pr_medx: Probability of minimum X% effective dose

In dreamer: Dose Response Models for Bayesian Model Averaging

Probability of minimum X% effective dose

Description

Calculate the probability a dose being the smallest dose that has at least X% of the maximum efficacy.

Usage

pr_medx(
  x,
  doses = attr(x, "doses"),
  ed,
  greater = TRUE,
  small_bound = 0,
  time = NULL
)

Arguments

x	output from a call to dreamer_mcmc().
doses	the doses for which pr(minimum effective X% dose) is to be calculated.
ed	a number between 0 and 100 indicating the ed% dose that is being sought.
greater	if TRUE, higher responses indicate better efficacy. If FALSE, lower responses indicate better efficacy.'
small_bound	the lower (upper) bound of the response variable when greater = TRUE (FALSE). This is used to calculate the ed% effect as ed / 100 * (effect_100 - small_bound) + small_bound.
time	the time (scalar) at which the Pr(MEDX) should be calculated.

Details

Obtaining the probability of a particular does being the minimum efficacious dose achieving ed% efficacy is dependent on the doses specified.

For a given MCMC sample of parameters, the 100% efficacy value is defined as the highest efficacy of the doses specified. For each posterior draw of MCMC parameters, the minimum ed% efficacious dose is defined as the lowest dose what has at least ed% efficacy relative to the 100% efficacy value.

The ed% effect is calculated as ed / 100 * (effect_100 - small_bound) + small_bound where effect_100 is the largest mean response among doses for a given MCMC iteration.

Value

A data frame with the following columns:

• dose: numeric dose levels.

• prob: Prob(EDX | data) for each dose. Note: these probabilities do not necessarily sum to 1 because the EDX may not exist. In fact, Pr(EDX does not exist | data) = 1 - sum(prob).

Examples

set.seed(888)
data <- dreamer_data_linear(
  n_cohorts = c(20, 20, 20),
  dose = c(0, 3, 10),
  b1 = 1,
  b2 = .1,
  sigma = 5
)

# Bayesian model averaging
output <- dreamer_mcmc(
 data = data,
 n_adapt = 1e3,
 n_burn = 1e3,
 n_iter = 1e3,
 n_chains = 2,
 silent = FALSE,
 mod_linear = model_linear(
   mu_b1 = 0,
   sigma_b1 = 1,
   mu_b2 = 0,
   sigma_b2 = 1,
   shape = 1,
   rate = .001,
   w_prior = 1 / 2
 ),
 mod_quad = model_quad(
   mu_b1 = 0,
   sigma_b1 = 1,
   mu_b2 = 0,
   sigma_b2 = 1,
   mu_b3 = 0,
   sigma_b3 = 1,
   shape = 1,
   rate = .001,
   w_prior = 1 / 2
 )
)

pr_medx(output, ed = 80)

# single model
pr_medx(output$mod_linear, ed = 80)

dreamer documentation built on Sept. 1, 2022, 5:05 p.m.