pvLRT: An R package implementing various Likelihood Ratio Test-based approaches to pharmacovigilance

Description

pvLRT is an R package that implements a suite of likelihood ratio test based methods to use in pharmacovigilance. It can handle adverse events data on several simultaneous drugs, with possibly zero inflated report counts. Several testing and post-processing functions are implemented.

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Test if two pvlrt objects are (Nearly) Equal

Description

Test if two pvlrt objects are (Nearly) Equal

Usage

## S3 method for class 'pvlrt'
all.equal(target, current, ...)

Arguments

Details

Compares all values and attributes of target and current pvlrt objects except running times. See all.equal.default for details on the generic function.

See Also

all.equal.default

Casting a pvlrt object as a matrix of log LR statistics

Description

Casting a pvlrt object as a matrix of log LR statistics

Usage

## S3 method for class 'pvlrt'
as.matrix(x, ...)

Arguments

Value

Returns a matrix with the same dimensions as the input contingency table in the original pvlrt call, with each cell providing the corresponding value of the observed log-likelihood ratio test statistic.

See Also

pvlrt

Examples

# 500 bootstrap iterations (nsim) in the example below
# are for quick demonstration only --
# we recommended setting nsim to 10000 (default) or bigger

test1 <- pvlrt(statin46, nsim = 500)
as.matrix(test1)

Convert raw AE-Drug incidence data into a contingency table

Description

Convert raw AE-Drug incidence data into a contingency table

Usage

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

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

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.

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)

Extracting and setting AE and Drug names from a pvlrt object

Description

Extracting and setting AE and Drug names from a pvlrt object

Usage

extract_AE_names(object)

extract_Drug_names(object)

set_AE_names(object, old, new)

set_Drug_names(object, old, new)

Arguments

Value

• extract_AE_names returns a character vector of the names of the AEs in the input pvlrt object

• extract_Drug_names returns a character vector of the names of the Drugs in the input pvlrt object

• set_AE_names returns a new pvlrt object with updated AE names as specified through the arguments old and new.

• set_Drug_names returns a new pvlrt object with updated Drug names as specified through the arguments old and new.

Note

Because a pvlrt object is simply a reclassified matrix, the AE (rows) and Drug (columns) names can also be extracted/modified through rownames and colnames respectively.

See Also

pvlrt

Examples

# 500 bootstrap iterations (nsim) in the example below
# are for quick demonstration only --
# we recommended setting nsim to 10000 (default) or bigger

test1 <- pvlrt(statin46, test_zi = TRUE, nsim = 500)
extract_AE_names(test1)
extract_Drug_names(test1)

set_AE_names(test1, old = "Rhabdomyolysis", new = "Rhabdo")
set_Drug_names(test1, old = "Other", new = "Other-Drugs")

## can be chained with pipes `%>%`:
test2 <- test1 %>%
  set_AE_names(old = "Rhabdomyolysis", new = "Rhabdo") %>%
  set_Drug_names(old = "Other", new = "Other-Drugs")

# see the summary for changed labels
summary(test2)

Extract various summary measures from a pvlrt object

Description

Extract various summary measures from a pvlrt object

Usage

extract_lrstat_matrix(object, ...)

extract_p_value_matrix(object, ...)

extract_zi_probability(object, ...)

extract_n_matrix(object, ...)

extract_significant_pairs(object, significance_level = 0.05, ...)

extract_run_time(object, ...)

Arguments

Value

• extract_lrstat_matrix returns the matrix of the computed log-likelihood ratio test statistics for signals. This produces a result identical to applying as.matrix.

• extract_p_value_matrix returns the matrix of computed p-values associated with the likelihood ratio tests.

• extract_zi_probability returns a vector of (estimated) zero-inflation probabilities.

• extract_n_matrix returns the original contingency table (matrix) used.

• extract_significant_pairs returns a data.table listing the AE/drug pairs determined to be significant at the provided significance level. This is essentially a subset of the data.table obtained through summary.pvlrt() that satisfies the provided significance threshold.

