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Select hyperparameter and obtain the optimal general-gamma or efron model based on AIC and BIC

Source: R/pvEBayes_main_function.R
pvEBayes_tune.Rd

This function performs hyperparameter tuning for the general-gamma or Efron model using AIC or BIC. For a given AE-drug contingency table, the function fits a series of models across a grid of candidate hyperparameter values and computes their AIC and BIC. The models with the lowest AIC or BIC values are selected as the optimal fits.

Usage

pvEBayes_tune(
  contin_table,
  model = "general-gamma",
  alpha_vec = NULL,
  p_vec = NULL,
  c0_vec = NULL,
  use_AIC = TRUE,
  n_posterior_draws = 1000,
  return_all_fit = FALSE,
  return_all_AIC = TRUE,
  return_all_BIC = TRUE,
  tol_ecm = 1e-04,
  rtol_efron = 1e-10,
  E = NULL
)

Arguments

contin_table

an IxJ contingency table showing pairwise counts of adverse events for I AEs (along the rows) and J drugs (along the columns).

model

the model to be tuned. Available models are "general-gamma" and "efron". Default to "general-gamma". Note that the input for model is case-sensitive.

alpha_vec

vector of hyperparameter alpha values to be selected. Alpha is a hyperparameter in general-gamma model which is numeric and between 0 and 1. If is NULL, a default set of alpha values (0, 0.1, 0.3, 0.5, 0.7, 0.9) will be used.

p_vec

vector of hyperparameter p values to be selected. p is a hyperparameter in "efron" model which should be a positive integer. If is NULL, a default set of p values (80, 100, 120, 150, 200) will be used.

c0_vec

vector of hyperparameter c0 values to be selected. c0 is a hyperparameter in "efron" model which should be a positive number. If is NULL, a default set of c0 values (0.001, 0.01, 0.1, 0.2, 0.5) will be used.

use_AIC

logical, indicating whether AIC or BIC is used. Default to be TRUE.

n_posterior_draws

number of posterior draws for each AE-drug combination.

return_all_fit

logical, indicating whether to return all the fitted model under the selection. Default to be FALSE.

return_all_AIC

logical, indicating whether to return AIC values for each fitted model under the selection. Default to be TRUE.

return_all_BIC

logical, indicating whether to return BIC values for each fitted model under the selection. Default to be TRUE.

tol_ecm

a tolerance parameter used for the ECM stopping rule, defined as the absolute change in the joint marginal likelihood between two consecutive iterations. It is used when 'GPS', 'K-gamma' or 'general-gamma' model is fitted. Default to be 1e-4.

rtol_efron

a tolerance parameter used when 'efron' model is fitted. Default to 1e-10. See 'stats::nlminb' for detail.

E

A matrix of expected counts under the null model for the SRS frequency table. If NULL (default), the expected counts are estimated from the SRS data using 'estimate_null_expected_count()'.

Value

The function returns an S3 object of class pvEBayes containing the selected estimated model parameters as well as posterior draws for each AE-drug combination if the number of posterior draws is specified.

References

Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control. 2003; 19(6):716-23.

Schwarz G. Estimating the dimension of a model. The Annals of Statistics. 1978; 1:461-4.

Tan Y, Markatou M and Chakraborty S. Flexible Empirical Bayesian Approaches to Pharmacovigilance for Simultaneous Signal Detection and Signal Strength Estimation in Spontaneous Reporting Systems Data. Statistics in Medicine. 2025; 44: 18-19, https://doi.org/10.1002/sim.70195.

Examples


fit <- pvEBayes_tune(statin2025_44,
  model = "general-gamma",
  alpha_vec = c(0, 0.1, 0.3, 0.5, 0.7, 0.9)
)
#> The alpha value selected under AIC is 0.3,
#> The alpha value selected under BIC is 0.1.
#>   alpha      AIC      BIC num_mixture
#> 1   0.0 4551.612 4697.962          13
#> 2   0.1 3799.011 3990.392          17
#> 3   0.3 3798.874 4001.513          18
#> 4   0.5 3802.753 4016.649          19
#> 5   0.7 3822.367 4081.295          23
#> 6   0.9 3906.526 4323.061          37