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 |
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)