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Disproportionality Time Trend for a Drug-Event Combination

Source: R/disproportionality_analysis.R
disproportionality_trend.Rd

This function calculates the disproportionality time trend for a given drug-event combination. Check disproportionality_analysis() for more options to address biases. Its results can be visualized using plot_disproportionality_trend().

Usage

disproportionality_trend(
  drug_selected,
  reac_selected,
  temp_drug = Drug,
  temp_reac = Reac,
  temp_demo = Demo,
  temp_demo_supp = Demo_supp[, .(primaryid, quarter)],
  meddra_level = "pt",
  drug_level = "substance",
  restriction = "none",
  time_granularity = "year",
  cumulative = TRUE
)

Arguments

drug_selected

Drug selected

reac_selected

Event selected

temp_drug

Drug dataset. Can be set to sample_Drug for testing

temp_reac

Reac dataset. Can be set to sample_Reac for testing

temp_demo

Demo dataset. Defaults to Demo. Can be se to sample_Demo for testing

temp_demo_supp

Data frame containing supplementary demographic data, and in particular the quarter. Defaults to Demo_supp\[, .(primaryid, quarter)\].

meddra_level

The desired MedDRA level for analysis (default is "pt").

drug_level

The desired drug level for analysis (default is "substance"). If set to "custom" allows a list of lists for reac_selected (collapsing multiple terms).

restriction

Primary IDs to consider for analysis (default is "none", which includes the entire population). If set to Demo[!RB_duplicates_only_susp]$primaryid, for example, allows to exclude duplicates according to one of the deduplication algorithms.

time_granularity

Character string specifying the time granularity. Options are "year", "quarter", or "month". Defaults to "year".

cumulative

Logical indicating whether to calculate cumulative values. Defaults to TRUE.

Value

A data frame containing the disproportionality results over time, including:

period

Time period. Deafult is 'year'. Other values are 'quarter' and 'month'. When using 'quarter' Demo_supp is required

TOT

Total number of reports

D_E

Number of reports with both drug and event

D_nE

Number of reports with the drug but not the event

D

Total number of reports with the drug

nD_E

Number of reports with the event but not the drug

E

Total number of reports with the event

nD_nE

Number of reports with neither the drug nor the event

ROR_median

Reporting odds ratio (ROR) median

ROR_lower

ROR lower bound (2.5%)

ROR_upper

ROR upper bound (97.5%)

p_value_fisher

Fisher's exact test p-value

Bonferroni

Bonferroni-corrected p-value

IC_median

Information component (IC) median

IC_lower

IC lower bound

IC_upper

IC upper bound

label_ROR

Formatted ROR label

label_IC

Formatted IC label

Details

The function processes the provided data to calculate the reporting odds ratio (ROR) and the information component (IC) for the specified drug-event combination over time.

See also

Other disproportionality functions: disproportionality_analysis(), disproportionality_comparison()

Examples

drug_selected <- "paracetamol"
reac_selected <- "overdose"
result <- disproportionality_trend(drug_selected, reac_selected,
  temp_drug = sample_Drug, temp_reac = sample_Reac, temp_demo = sample_Demo,
  temp_demo_supp = sample_Demo_supp[, .(primaryid, quarter)]
)