f2<-stan_jm(formulaLong =logTSH~time+(time|code),dataLong =dataLong,
formulaEvent = Surv(surv_time,incidenceDM)~sex15_1+age+BMI+fbs+bs2HR+waist+FHD,
dataEvent=data.cox,time_var = “time”,chains=3,iter=1000,assoc=c(“etavalue”,“etaslope”))
SAMPLING FOR MODEL ‘jm’ NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0.025 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 250 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
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Chain 1:
Chain 1: Elapsed Time: 2120.88 seconds (Warm-up)
Chain 1: 1086.63 seconds (Sampling)
Chain 1: 3207.51 seconds (Total)
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Warning messages:
1: The largest R-hat is 1.09, indicating chains have not mixed.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#r-hat
2: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#bulk-ess
3: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#tail-ess
summary(f2,“assoc”)
Model Info:
function: stan_jm
formula (Long1): logTSH ~ time + (time | code)
family (Long1): gaussian [identity]
formula (Event): Surv(surv_time, incidenceDM) ~ sex15_1 + age + BMI + fbs + bs2HR +
waist + FHD
baseline hazard: bs
assoc: etavalue (Long1), etaslope (Long1)
algorithm: sampling
priors: see help(‘prior_summary’)
sample: 1500 (posterior sample size)
num obs: 6980 (Long1)
num subjects: 1745
num events: 42 (2.4%)
groups: code (1745)
runtime: 162.1 mins
Estimates:
mean sd 2.5% 25% 50% 75% 97.5%
Assoc|Long1|etavalue -5.380 2.336 -9.712 -6.978 -5.553 -3.879 -0.925
Assoc|Long1|etaslope -45.073 21.607 -87.776 -59.362 -46.655 -30.876 -5.527
Diagnostics:
mcse Rhat n_eff
Assoc|Long1|etavalue 0.681 1.242 12
Assoc|Long1|etaslope 5.998 1.224 13
For each parameter, mcse is Monte Carlo standard error, n_eff is a crude measure of effective sample size, and Rhat is the potential scale reduction factor on split chains (at convergence Rhat=1).