• extract_run_time returns a difftime object measuring the total CPU time needed to run the original pvlrt call.

See Also

pvlrt

Examples

# 500 bootstrap iterations (nsim) in the example below
# are for quick demonstration only --
# we recommended setting nsim to 10000 (default) or bigger

test1 <- pvlrt(statin46, test_zi = TRUE, nsim = 500)
extract_lrstat_matrix(test1)
extract_p_value_matrix(test1)
extract_zi_probability(test1)
extract_n_matrix(test1)
extract_significant_pairs(test1)

FDA FAERS dataset for 2022 Q3

Description

The raw FDA FAERS dataset for 2022 Q3; downloaded from FDA's website and then subsetted to inlcude incidences with ROLE_COD == "PS".

Usage

faers22q3raw

Format

An object of class data.table (inherits from data.frame) with 496312 rows and 3 columns.

Details

obtained from the FDA FAERS database for the quarters 2014Q3 - 2020Q4

This is a raw incidence data stored as a data.table with each row corresponding to a specific incidence index by case ids. It contains the following columns:

• CASEID - case ids for each incidence

• DRUG - names of the drugs

• AE - names of the adverse events

Source

https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html

FDA GBCA dataset with all observed 1707 adverse events

Description

A Drug-Adverse event (AE) count dataset (contingency table) obtained from the FDA FAERS database for the quarters 2014Q3 - 2020Q4

Usage

gbca

Format

An object of class matrix (inherits from array) with 1707 rows and 10 columns.

Details

Data are stored in the form of a contingency table, with drugs listed across the columns and the 1707 AEs presented across the rows. Each cell in the contingency table represents the total report counts associated with that drug/AE pair and detected in the FDA FAERS database during 2014Q3 - 2020Q4.

The dataset contains 6 Gadolinium-Based Contrast Agents (GBCAs) as drugs:

gadobenate, gadobutrol, gadodiamide, gadofosveset, gadopentetate, gadoterate, gadoteridol, gadoversetamide, gadoxetate

Corresponding to all 1707 observed adverse events (AEs) as curated in FAERS database.

Source

https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html

Heatmap, barplot and bubbleplot displaying likelihood ratio test results in a pvlrt object

Description

Heatmap, barplot and bubbleplot displaying likelihood ratio test results in a pvlrt object

Usage

heatmap_pvlrt(
  object,
  AE = NULL,
  Drug = NULL,
  grep = FALSE,
  fill_measure = "p_value",
  show_n = FALSE,
  show_lrstat = FALSE,
  show_p_value = FALSE,
  p_value_lower = 0,
  p_value_upper = 1,
  lrstat_lower = 0,
  lrstat_upper = Inf,
  n_lower = 0,
  n_upper = Inf,
  arrange_alphabetical = FALSE,
  remove_outside = FALSE,
  digits = 2,
  border_color = "black",
  fill_gradient_range = c("darkred", "white"),
  ...
)

## S3 method for class 'pvlrt'
barplot(
  height,
  AE = NULL,
  Drug = NULL,
  grep = FALSE,
  x_axis_measure = "lrstat",
  fill_measure = "p_value",
  show_n = FALSE,
  arrange_alphabetical = FALSE,
  show_p_value = FALSE,
  show_lrstat = FALSE,
  p_value_lower = 0,
  p_value_upper = 1,
  lrstat_lower = 0,
  lrstat_upper = Inf,
  n_lower = 0,
  n_upper = Inf,
  remove_outside = FALSE,
  digits = 2,
  Drug_nrow = 1,
  border_color = "black",
  x_axis_logscale = TRUE,
  fill_gradient_range = c("darkred", "white"),
  ...
)

bubbleplot_pvlrt(
  object,
  AE = NULL,
  Drug = NULL,
  grep = FALSE,
  x_axis_measure = "lrstat",
  fill_measure = "p_value",
  size_measure = "n",
  show_n = FALSE,
  arrange_alphabetical = FALSE,
  show_p_value = FALSE,
  show_lrstat = FALSE,
  p_value_lower = 0,
  p_value_upper = 1,
  lrstat_lower = 0,
  lrstat_upper = Inf,
  n_lower = 0,
  n_upper = Inf,
  remove_outside = FALSE,
  digits = 2,
  Drug_nrow = 1,
  border_color = "black",
  x_axis_logscale = TRUE,
  size_logscale = TRUE,
  fill_gradient_range = c("darkred", "white"),
  ...
)

