oc_pos: Assessing probability of success

In tipmap: Tipping Point Analysis for Bayesian Dynamic Borrowing

Assessing probability of success

Description

Assessment of the probability of truly or falsely (depending on simulated scenario) rejecting the null hypothesis of interest for a given weight and evidence level, using simulated data as input.

Usage

oc_pos(
  m,
  se,
  probs,
  weights = seq(0, 1, by = 0.01),
  map_prior,
  sigma,
  null_effect = 0,
  direction_pos = T,
  n_cores = 1,
  eval_strategy = "sequential"
)

Arguments

m	Numerical vector of simulated effect estimates.
se	Numerical vector of simulated standard errors (m and se need to have the same length).
probs	Vector of quantiles q, with 1 minus q representing an evidence level of interest (where positive effect estimate indicate a beneficial treatment).
weights	Vector of weights of the informative component of the MAP prior (defaults to seq(0, 1, by = 0.01)).
map_prior	A MAP prior containing information about the trials in the source population, created using RBesT; a mixture of normal distributions is required.
sigma	Standard deviation of the weakly informative component of the MAP prior, recommended to be the unit-information standard deviation.
null_effect	Numerical value, representing the null effect (defaults to 0).
direction_pos	Logical value, TRUE (default) if effects greater that the null_effect indicate a beneficial treatment and FALSE otherwise.
n_cores	Integer value, representing the number of cores to be used (defaults to 1); only applies if eval_strategy is not "sequential".
eval_strategy	Character variable, representing the evaluation strategy, either "sequential", "multisession", or "multicore" (see documentation of future::plan, defaults to "sequential").

Value

A 2-dimensional array containing probabilities, either of truly (probability of success) or falsely rejecting the null hypothesis of interest for a given weight and evidence level.

See Also

oc_bias and oc_coverage.

Examples

set.seed(123)
n_sims <- 5 # small number for exemplary application
sim_dat <- list(
  "m" = rnorm(n = n_sims, mean = 1.15, sd = 0.1),
  "se" = rnorm(n = n_sims, mean = 1.8, sd = 0.3)
)
results <- oc_pos(
  m = sim_dat[["m"]],
  se = sim_dat[["se"]],
  probs = c(0.025, 0.05, 0.1, 0.2), 
  weights = seq(0, 1, by = 0.01), 
  map_prior = load_tipmap_data("tipmapPrior.rds"), 
  sigma = 16.23,
  null_effect = 0,
  direction_pos = TRUE, 
  eval_strategy = "sequential",
  n_cores = 1
) 
print(results)

tipmap documentation built on Aug. 14, 2023, 5:09 p.m.