posterior summary:
                  Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
copper               0  0    0     0          0     NaN    NaN
sexfemale            0  0    0     0          0     NaN    NaN
age                  0  0    0     0          0     NaN    NaN
abs(age - copper)    0  0    0     0          0     NaN    NaN
log(trig)            0  0    0     0          0     NaN    NaN

Posterior summary of random effects covariance matrix:
                              Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Srv_ftm_stts_cn_center[1,1]    0  0    0     0                NaN    NaN

Posterior summary of other parameters:
                            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[2]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[3]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[4]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[5]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[6]    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 3:12
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 312 
Number of groups:
 - center: 10

$m4a

Bayesian proportional hazards model fitted with JointAI

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")


Number of events: 169 

Posterior summary:
           Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age           0  0    0     0          0     NaN    NaN
sexfemale     0  0    0     0          0     NaN    NaN
trtplacebo    0  0    0     0          0     NaN    NaN
albumin       0  0    0     0          0     NaN    NaN
platelet      0  0    0     0          0     NaN    NaN
stage.L       0  0    0     0          0     NaN    NaN
stage.Q       0  0    0     0          0     NaN    NaN
stage.C       0  0    0     0          0     NaN    NaN

Posterior summary of other parameters:
                            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[2]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[3]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[4]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[5]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[6]    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 3:12
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 2257 
Number of groups:
 - id: 312

$m4b

Bayesian proportional hazards model fitted with JointAI

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")


Number of events: 169 

Posterior summary:
                     Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age                     0  0    0     0          0     NaN    NaN
sexfemale               0  0    0     0          0     NaN    NaN
trtplacebo              0  0    0     0          0     NaN    NaN
sexfemale:trtplacebo    0  0    0     0          0     NaN    NaN
albumin                 0  0    0     0          0     NaN    NaN
log(platelet)           0  0    0     0          0     NaN    NaN

Posterior summary of other parameters:
                            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[2]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[3]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[4]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[5]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[6]    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 3:12
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 2257 
Number of groups:
 - id: 312

$m4c

Bayesian proportional hazards model fitted with JointAI

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")


Number of events: 169 

Posterior summary:
              Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age              0  0    0     0          0     NaN    NaN
sexfemale        0  0    0     0          0     NaN    NaN
albumin          0  0    0     0          0     NaN    NaN
log(platelet)    0  0    0     0          0     NaN    NaN

Posterior summary of random effects covariance matrix:
                              Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Srv_ftm_stts_cn_center[1,1]    0  0    0     0                NaN    NaN

Posterior summary of other parameters:
                            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[2]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[3]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[4]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[5]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[6]    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 3:12
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 2257 
Number of groups:
 - center: 10
 - id: 312

$m4d

Bayesian proportional hazards model fitted with JointAI

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")


Number of events: 169 

Posterior summary:
                      Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
age                      0  0    0     0          0     NaN    NaN
sexfemale                0  0    0     0          0     NaN    NaN
albumin                  0  0    0     0          0     NaN    NaN
ns(platelet, df = 2)1    0  0    0     0          0     NaN    NaN
ns(platelet, df = 2)2    0  0    0     0          0     NaN    NaN

Posterior summary of random effects covariance matrix:
                              Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
D_Srv_ftm_stts_cn_center[1,1]    0  0    0     0                NaN    NaN

Posterior summary of other parameters:
                            Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
beta_Bh0_Srv_ftm_stts_cn[1]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[2]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[3]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[4]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[5]    0  0    0     0          0     NaN    NaN
beta_Bh0_Srv_ftm_stts_cn[6]    0  0    0     0          0     NaN    NaN


MCMC settings:
Iterations = 3:12
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 2257 
Number of groups:
 - center: 10
 - id: 312