Arguments

Value

A ggplot object.

See Also

pvlrt

Examples

# 500 bootstrap iterations (nsim) in the example below
# are for quick demonstration only --
# we recommended setting nsim to 10000 (default) or bigger
test1 <- pvlrt(statin46, nsim = 500)
bubbleplot_pvlrt(test1)
heatmap_pvlrt(test1)
barplot(test1)

Overall Log-likelihood for a pvlrt object

Description

Overall Log-likelihood for a pvlrt object

Usage

## S3 method for class 'pvlrt'
logLik(object, type = "full-zip", ...)

Arguments

Details

The function extracts the overall log-likelihood and degrees of freedom (the number of estimated parameters) from a pvlrt object. There are four possible choices of distribution and hypothesis combinations, supplied through the argument type, with the default being type = "full-zip". In a "full" model the signal parameters lambdas are estimated for all cells in the contingency table from the data (subject to the condition lambda >= 1), whereas under a "null" model each lambda is fixed at 1 for each cell. In a "zip" model (type = "full-zip" and type = "null-zip") the log-likelihood under a zero-inflated Poisson model with estimated or supplied zero inflation parameters ( through zi_prob in pvlrt) is returned. The degrees of freedom reflects whether the zero-inflation parameters are estimated. Note that if an ordinary Poisson LRT is run either by setting zi_prob = 0 in pvlrt or equivalently through lrt_poisson then "full-zip" and "null-zip" refer to zero-inflated poisson models with presepecified zero-inflation probabilities equal to 0. Thus, in such cases the results with type = "full-zip" and type = "null-zip" are identical to type = "full-poisson" and type = "null-poisson" respectively. See examples.

Value

An object of class logLik. See Details.

Note

The overall log likelihood must be computed during the original pvlrt run. This is ensured by setting return_overall_loglik = TRUE, and parametrization = "lambda" (or parametrization = "rrr") while running pvlrt().

See Also

pvlrt; AIC

Examples

# 500 bootstrap iterations (nsim) in each example below
# are for quick demonstration only --
# we recommended setting nsim to 10000 (default) or bigger

set.seed(100)
# estimates zero inflation probabilities
test1 <- pvlrt(statin46, nsim = 500)
logLik(test1)
AIC(test1)
BIC(test1)

# compare with and without zero inflation
BIC(logLik(test1, type = "full-zip"))
BIC(logLik(test1, type = "full-poisson"))

# ordinary poisson model
## equivalent to pvlrt(statin46, zi_prob = 0, nsim = 500)
test2 <- lrt_poisson(statin46, nsim = 500)

all.equal(logLik(test2, "full-zip"), logLik(test2, "full-poisson"))

FDA lovastatin dataset

Description

A Drug-Adverse event (AE) count dataset (contingency table) obtained from the FDA FAERS database for the quarters 2014Q3 - 2020Q4

Usage

lovastatin

Format

An object of class matrix (inherits from array) with 47 rows and 3 columns.

Details

Data are stored in the form of a contingency table, with drugs listed across the columns and the 46 AEs presented across the rows. Each cell in the contingency table represents the total report counts associated with that drug/AE pair and detected in the FDA FAERS database during 2014Q3 - 2020Q4.

The dataset contains 1 column for the lovastatin drug, and one column for all other drugs combined.

