convert_raw_to_contin_table function

Convert raw AE-Drug incidence data into a contingency table

convert_raw_to_contin_table(
  rawdata,
  Drug_col_name = "DRUG",
  AE_col_name = "AE",
  id_col_name = "CASEID",
  count_col_name = "COUNT",
  aggregated = FALSE,
  create_other_Drug_col = FALSE,
  other_Drug_excludes = NULL,
  other_Drug_colname = "Other_Drug",
  create_other_AE_row = FALSE,
  other_AE_excludes = NULL,
  other_AE_rowname = "Other_AE",
  ...
)

Arguments

• rawdata: a data.frame or an object that can be converted to a data.frame. Must contain 3 columns (i) DRUG: the drug names/labels, (ii) AE: the AE names, and either (iii) CASEID: case ids corresponding to each combination of AE and DRUG, (if aggregated is FALSE), or (iii') COUNT: the total number of incidences of each AE and DRUG combination (if aggregated is TRUE). If these columns are named differently in rawdata, supply the appropriate column names through Drug_col_name, AE_col_name, id_col_name and count_col_name.
• Drug_col_name, AE_col_name, id_col_name, count_col_name: Drug, AE, case id and count column names in rawdata. Defaults to DRUG, AE, CASEID and COUNT.
• aggregated: logical. Has the incidences been already aggregated/summarized into counts in rawdata? If TRUE then then COUNT column is used to produce the output contingency table. If FALSE (default) incidences are first aggregated into counts before converting to contingency tables.
• create_other_Drug_col: logical. Add a column in the contingency table for "Other Drugs"? This column plays the role of a "baseline" group of drugs that typically do not indicate an adverse event association with the signal of interest. Care should be taken while determining which drugs to include in this group; See Ding et al (2020) for guidance.
• other_Drug_excludes: character vector cataloging Drugs that are NOT to be included in the column for Other Drugs. If NULL (default) then then no Drugs are included in Other Drugs (i.e., other_Drug_excludes contains all Drugs in the raw data). Ignored if create_other_Drug_col = FALSE.
• other_Drug_colname: character. Row name for the "Other Drug" column created. Ignored if create_other_Drug_col = FALSE.
• create_other_AE_row: logical. Add a row in the contingency table for "Other AEs"? This can aid computation in situations where there are certain AEs of primary interest. See other_AE_excludes for details on how to specify the "Other AE" row.
• other_AE_excludes: character vector cataloging AEs that are NOT to be included in the row for Other AEs. If NULL (default) then then no AEs are included in Other AEs (i.e., other_AE_excludes contains all AEs in the raw data). Ignored if create_other_AE_row = FALSE.
• other_AE_rowname: character. Row name for the "Other AE" row created. Defaults to "Other AE". Ignored if create_other_AE_row = FALSE.
• ...: unused.

Details

This is a convenience function that creates a contingency table cataloging counts of AE-Drug incidences from a raw Drug/AE incidence data frame. It accepts both raw incidence data (each row is one incidence of a Drug-AE combination, indexed by case ids) and summarized count data (each row catalogs the total counts of incidences of each Drug-AE pair). The output is a matrix (contingency table) enumerating total count of cases for each pair of AE (along the rows) and drug (along the columns) with appropriately specified row and column names, and can be passed to a pvlrt() call. See the examples for more details.

The output can be fed into pvlrt or its wrappers as contin_table

Examples

# convert to contingency table form incidence (non-aggregated) raw data
# AE subset = AEs in statin46
# Durg subset = union of statin46 and gbca drugs
tab1 <- convert_raw_to_contin_table(
  rawdata = faers22q3raw,
  Drug_col_name = "DRUG",
  AE_col_name = "AE",
  id_col_name = "CASEID",
  aggregated = FALSE,
  other_AE_excludes = rownames(statin46),
  other_Drug_excludes = union(colnames(gbca), colnames(statin)),
  create_other_Drug_col = TRUE,
  create_other_AE_row = FALSE
)

# convert to contingency table AFTER aggregating and counting
# the total number of incidences of each (AE, Drug) pair
## Same AE and Drug subsets as before
## aggregation (counting) done using data.table dt[i, j, by] syntax
## uses magrittr %>% pipe
tab2 <- data.table::as.data.table(
  faers22q3raw
)[
  ,
  .(COUNT = length(unique(CASEID))),
  by = .(DRUG, AE)
] %>%
  convert_raw_to_contin_table(
    Drug_col_name = "DRUG",
    AE_col_name = "AE",
    count_col_name = "COUNT",
    aggregated = TRUE,
    other_AE_excludes = rownames(statin46),
    other_Drug_excludes = union(colnames(gbca), colnames(statin)),
    create_other_Drug_col = TRUE,
    create_other_AE_row = FALSE
  )

all.equal(tab1, tab2)

# use the contingency table produced above in pvlrt()
## 500 bootstrap iterations (nsim) in the example below
## is for quick demonstration only --
## we recommended setting nsim to 10000 (default) or bigger
test1 <- pvlrt(tab1, nsim = 500)

References

Ding, Y., Markatou, M. and Ball, R., 2020. An evaluation of statistical approaches to postmarketing surveillance. Statistics in Medicine, 39(7), pp.845-874.

Chakraborty, S., Liu, A., Ball, R. and Markatou, M., 2022. On the use of the likelihood ratio test methodology in pharmacovigilance. Statistics in Medicine, 41(27), pp.5395-5420.