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Disproportionality Analysis

Source: R/da_object_oriented_functions.R
da.Rd

The function da executes disproportionality analyses, i.e. compares the proportion of reports with a specific adverse event for a drug, against an event proportion from a comparator based on the passed data frame. See the vignette for a brief introduction to disproportionality analysis. Furthermore, da supports three estimators: Information Component (IC), Proportional Reporting Rate (PRR) and the Reporting Odds Ratio (ROR).

Usage

da(
  df = NULL,
  df_colnames = list(report_id = "report_id", drug = "drug", event = "event", group_by =
    NULL),
  da_estimators = c("ic", "prr", "ror"),
  sort_by = "ic",
  number_of_digits = 2,
  rule_of_N = 3,
  conf_lvl = 0.95,
  excel_path = NULL
)

Arguments

df

An object possible to convert to a data table, e.g. a tibble or data.frame, containing patient level reported drug-event-pairs. See header 'The df object' below for further details.

df_colnames

A list of column names to use in df. That is, point da to the 'report id'-column (report_id), the 'drug name'-column (drug), the 'adverse event'-column (event) and optionally a grouping column group_by to calculate disproportionality across. See the vignette for further details.

da_estimators

Character vector specifying which disproportionality estimators to use, in case you don't need all implemented options. Defaults to c("ic", "prr", "ror").

sort_by

The output is sorted in descending order of the lower bound of the confidence/credibility interval for a passed da estimator. Any of the passed strings in "da_estimators" is accepted, the default is "ic". If a grouping variable is passed, sorting is made by the sample average across each drug-event-combination (ignoring NAs).

number_of_digits

Round decimal columns to specified precision, default is two decimals.

rule_of_N

Numeric value. Sets estimates for ROR and PRR to NA when observed counts are strictly less than the passed value of rule_of_N. Default value is 3, 5 is sometimes used as a more liberal alternative. Set to NULL if you don't want to apply any such rule.

conf_lvl

Confidence level of confidence or credibility intervals. Default is 0.95 (i.e. 95 % confidence interval).

excel_path

Intended for users who prefer to work in excel with minimal work in R. To write the output of da to an excel file, provide a path to a folder. For instance, to write to your current working directory, pass getwd(). The excel file will by default be named da.xlsx. To control the excel file name, pass a path ending with the desired filename suffixed with .xlsx. If you do not want to export the output to an excel file, pass NULL (the default).

Value

da returns a data frame (invisibly) containing counts and estimates related to supported disproportionality estimators. Each row corresponds to a drug-event pair.

The df object

The passed df should be (convertible to) a data table and at least contain three columns: report_id, drug and event. The data table should contain one row per reported drug-event-combination, i.e. receiving a single additional report for drug X and event Y would add one row to the table. If the single report contained drug X for event Y and event Z, two rows would be added, with the same report_id and drug on both rows. Column report_id must be of type numeric or character. Columns drug and event must be of type character. If column group_by is provided, it can be either numeric or character. You can use a df with column names of your choosing, as long as you connect role and name in the df_colnames-parameter.

Examples

### Run a disproportionality analysis

da_1 <-
  tiny_dataset |>
  da()

### Run a disproportionality across subgroups
list_of_colnames <-
  list(
    report_id = "report_id",
    drug = "drug",
    event = "event",
    group_by = "group"
  )

 da_2 <-
  tiny_dataset |>
  da(df_colnames = list_of_colnames)

# If columns in your df have different names than the default ones,
# you can specify the column names in the df_colnames parameter list:

renamed_df <-
  tiny_dataset |>
  dplyr::rename(ReportID = report_id)

list_of_colnames$report_id <- "ReportID"

da_3 <-
  renamed_df |>
  da(df_colnames = list_of_colnames)