Source

https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html

Likelihood Ratio Test under the (vanilla, non-zero-inflated) Poisson model

Description

Likelihood Ratio Test under the (vanilla, non-zero-inflated) Poisson model

Usage

lrt_poisson(contin_table, nsim = 10000, parametrization = "rrr", ...)

lrt_vanilla_poisson(contin_table, nsim = 10000, parametrization = "rrr", ...)

Arguments

Value

Returns a pvlrt object. See pvlrt for more details.

Note

lrt_poisson() and lrt_vanilla_poisson() are both wrappers for pvlrt() with omega_vec = rep(0, ncol(contin_table))

See Also

pvlrt

Examples

data("statin46")

# 500 bootstrap iterations (nsim) in the example below
# are for quick demonstration only --
# we recommended setting nsim to 10000 (default) or bigger

# no grouping -- each drug forms its own class
test1 <- lrt_poisson(lovastatin, nsim = 500)

Pseudo Likelihood Ratio Test under the zero-inflated Poisson model with relative reporting rate parametrization

Description

Pseudo Likelihood Ratio Test under the zero-inflated Poisson model with relative reporting rate parametrization

Usage

lrt_zi_poisson(contin_table, nsim = 10000, ...)

Arguments

Value

Returns a pvlrt object. See pvlrt for more details.

Note

lrt_zi_poisson() is a wrapper for pvlrt() with parametrization = "rrr".

See Also

pvlrt

Examples

data("statin46")

# 500 bootstrap iterations (nsim) in the example below
# are for quick demonstration only --
# we recommended setting nsim to 10000 (default) or bigger
test1 <- lrt_zi_poisson(statin46, nsim = 500)
test1

Plotting method for a pvlrt object

Description

Plotting method for a pvlrt object

Usage

## S3 method for class 'pvlrt'
plot(x, type = "bubbleplot", ...)

Arguments

Value

A ggplot object.

See Also

pvlrt; bubbleplot_pvlrt; heatmap_pvlrt; barplot.pvlrt

Examples

# 500 bootstrap iterations (nsim) in the example below
# are for quick demonstration only --
# we recommended setting nsim to 10000 (default) or bigger

test1 <- pvlrt(statin46, nsim = 500)
plot(test1, type = "bubbleplot")
plot(test1, type = "barplot")
plot(test1, type = "heatmap")

Print method for pvlrt objects

Description

Print method for pvlrt objects

Usage

## S3 method for class 'pvlrt'
print(
  x,
  significance_level = 0.05,
  topn = 12,
  digits = 2,
  show_test_summary = FALSE,
  ...
)

Arguments

Value

Invisibly returns the input pvlrt object.

See Also

pvlrt

Examples

# 500 bootstrap iterations (nsim) in the example below
# are for quick demonstration only --
# we recommended setting nsim to 10000 (default) or bigger

test1 <- pvlrt(statin46, nsim = 500)
print(test1)

Pseudo Likelihood Ratio Test for determining significant AE-Drug pairs under Poisson and zero-inflated Poisson models for pharmacovigilance

Description

Pseudo Likelihood Ratio Test for determining significant AE-Drug pairs under Poisson and zero-inflated Poisson models for pharmacovigilance

Usage

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

Value

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

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

Generate random contingency tables for adverse event (across rows) and drug (across columns) combinations given row and column marginal totals, embedded signals, and possibly with zero inflation

Description

Generate random contingency tables for adverse event (across rows) and drug (across columns) combinations given row and column marginal totals, embedded signals, and possibly with zero inflation

Usage

r_contin_table_zip(
  n = 1,
  row_marginals,
  col_marginals,
  signal_mat = matrix(1, length(row_marginals), length(col_marginals)),
  omega_vec = rep(0, length(col_marginals)),
  no_zi_idx = NULL,
  ...
)

Arguments

Value

A list of length n, with each entry being a length(row_marginal) by length(col_marginal) matrix.

