adaptive_analysis_norm_global {adpss}

Analyze data according to a globally efficient adaptive design.

Description

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").

Usage

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
)

Arguments

initial_test

Designate the initial working test generated by work_test_norm_global function.

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 0.

final_analysis

If TRUE, the result input will be regarded as complete (no more data will be obtained) and the significance level will be exhausted. If FALSE, the current analysis will be regarded as an interim analysis and the significance level will be preserved.

estimate

If TRUE, p-value, median unbiased estimator and upper and lower confidence limits will be calculated.

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.

Value

It returns whether or not the result was statistically significant, a p-value and an exact confidence limits.

References

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.

See Also

work_test_norm_global and sample_size_norm_global.

Examples

# 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
  )