BayesAT {BayesAT}R Documentation

Bayesian adaptive trial interim analysis

Description

BayesAT conducts Bayesian adaptive trials through multiple-stage interim analysis.

Usage

BayesAT(
  data,
  D,
  stage,
  threshold,
  start,
  objective,
  alpha,
  beta,
  boundary = NULL
)

Arguments

data

Matrix. The data contains both survival time and event status.

D

Numerical. The duration of interim analysis, matching the length of enrollment time.

stage

Integer. Numbers of interim analysis stages.

threshold

Numerical. The value tested against hypothesis or evidence.

start

Numerical. The time point when the interim analysis starts.

objective

Numerical. The time point for predicted survival rate, for example, 2 years, or 5 years survival probability.

alpha

Numerical. Gamma distribution alpha parameter.

beta

Numerical. Gamma distribution beta parameter (rate = 1/scale).

boundary

The stopping criterion for interim analysis, and the default sets at 5% significance level and calculate quantiles by qnorm() for each stages.

Value

Interim analysis reporting Bayesian adaptive trial results.

If there is one data set applied to BayesAT, the result will provide a table containing:

⁠Upper bound⁠ can be used as stopping criterion for efficacy;

⁠Lower bound⁠ can be used as stopping criterion for futility;

⁠Z score⁠ Z statistic is calculated based on the predicted survival probability:

\frac{\hat{S} - S_0}{SD( \hat{S} )}

with predicted mean survival rate \hat{S} and test evidence or threshold S_0.

⁠Efficacy Prob⁠ and ⁠Futility Prob⁠ Predictive probability measures the efficacy or futility, such as P(\hat{S} > \text{Efficacy}) and P(\hat{S} < \text{Futility}).

Efficacy and Futility indicate the interim analysis results: + means the trial reach the stopping criterion, otherwise it is -.

Examples

data <- Simulate_Enroll(n = c(30,20,20,15,30), lambda = 0.03,
                        event = 0.1, M = 3, group = 5, maxt = 5,
                        accrual = 3,  censor = 0.9, followup = 2,
                        partition = "Uneven")
## assign patients in each group analyzed at each stage of time points
IA <- BayesAT(data,D = 3,stage = 5,threshold = 0.9, start = 1.5,
              objective = 2, alpha = 3, beta = 82)
summary(IA)
plot(IA)

[Package BayesAT version 0.1.0 Index]