pvda (version 0.0.4)

da: Disproportionality Analysis

Description

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
)

Value

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

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).

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

# \donttest{
### 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)
# }