adaptive_analysis_norm_local {adpss}

Analyze data according to a locally efficient adaptive design.

Description

adaptive_analysis_norm_local performs an locally 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. No procedure to calculate p-value or confidence intervals is employed. For detailed illustration, see vignette("adpss_ex").

Usage

adaptive_analysis_norm_local(
  overall_sig_level = 0.025,
  min_effect_size = 1,
  times = 0,
  stats = 0,
  final_analysis = TRUE,
  estimate = FALSE,
  ci_coef = 0.95,
  input_check = TRUE
)

Arguments

overall_sig_level

Overall significance level in (0, 1). Default is 0.025.

min_effect_size

The minimum effect size. It should be positive. The working test will be constructed to have the power of 1 - work_beta for this effect size. Default is 1.

times

The sequence of times (sample size or information level) at which analyses were conducted.

stats

The sequence of test statistics.

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.

input_check

Indicate whether or not the arguments input by user contain invalid values.

Value

List of results including the conditional Type I error probability.

References

Kashiwabara, K., Matsuyama, Y. An efficient adaptive design approximating fixed sample size designs. In preparation.

See Also

sample_size_norm_local.

Examples

# Sample size calculation
sample_size_norm_local(
  overall_sig_level = 0.025,
  min_effect_size = -log(0.65),
  effect_size = 11.11 / 20.02, # needs not be MLE
  time = 20.02,
  target_power = 0.75,
  sample_size = TRUE
  )