adjust_significance_level {RLoptimal}R Documentation

Adjust Significance Level on a Simulation Basis

Description

Adjust Significance Level on a Simulation Basis

Usage

adjust_significance_level(
  allocation_rule,
  models,
  N_total,
  N_ini,
  N_block,
  outcome_type = c("continuous", "binary"),
  sd_normal = NULL,
  alpha = 0.025,
  n_sim = 10000L,
  seed = NULL
)

Arguments

allocation_rule

An object of class AllocationRule specifying an obtained optimal adaptive allocation rule.

models

An object of class Mods specifying assumed dose-response models. This is used in the MCPMod method at the end of this study.

N_total

A positive integer value. The total number of subjects.

N_ini

A positive integer vector in which each element is greater than or equal to 2. The number of subjects initially assigned to each dose.

N_block

A positive integer value. The number of subjects allocated adaptively in each round.

outcome_type

A character value specifying the outcome type. Possible values are "continuous" (default), and "binary".

sd_normal

A positive numeric value. The standard deviation of the observation noise. When outcome_type is "continuous", sd_normal must be specified.

alpha

A positive numeric value. The original significance level. Default is 0.025.

n_sim

A positive integer value. The number of simulation studies to calculate the adjusted significance level. Default is 10000.

seed

An integer value. Random seed for data generation in the simulation studies.

Value

A positive numeric value specifying adjusted significance level.

Examples

library(RLoptimal)

doses <- c(0, 2, 4, 6, 8)

models <- DoseFinding::Mods(
  doses = doses, maxEff = 1.65,
  linear = NULL, emax = 0.79, sigEmax = c(4, 5)
)

## Not run: 
allocation_rule <- learn_allocation_rule(
  models,
  N_total = 150, N_ini = rep(10, 5), N_block = 10, Delta = 1.3,
  outcome_type = "continuous", sd_normal = sqrt(4.5), 
  seed = 123, rl_config = rl_config_set(iter = 1000),
  alpha = 0.025
)

# Simulation-based adjustment of the significance level using `allocation_rule`
adjusted_alpha <- adjust_significance_level(
  allocation_rule, models,
  N_total = 150, N_ini = rep(10, 5), N_block = 10,
  outcome_type = "continuous", sd_normal = sqrt(4.5),
  alpha = 0.025, n_sim = 10000, seed = 123
)
## End(Not run)


[Package RLoptimal version 1.2.0 Index]