[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
[1] "No variability observed in a component. Setting batch size to 1"
$m0a

Bayesian weibull survival model fitted with JointAI

Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ 1, 
    data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)


Number of events: 169 

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

Posterior summary of the shape of the Weibull distribution:
                      Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 312 

$m1a

Bayesian weibull survival model fitted with JointAI

Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ age + 
    sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)


Number of events: 169 

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

Posterior summary of the shape of the Weibull distribution:
                      Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 312 

$m1b

Bayesian weibull survival model fitted with JointAI

Call:
survreg_imp(formula = Surv(futime, I(status != "censored")) ~ 
    age + sex, data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, 
    warn = FALSE, mess = FALSE)


Number of events: 169 

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

Posterior summary of the shape of the Weibull distribution:
                      Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 312 

$m2a

Bayesian weibull survival model fitted with JointAI

Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper, 
    data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE)


Number of events: 169 

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

Posterior summary of the shape of the Weibull distribution:
                      Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 312 

$m3a

Bayesian weibull survival model fitted with JointAI

Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper + 
    sex + age + abs(age - copper) + log(trig), data = PBC2, n.adapt = 5, 
    n.iter = 10, seed = 2020, warn = FALSE, mess = FALSE, trunc = list(trig = c(1e-04, 
        NA)))


Number of events: 169 

Posterior summary:
                  Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)          0  0    0     0          0     NaN    NaN
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 the shape of the Weibull distribution:
                      Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

Number of observations: 312 

$m3b

Bayesian weibull survival model fitted with JointAI

Call:
survreg_imp(formula = Surv(futime, status != "censored") ~ copper + 
    sex + age + abs(age - copper) + log(trig) + (1 | center), 
    data = PBC2, n.adapt = 5, n.iter = 10, seed = 2020, warn = FALSE, 
    mess = FALSE, trunc = list(trig = c(1e-04, NA)))


Number of events: 169 

Posterior summary:
                  Mean SD 2.5% 97.5% tail-prob. GR-crit MCE/SD
(Intercept)          0  0    0     0          0     NaN    NaN
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 the shape of the Weibull distribution:
                      Mean SD 2.5% 97.5% GR-crit MCE/SD
shape_Srv_ftm_stts_cn    0  0    0     0     NaN    NaN


MCMC settings:
Iterations = 6:15
Sample size per chain = 10 
Thinning interval = 1 
Number of chains = 3 

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