Examples

set.seed(42)

# first load the 46 statin data
data(statin46)
# zero inflation probabilities
omega_tru <- c(rep(0.15, ncol(statin46) - 1), 0)

# signal matrix
signal_mat <- matrix(1, nrow(statin46), ncol(statin46))

# "positive" signal at the (1, 1) entry
# the first column
signal_mat[1, 1] <- 10

# Now simulate data with the same row/column marginals
# as in statin46, with embedded signals as specified in
# the above signal_mat

# no zero inflation at (1, 1)
# (where signal is elicited) and the last row ("Other PT")
# and at the last column ("Other drugs") of the simulated matrix

sim_statin <- r_contin_table_zip(
  n = 1,
  row_marginals = rowSums(statin46),
  col_marginals = colSums(statin46),
  signal_mat = signal_mat,
  omega_vec = omega_tru,
  no_zi_idx = list(
    c(1, 1),
    c(nrow(statin46), 0), # the entire last row
    c(0, ncol(statin46)) # the entire last column
  )
)[[1]]

# now analyze the above simulated data

# using a pseudo LRT with a ZIP model
test1 <- pvlrt(
  contin_table = sim_statin,
  nsim = 500
  # set to 500 for demonstration purposes only,
  # we recommend the default 10000 or bigger
)
extract_zi_probability(test1)
extract_significant_pairs(test1)

# LRT with a poisson model
test2 <- lrt_poisson(
  contin_table = sim_statin,
  nsim = 500
  # set to 500 for demonstration purposes only,
  # we recommend the default 10000 or bigger
)
extract_zi_probability(test2)
extract_significant_pairs(test2)

# LRT with true omega supplied
test3 <- pvlrt(
  contin_table = sim_statin,
  zi_prob = omega_tru,
  nsim = 500
  # set to 500 for demonstration purposes only,
  # we recommend the default 10000 or bigger
)
extract_zi_probability(test3)
extract_significant_pairs(test3)

FDA rotavirus vaccine dataset with 794 adverse events observed among combined old (age >= 1 year) and young (age < 1 year) individuals

Description

A vaccine-Adverse event (AE) count dataset (contingency table) obtained from the FDA VAERS database for the year 1999

Usage

rv

Format

An object of class matrix (inherits from array) with 794 rows and 2 columns.

Details

Data are stored in the forms of contingency table, with the vaccines listed across the columns and the 794 AEs presented across the rows. Each cell in the contingency table represents the total report counts associated with that vaccine/AE pair and detected in the FDA VAERS database for the year 1999.

The dataset contains two columns – one for the rotavirus vaccine, and another for other (37 vaccines combined).

Source

https://vaers.hhs.gov/data/datasets.html

FDA rotavirus vaccine dataset with 727 adverse events observed among "old" (non-infant; age >= 1 year) individuals

Description

A vaccine-Adverse event (AE) count dataset (contingency table) obtained from the FDA VAERS database for the year 1999

Usage

rvold

Format

An object of class matrix (inherits from array) with 727 rows and 2 columns.

Details

Data are stored in the forms of contingency table, with the vaccines listed across the columns and the 727 AEs presented across the rows. Each cell in the contingency table represents the total report counts (from "old" individuals with age >= 1 year) associated with that vaccine/AE pair and detected in the FDA VAERS database for the year 1999.

The dataset contains two columns – one for the rotavirus vaccine, and another for other (37 vaccines combined).

Source

https://vaers.hhs.gov/data/datasets.html

FDA rotavirus vaccine dataset with 346 adverse events observed among young (infant – 1 year) individuals

Description

A vaccine-Adverse event (AE) count dataset (contingency table) obtained from the FDA VAERS database for the year 1999

Usage

rvyoung

Format

An object of class matrix (inherits from array) with 346 rows and 2 columns.

Details

Data are stored in the forms of contingency table, with the vaccines listed across the columns and the 346 AEs presented across the rows. Each cell in the contingency table represents the total report counts from young individuals with age < 1 year associated with that vaccine/AE pair and detected in the FDA VAERS database for the year 1999.

