Adds data rows to a
blrm_trial()object (add_data argument)Replaces data of a
blrm_trial()object (data argument)Sets the prior of a
blrm_trial()object (... argument will be passed to blrm_exnex)
Usage
# S3 method for class 'blrm_trial'
update(object, ...)Examples
## Setting up dummy sampling for fast execution of example
## Please use 4 chains and 100x more warmup & iter in practice
.user_mc_options <- options(
OncoBayes2.MC.warmup = 10, OncoBayes2.MC.iter = 20, OncoBayes2.MC.chains = 1,
OncoBayes2.MC.save_warmup = FALSE
)
# the combo2_trial example demonstrates the use of add_data of
# update.blrmfit
example_model("combo2_trial")
#> Running combo2_trial example:
#> library(tibble)
#> library(dplyr)
#> library(tidyr)
#>
#> # Combo2 example using blrm_trial functionality
#>
#> # construct initial blrm_trial object from built-in example datasets
#> combo2_trial_setup <- blrm_trial(
#> data = hist_combo2,
#> dose_info = dose_info_combo2,
#> drug_info = drug_info_combo2,
#> simplified_prior = FALSE
#> )
#>
#> # summary of dimensionality of data structures
#> dims <- summary(combo2_trial_setup, "dimensionality")
#>
#> # Fit the initial model with the historical data and fully specified prior
#>
#>
#> combo2_trial_start <- update(
#> combo2_trial_setup,
#> ## bivariate normal prior for drug A and drug B of intercept and
#> ## log-slope
#> prior_EX_mu_comp =
#> replicate(2,
#> mixmvnorm(c(1,
#> logit(0.2), 0,
#> diag(c(2^2, 1))))
#> , FALSE),
#> prior_EX_tau_comp =
#> replicate(2,
#> mixmvnorm(c(1,
#> log(0.25), log(0.125),
#> diag(c(log(4)/1.96, log(4)/1.96)^2)))
#> , FALSE),
#> prior_EX_mu_inter = mixmvnorm(c(1, 0, 1.121^2)),
#> prior_EX_tau_inter = mixmvnorm(c(1, log(0.125), (log(4) / 1.96)^2)),
#> prior_is_EXNEX_comp = c(FALSE, FALSE),
#> prior_is_EXNEX_inter = FALSE,
#> prior_EX_prob_comp = matrix(1,
#> nrow = dims$num_groups,
#> ncol = 2
#> ),
#> prior_EX_prob_inter = matrix(1,
#> nrow = nlevels(dose_info_combo2$group_id),
#> ncol = 1
#> ),
#> prior_tau_dist = 1
#> )
#>
#> # print summary of prior specification
#> prior_summary(combo2_trial_start)
#>
#> # summarize inference at observed dose levels
#> summary(combo2_trial_start, "data_prediction")
#>
#> # summarize inference at specified dose levels
#> summary(combo2_trial_start, "dose_prediction")
#>
#>
#> # Update again with new data
#>
#> # using update() with data argument supplied
#> # dem <- update(combo2_trial_start, data = codata_combo2)
#>
#> # alternate way using update() with add_data argument for
#> # new observations only (those collected after the trial
#> # design stage).
#> new_data <- filter(codata_combo2, cohort_time > 0)
#>
#> combo2_trial <- update(combo2_trial_start, add_data = new_data)
#>
#> summary(combo2_trial, "data") # cohort_time is tracked
#> summary(combo2_trial, "data_prediction")
#> summary(combo2_trial, "dose_prediction")
#>
#> rm(dims, new_data)
#>
#> No stratum defined - assigning all groups to single stratum "all"
#> Please configure blrm_exnex using the update() function.
#> Warning: There were 10 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
#> https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is NA, 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
#> Warning: 11 out of 42 ewoc metrics have not converged (some Rhats are > 1.1).
#> Be careful when analysing the results! It is recommended to run
#> more iterations and/or setting stronger priors.
#> You may call "summary(trial, summarize='ewoc_check', ...)" for more diagnostic details.
#> Please call "help('blrm_trial', help_type='summary')" for further documentation.
#> Warning: 22 out of 42 ewoc metrics are within the 95% MCMC error of the decision boundary.
#> Be careful when using the imprecise ewoc estimates! It is recommended to run
#> more iterations and review doses close to critical thresholds.
#> You may call "summary(trial, summarize='ewoc_check', ...)" for more diagnostic details.
#> Please call "help('blrm_trial', help_type='summary')" for further documentation.
#> stratum_id not given, but only one stratum defined. Assigning first stratum.
#> Warning: The largest R-hat is NA, 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
#> Warning: 16 out of 42 ewoc metrics have not converged (some Rhats are > 1.1).
#> Be careful when analysing the results! It is recommended to run
#> more iterations and/or setting stronger priors.
#> You may call "summary(trial, summarize='ewoc_check', ...)" for more diagnostic details.
#> Please call "help('blrm_trial', help_type='summary')" for further documentation.
#> Warning: 7 out of 42 ewoc metrics are within the 95% MCMC error of the decision boundary.
#> Be careful when using the imprecise ewoc estimates! It is recommended to run
#> more iterations and review doses close to critical thresholds.
#> You may call "summary(trial, summarize='ewoc_check', ...)" for more diagnostic details.
#> Please call "help('blrm_trial', help_type='summary')" for further documentation.
## Recover user set sampling defaults
options(.user_mc_options)