adaptive_analysis_norm_global {adpss} | R Documentation |
adaptive_analysis_norm_global
performs an globally efficient adaptive test,
a Frequentist adaptive test with the specified significance level
with full flexibility.
Normality with known variance is assumed for the test statistic
(more accurately, the test statistic is assumed to follow Brownian motion.)
Null hypothesis is fixed at 0 without loss of generality.
Exact p-value, median unbiased estimate and confidence limits proposed by Gao et al. (2013) can also be calculated.
For detailed illustration, see vignette("adpss_ex")
.
adaptive_analysis_norm_global(initial_test = 0, times = 0, stats = 0, costs = 0, final_analysis = TRUE, estimate = TRUE, ci_coef = 0.95, tol_est = 1e-08, input_check = TRUE)
initial_test |
Designate the initial working test generated by |
times |
The sequence of times (sample size or information level) at which analyses were conducted. |
stats |
The sequence of test statistics. |
costs |
The sequence of loss required to construct working tests. Specification is optional. Partial specification is allowed, in which non-specification may be represented by |
final_analysis |
If |
estimate |
If |
ci_coef |
The confidence coefficient. Default is 0.95. |
tol_est |
The precision of the calculated results. |
input_check |
Indicate whether or not the arguments input by user contain invalid values. |
It returns whether or not the result was statistically significant, a p-value and an exact confidence limits.
Kashiwabara, K., Matsuyama, Y. An efficient adaptive design approximating fixed sample size designs. In preparation. Gao, P., Liu, L., Mehta, C. (2013) Exact inference for adaptive group sequential designs. Stat Med 32: 3991-4005.
work_test_norm_global
and sample_size_norm_global
.
# Construct an initial working test # Note: cost_type_1_err will be automatically calculated when 0 is specified. init_work_test <- work_test_norm_global(min_effect_size = -log(0.65), cost_type_1_err=1683.458) # Sample size calculation sample_size_norm_global( initial_test = init_work_test, effect_size = 11.11 / 20.02, # needs not be MLE time = 20.02, target_power = 0.75, sample_size = TRUE )