The dataset contains two columns – one for the rotavirus vaccine, and another for other (37 vaccines combined).

Source

https://vaers.hhs.gov/data/datasets.html

FDA Statin dataset with 6039 adverse events

Description

A Drug-Adverse event (AE) count dataset (contingency table) obtained from the FDA FAERS database for the quarters 2014Q3 - 2020Q4

Usage

statin

Format

An object of class matrix (inherits from array) with 6039 rows and 7 columns.

Details

Data are stored in the form of a contingency table, with drugs listed across the columns and the 6039 AEs presented across the rows. Each cell in the contingency table represents the total report counts associated with that drug/AE pair and detected in the FDA FAERS database during 2014Q3 - 2020Q4.

The dataset catalogs 6 statin drugs (across columns):

Atorvastatin, Fluvastatin, Lovastatin, Pravastatin, Rosuvastatin, Simvastatin

Corresponding to all 6039 observed adverse events (AEs) observed in statins

Source

https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html

FDA Statin dataset with 1491 adverse events

Description

A Drug-Adverse event (AE) count dataset (contingency table) obtained from the FDA FAERS database for the quarters 2014Q3 - 2020Q4

Usage

statin1491

Format

An object of class matrix (inherits from array) with 1491 rows and 7 columns.

Details

Data are stored in the form of a contingency table, with drugs listed across the columns and the 1491 AEs presented across the rows. Each cell in the contingency table represents the total report counts associated with that drug/AE pair and detected in the FDA FAERS database during 2014Q3 - 2020Q4.

The dataset catalogs 6 statin drugs (across columns):

Atorvastatin, Fluvastatin, Lovastatin, Pravastatin, Rosuvastatin, Simvastatin

The 1491 AEs stored in the dataset represent the intersection of adverse events of the statin class of drugs and the GBCA drugs

Source

https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html

See Also

statin46, statin, gbca

FDA Statin dataset with 46 adverse events

Description

A Drug-Adverse event (AE) count dataset (contingency table) obtained from the FDA FAERS database for the quarters 2014Q3 - 2020Q4

Usage

statin46

Format

An object of class matrix (inherits from array) with 47 rows and 7 columns.

Details

Data are stored in the form of a contingency table, with drugs listed across the columns and the 46 AEs presented across the rows. Each cell in the contingency table represents the total report counts associated with that drug/AE pair and detected in the FDA FAERS database during 2014Q3 - 2020Q4.

The dataset catalogs 6 statin drugs (across columns):

Atorvastatin, Fluvastatin, Lovastatin, Pravastatin, Rosuvastatin, Simvastatin

The 46 adverse events presented across the rows are considered significant by FDA.

Source

https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html

See Also

statin, statin1491, gbca

Summary method for a pvlrt object

Description

Summary method for a pvlrt object

Usage

## S3 method for class 'pvlrt'
summary(object, show_zi = FALSE, ...)

Arguments

Value

Returns a data.table with rows corresponding to all possible AE/Drug pairs as obtained from the input contingency table, and columns titled "AE", "Drug", "n", "lrstat" (log-likelihood ratio test statistic) and "p_value". Additionally, if show_zi is set to TRUE, then as an attribute named "zi" a data.table with rows corresponding to Drugs (columns in the input contingency table), and columns titled "AE", "zi", "lrstat" (log-likelihood ratio test statistic for zero-inflation), "p_value" and "q_value" (Benjamini-Hochberg adjusted p-values, as obtained through p.adjust) is returned.

See Also

pvlrt

Examples

# 500 bootstrap iterations (nsim) in the example below
# are for quick demonstration only --
# we recommended setting nsim to 10000 (default) or bigger

test1 <- pvlrt(statin46, test_zi = TRUE, nsim = 500)
summary(test1)
tmp <- summary(test1, show_zi = TRUE)
print(tmp)
tmp_zi <- attr(tmp, "zi")
print(tmp_zi)