# Pseudo Likelihood Ratio Test for determining significant AE-Drug pairs under Poisson and zero-inflated Poisson models for pharmacovigilance ```r pvlrt( contin_table, nsim = 10000, omega_vec = NULL, zi_prob = NULL, no_zi_idx = NULL, test_drug_idx = seq_len(max(ncol(contin_table) - 1, 0)), drug_class_idx = list(test_drug_idx), grouped_omega_est = FALSE, test_zi = NULL, test_omega = NULL, pval_ineq_strict = FALSE, parametrization = "rrr", null_boot_type = "parametric", is_zi_structural = TRUE, return_overall_loglik = TRUE, ... ) ``` ## Arguments - `contin_table`: IxJ contingency table showing pairwise counts of adverse events for I AE (along the rows) and J Drugs (along the columns) - `nsim`: Number of simulated null contingency table to use for computing the p-value of the test - `zi_prob, omega_vec`: Alias, determining zero inflation probabilities in the model. Can be a vector, providing different zero inflation probabilities for different drugs, or a scalar, providing the common zero inflation probability for all drugs. If NULL (default), then is estimated from the data. See also the description of the argument `grouped_omega_est`. If `omega_vec = rep(0, ncol(contin_table))`, then test reduces to an ordinary (non-zero inflated) Poisson test. NOTE: `zi_prob` and `omega_vec` are alias. - `no_zi_idx`: List of pairs (i, j) where zero inflation is not allowed. To specify the entirety i-th row (or j-th column) use c(i, 0) (or c(0, j)). See examples. - `test_drug_idx`: integer (position) or character (names) vector indicating the columns (drugs indices or drug labels) of contin_table to be tested for signal using LRT. Defaults to all except the last columns (which is typically the column for "Other Drugs"). - `drug_class_idx`: a list, with the h-th entry providing the h-th group/class of drugs. Relevant only for drugs used for testing (supplied through `test_drug_idx`). By default all drugs provided in `test_drug_idx` are included in the same class, which is ensured by supplying `drug_class_idx = list(test_drug_idx)`. If more than one drug is present in a class, an extended LRT is performed for the class (which ensures the correct Type I error rate is preserved). If `drug_class_idx` excludes any drug present in `test_drug_idx`, each remaining drug is made to form its own class. See examples. - `grouped_omega_est`: Logical. When performing a test with grouped drug classes (extended LRT), should the estimated zero-inflation parameter "omega" reflect the corresponding grouping? If TRUE, then the estimated omegas are obtained by combining columns from the same group, and if FALSE (default), then omegas are estimated separately for each drug (column) irrespective of the groups specified through `drug_class_idx`. Ignored if `omega_vec` is supplied/non-`NULL` (i.e., not estimated). - `test_zi, test_omega`: logical indicators specifying whether to perform a pseudo likelihood ratio test for zero inflation. Defaults to FALSE. Ignored if `omega_vec` is supplied (is non-NULL). Defaults to FALSE. NOTE: `test_omega` and `test_zi` are aliases. - `pval_ineq_strict`: logical. Use a strict inequality in the definition of the p-values? Defaults to FALSE. - `parametrization`: Type of parametrization to use in the LR test. Available choices are "rrr", "lambda", "rr", and "p-q". The relative reporting ratio (default) parametrization of the test is used when when `parametrization %in% c("rrr", "lambda")`, and the reporting rate parametrization is used otherwise. NOTE: zero inflation can be handled only for the relative reporting ratio parametrization. - `null_boot_type`: Type of bootstrap sampling to perform for generating null resamples. Available choices are "parametric" (default) and "non-parametric". NOTE: zero inflation is not handled properly in a non-parametric bootstrap resampling. - `is_zi_structural`: logical. Do the inflated zeros correspond to structural zeros (indicating impossible AE-Drug combination)? This determines how the bootstrap null zero-inflation indicators are generated. If TRUE (default), then then the null zero-inflation random indicators are randomly generated using the (estimated) **conditional** probabilities of zero inflation given observed counts. If FALSE, then they are generated using the **marginal** (drug-specific) estimated probabilities of zero-inflation. - `return_overall_loglik`: logical. Return overall log-likelihood for the table? This is needed if `logLik` method is to be used. - `...`: additional arguments. Currently unused. ## Returns The function returns an S3 object of class `pvlrt` containing test results. A `pvlrt` object is simply a re-classified matrix containing log likelihood ratio test statistics for cells in the input contingency table, with various other test and input data information (including p-values, estimated zero inflation parameters, overall log-likelihood etc.) embedded as attributes. The following S3 methods and functions are available for an `pvlrt` object: Various post processing methods for `pvlrt` objects are available. This includes: * bubbleplot_pvlrt * extract_AE_names * extract_Drug_names * extract_lrstat_matrix * extract_n_matrix * extract_p_value_matrix * extract_significant_pairs * extract_zi_probability * heatmap_pvlrt * lrt_poisson * lrt_vanilla_poisson * lrt_zi_poisson * r_contin_table_zip * set_AE_names * set_Drug_names * print.pvlrt * plot.pvlrt * summary.pvlrt * logLik.pvlrt * as.matrix.pvlrt ## Examples ```r data("statin46") # 500 bootstrap iterations (nsim) in each example below # are for quick demonstration only -- # we recommended setting nsim to 10000 (default) or bigger # no grouping -- each drug forms its own class, # default "rrr" (lambda) parametrization, possible zi, # null distribution evaluated using parametric bootstrap test1 <- pvlrt(statin46, nsim = 500) test1 ## extract the observed LRT statistic extract_lrstat_matrix(test1) ## extract the estimated omegas extract_zi_probability(test1) # grouped drugs -- # group 1: drug 1, drug 2 # group 2: drug 3, drug 4 # drug 5, 6, in their own groups ## 7 is not tested, so excluded from test_drug_idx (default) ## if needed, include 7 in test_drug_idx drug_groups <- list(c(1, 2), c(3, 4)) ## 5, 6 not present in drug_groups, so each will form their own groups set.seed(50) ## test2 <- pvlrt(statin46, drug_class_idx = drug_groups, nsim = 500) test2 # instead of column positions column names can also be supplied # in drug_class_idx and/or test_drug_idx ## column name version of drug_groups drug_groups_colnames <- lapply(drug_groups, function(i) colnames(statin46)[i]) test_drug_colnames <- head(colnames(statin46), -1) set.seed(50) test20 <- pvlrt( statin46, test_drug_idx = test_drug_colnames, drug_class_idx = drug_groups_colnames, nsim = 500 ) test20 all.equal(test2, test20) # specify no zero inflation at the entirety of the last row and the last column no_zi_idx <- list(c(nrow(statin46), 0), c(0, ncol(statin46))) test3 <- pvlrt(statin46, no_zi_idx = no_zi_idx, nsim = 500) test3 # use non-parametric bootstrap to evaluate the null distribution # takes longer, due to computational costs with non-parametric # contigency table generation test4 <- pvlrt(statin46, null_boot_type = "non-parametric", nsim = 500) test4 # test zi probabilities (omegas) test5 <- pvlrt(statin46, test_omega = TRUE, nsim = 500) test5 ```

pvlrt function