r_contin_table_zip function

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

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

• n: number of random matrices to generate.
• row_marginals, col_marginals: (possibly named) vector of row and column marginal totals. Must add up to the same total. If named, the names are passed on to the randomly generated matrix/matrices.
• signal_mat: numeric matrix of dimension length(row_marginals) x length(col_marginals). The (i, j)-th entry of signal_mat determines the signal strength of the i-th adverse event and j-th drug pair. The default is 1 for each pair, which means no signal for the pair.
• omega_vec: vector of zero inflation probabilities. Must be of the same length as col_marginals.
• no_zi_idx: List of pairs (i, j) where zero inflation is not allowed. To specify the entirety i-th row (or j-th column) use c(i, 0) (or c(0, j)). See examples.
• ...: Additional arguments. Currently unused.

Returns

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)