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 (
mandseneed 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 thenull_effectindicate a beneficial treatment andFALSEotherwise.- n_cores
Integer value, representing the number of cores to be used (defaults to 1); only applies if
eval_strategyis 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.
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)
#> q=0.025 q=0.05 q=0.1 q=0.2
#> w=0 0.0 0.0 0.0 0.2
#> w=0.01 0.0 0.0 0.0 0.2
#> w=0.02 0.0 0.0 0.0 0.4
#> w=0.03 0.0 0.0 0.0 0.6
#> w=0.04 0.0 0.0 0.0 0.6
#> w=0.05 0.0 0.0 0.0 0.6
#> w=0.06 0.0 0.0 0.0 0.8
#> w=0.07 0.0 0.0 0.0 0.8
#> w=0.08 0.0 0.0 0.2 0.8
#> w=0.09 0.0 0.0 0.2 1.0
#> w=0.1 0.0 0.0 0.2 1.0
#> w=0.11 0.0 0.0 0.2 1.0
#> w=0.12 0.0 0.0 0.2 1.0
#> w=0.13 0.0 0.0 0.2 1.0
#> w=0.14 0.0 0.0 0.2 1.0
#> w=0.15 0.0 0.0 0.4 1.0
#> w=0.16 0.0 0.0 0.6 1.0
#> w=0.17 0.0 0.0 0.6 1.0
#> w=0.18 0.0 0.0 0.6 1.0
#> w=0.19 0.0 0.0 0.6 1.0
#> w=0.2 0.0 0.0 0.6 1.0
#> w=0.21 0.0 0.0 0.6 1.0
#> w=0.22 0.0 0.0 0.8 1.0
#> w=0.23 0.0 0.0 0.8 1.0
#> w=0.24 0.0 0.2 0.8 1.0
#> w=0.25 0.0 0.2 0.8 1.0
#> w=0.26 0.0 0.2 0.8 1.0
#> w=0.27 0.0 0.2 0.8 1.0
#> w=0.28 0.0 0.2 1.0 1.0
#> w=0.29 0.0 0.2 1.0 1.0
#> w=0.3 0.0 0.2 1.0 1.0
#> w=0.31 0.0 0.2 1.0 1.0
#> w=0.32 0.0 0.2 1.0 1.0
#> w=0.33 0.0 0.2 1.0 1.0
#> w=0.34 0.0 0.2 1.0 1.0
#> w=0.35 0.0 0.4 1.0 1.0
#> w=0.36 0.0 0.4 1.0 1.0
#> w=0.37 0.0 0.6 1.0 1.0
#> w=0.38 0.0 0.6 1.0 1.0
#> w=0.39 0.0 0.6 1.0 1.0
#> w=0.4 0.0 0.6 1.0 1.0
#> w=0.41 0.0 0.6 1.0 1.0
#> w=0.42 0.0 0.6 1.0 1.0
#> w=0.43 0.0 0.6 1.0 1.0
#> w=0.44 0.0 0.6 1.0 1.0
#> w=0.45 0.0 0.6 1.0 1.0
#> w=0.46 0.0 0.8 1.0 1.0
#> w=0.47 0.0 0.8 1.0 1.0
#> w=0.48 0.0 0.8 1.0 1.0
#> w=0.49 0.0 0.8 1.0 1.0
#> w=0.5 0.2 0.8 1.0 1.0
#> w=0.51 0.2 0.8 1.0 1.0
#> w=0.52 0.2 0.8 1.0 1.0
#> w=0.53 0.2 0.8 1.0 1.0
#> w=0.54 0.2 1.0 1.0 1.0
#> w=0.55 0.2 1.0 1.0 1.0
#> w=0.56 0.2 1.0 1.0 1.0
#> w=0.57 0.2 1.0 1.0 1.0
#> w=0.58 0.2 1.0 1.0 1.0
#> w=0.59 0.2 1.0 1.0 1.0
#> w=0.6 0.2 1.0 1.0 1.0
#> w=0.61 0.2 1.0 1.0 1.0
#> w=0.62 0.2 1.0 1.0 1.0
#> w=0.63 0.2 1.0 1.0 1.0
#> w=0.64 0.4 1.0 1.0 1.0
#> w=0.65 0.4 1.0 1.0 1.0
#> w=0.66 0.4 1.0 1.0 1.0
#> w=0.67 0.6 1.0 1.0 1.0
#> w=0.68 0.6 1.0 1.0 1.0
#> w=0.69 0.6 1.0 1.0 1.0
#> w=0.7 0.6 1.0 1.0 1.0
#> w=0.71 0.6 1.0 1.0 1.0
#> w=0.72 0.6 1.0 1.0 1.0
#> w=0.73 0.6 1.0 1.0 1.0
#> w=0.74 0.6 1.0 1.0 1.0
#> w=0.75 0.8 1.0 1.0 1.0
#> w=0.76 0.8 1.0 1.0 1.0
#> w=0.77 0.8 1.0 1.0 1.0
#> w=0.78 0.8 1.0 1.0 1.0
#> w=0.79 0.8 1.0 1.0 1.0
#> w=0.8 0.8 1.0 1.0 1.0
#> w=0.81 0.8 1.0 1.0 1.0
#> w=0.82 1.0 1.0 1.0 1.0
#> w=0.83 1.0 1.0 1.0 1.0
#> w=0.84 1.0 1.0 1.0 1.0
#> w=0.85 1.0 1.0 1.0 1.0
#> w=0.86 1.0 1.0 1.0 1.0
#> w=0.87 1.0 1.0 1.0 1.0
#> w=0.88 1.0 1.0 1.0 1.0
#> w=0.89 1.0 1.0 1.0 1.0
#> w=0.9 1.0 1.0 1.0 1.0
#> w=0.91 1.0 1.0 1.0 1.0
#> w=0.92 1.0 1.0 1.0 1.0
#> w=0.93 1.0 1.0 1.0 1.0
#> w=0.94 1.0 1.0 1.0 1.0
#> w=0.95 1.0 1.0 1.0 1.0
#> w=0.96 1.0 1.0 1.0 1.0
#> w=0.97 1.0 1.0 1.0 1.0
#> w=0.98 1.0 1.0 1.0 1.0
#> w=0.99 1.0 1.0 1.0 1.0
#> w=1 1.0 1.0 1.0 1.0