SMARTbayesR (version 2.0.0)

PowerBayesian: Power Calculation for a SMART with a Binary Outcome

Description

This function computes the power for a sequential multiple assignment randomized trial (SMART) of one of three designs: "design-1" or "general" or "design-3".

Usage

PowerBayesian(
  design = "design-1",
  sample_size = 100,
  response_prob = c(0.5, 0.9, 0.3, 0.7, 0.5, 0.8),
  stage_one_trt_one_response_prob = 0.7,
  stage_one_trt_two_response_prob = 0.5,
  stage_one_trt_three_response_prob = 0.4,
  type = "log-OR",
  threshold,
  alpha = 0.05
)

Arguments

design

specifies for which SMART design to calculate the power: design-1, general, or design-3.

sample_size

the total SMART study sample size.

response_prob

a vector of probabilities of response for each of embedded treatment sequences. In the case of design 1, there are 6, for general design there are 8, and for design-3 there are 9

stage_one_trt_one_response_prob

the probability of response to stage-1 treatment for first stage-1 treatment.

stage_one_trt_two_response_prob

the probability of response to stage-1 treatment for second stage-1 treatment.

stage_one_trt_three_response_prob

the probability of response to stage-1 treatment for third stage-1 treatment (for design-3 only).

type

specifies log-OR, RD or log-RR.

threshold

minimum detectable difference between each EDTR and the best

alpha

probability of excluding optimal embedded dynamic treatment regime

Value

The power to exclude embedded dynamic treatment regimes bigger than threshold from the set of best.

Examples

# NOT RUN {
PowerBayesian(
  design = "design-1",
  sample_size = 100,
  response_prob = c(0.5, 0.9, 0.3, 0.7, 0.5, 0.8),
  stage_one_trt_one_response_prob = 0.7,
  stage_one_trt_two_response_prob = 0.5,
  type="log-OR",
  threshold=0.2
)

PowerBayesian(
  design = "general",
  sample_size = 250,
  response_prob = c(0.5, 0.9, 0.7, 0.2, 0.3, 0.8, 0.4, 0.7),
  stage_one_trt_one_response_prob = 0.7,
  stage_one_trt_two_response_prob = 0.5,
  type="log-OR",
  threshold=0.2
)
# }