Print pooled Cox regression results

Print pooled Cox regression results

Usage

# S3 method for class 'mi_link_pool_survreg'
print(x, digits = max(3L, getOption("digits") - 2L), ...)

Arguments

x

An object of class mi_link_pool_survreg, typically returned by mi_with() for a survMixBayes fit.

digits

the number of significant digits to print.

...

further arguments (unused).

Value

The input x, invisibly.

Examples

# \donttest{
set.seed(301)
n <- 150
trt <- rbinom(n, 1, 0.5)

# Simulate Weibull AFT data
true_time <- rweibull(n, shape = 1.5, scale = exp(1 + 0.8 * trt))
cens_time <- rexp(n, rate = 0.1)
true_obs_time <- pmin(true_time, cens_time)
true_status <- as.integer(true_time <= cens_time)

# Induce linkage mismatch errors in approximately 20% of records
is_mismatch <- rbinom(n, 1, 0.2)
obs_time <- true_obs_time
obs_status <- true_status
mismatch_idx <- which(is_mismatch == 1)

shuffled <- sample(mismatch_idx)
obs_time[mismatch_idx] <- obs_time[shuffled]
obs_status[mismatch_idx] <- obs_status[shuffled]

linked_df <- data.frame(time = obs_time, status = obs_status, trt = trt)
adj <- adjMixBayes(linked.data = linked_df)

fit <- plsurvreg(
  survival::Surv(time, status) ~ trt,
  dist = "weibull",
  adjustment = adj,
  control = list(iterations = 200, burnin.iterations = 100, seed = 123)
)
#> 
#> SAMPLING FOR MODEL 'survMixBayes_weibull' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 9.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.94 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1:          three stages of adaptation as currently configured.
#> Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1:          the given number of warmup iterations:
#> Chain 1:            init_buffer = 15
#> Chain 1:            adapt_window = 75
#> Chain 1:            term_buffer = 10
#> Chain 1: 
#> Chain 1: Iteration:   1 / 200 [  0%]  (Warmup)
#> Chain 1: Iteration:  20 / 200 [ 10%]  (Warmup)
#> Chain 1: Iteration:  40 / 200 [ 20%]  (Warmup)
#> Chain 1: Iteration:  60 / 200 [ 30%]  (Warmup)
#> Chain 1: Iteration:  80 / 200 [ 40%]  (Warmup)
#> Chain 1: Iteration: 100 / 200 [ 50%]  (Warmup)
#> Chain 1: Iteration: 101 / 200 [ 50%]  (Sampling)
#> Chain 1: Iteration: 120 / 200 [ 60%]  (Sampling)
#> Chain 1: Iteration: 140 / 200 [ 70%]  (Sampling)
#> Chain 1: Iteration: 160 / 200 [ 80%]  (Sampling)
#> Chain 1: Iteration: 180 / 200 [ 90%]  (Sampling)
#> Chain 1: Iteration: 200 / 200 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.618 seconds (Warm-up)
#> Chain 1:                0.488 seconds (Sampling)
#> Chain 1:                1.106 seconds (Total)
#> Chain 1: 
#> Warning: The largest R-hat is 1.18, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: 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
#> Warning: 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
#> 
#>     ......................................................................................
#>     . Method                         Time (sec)           Status                         . 
#>     ......................................................................................
#>     . ECR-ITERATIVE-1                0.095                Converged (2 iterations)       . 
#>     ......................................................................................
#> 
#>     Relabelling all methods according to method ECR-ITERATIVE-1 ... done!
#>     Retrieve the 1 permutation arrays by typing:
#>         [...]$permutations$"ECR-ITERATIVE-1"
#>     Retrieve the 1 best clusterings: [...]$clusters
#>     Retrieve the 1 CPU times: [...]$timings
#>     Retrieve the 1 X 1 similarity matrix: [...]$similarity
#>     Label switching finished. Total time: 0.1 seconds. 

pooled_obj <- mi_with(
  object = fit,
  data = linked_df,
  formula = survival::Surv(time, status) ~ trt
)

print(pooled_obj, digits = 4)
#> Pooled Cox regression results across posterior match classifications:
#>   Retained imputations (m): 100 
#>   Mixture model distribution: weibull 
#>   Refit model: coxph
#> 
#>     Estimate Std.Error  CI.lwr  CI.upr       df
#> trt   -0.972     0.403 -1.7666 -0.1774 205.7726
# }