power_test: Empirical power analysis for single-case data

Description

Conducts a Monte-Carlo study on the test-power and alpha-error probability of a statistical function.

Usage

power_test(
  design,
  method = c("plm_level", "rand", "tauU"),
  effect = "level",
  n_sim = 100,
  design_is_one_study = TRUE,
  alpha_test = TRUE,
  power_test = TRUE,
  binom_test = FALSE,
  binom_test_alpha = FALSE,
  binom_test_power = FALSE,
  binom_test_correct = FALSE,
  ci = FALSE,
  alpha_level = 0.05
)

Arguments

design: An object returned from the design function.
method: A (named) list that defines the methods the power analysis is based on. Each element can contain a function (that takes an scdf file and returns a p value) or a character string (the name of predefined functions). default method = list("plm_level", "rand", "tauU") computes a power analysis based on tau_u(), rand_test() and plm() analyses. (Further predefined functions are: "plm_slope", "plm_poisson_level", "plm_poisson_slope", "hplm_level", "hplm_slope", "base_tau".
effect: Either "level" or "slope". The respective effect of the provided design is set to 0 when computing the alpha-error proportion.
n_sim: Number of sample studies created for the the Monte-Carlo study. Default is n = 100. Ignored if design_is_one_study = FALSE.
design_is_one_study: If TRUE, the design is assumed to define all cases of one study that is repeatedly randomly created n_sim times. If false, the design is assumed to contain all cases from which a random sample is generated. This is useful for very specific complex simulation studies.
alpha_test: Logical. If TRUE, alpha error is calculated.
power_test: Logical. If TRUE, power is calculated.
binom_test: Shortcut. When set TRUE, binom_test_power is set to 0.80, binom_test_alpha is set to 0.05, and binom_test_correct is set to 0.875.
binom_test_alpha: Either FALSE or a value. If a value is provided, a binomial test is calculated testing if the alpha error proportion is less than the provided value.
binom_test_power: Either FALSE or a value. If a value is provided, a binomial test is calculated testing if the power is greater than the provided value.
binom_test_correct: Either FALSE or a value. If a value is provided, a binomial test is calculated testing if the correct proportion is greater than the provided value.
ci: Either FALSE or a value. If a value is provided, confidence intervals at the provided level are calculated for power, alpha error, and correct proportions.
alpha_level: Alpha level used to calculate the proportion of significant tests. Default is alpha_level = 0.05.

Author

Juergen Wilbert

Details

Based on a design() object, a large number of single-cases are generated and re-analyzed with a provided statistical function. The proportion of significant analyzes is the test power. In a second step, a specified effect of the design object is set to 0 and again single-cases are generated and reanalyzed. The proportion of significant analyzes is the alpha error probability.

Examples

Run this code


## Assume you want to conduct a single-case study with 15 measurements
## (phases: A = 6 and B = 9) using a highly reliable test and
## an expected level effect of d = 1.4.
## A (strong) trend effect is trend = 0.05. What is the power?
## (Note: n_sims is set to 10. Set n_sims to 1000 for a serious calculation.)
design <- design(
  n = 1, phase_design = list(A = 6, B = 9),
  rtt = 0.8, level = 1.4, trend = 0.05
)
power_test(design, n_sim = 10)

## Would you achieve higher power by setting up a MBD with three cases?
design <- design(
  n = 3, phase_design = list(A = 6, B = 9),
  rtt = 0.8, level = 1.4, trend = 0.05
)
power_test(design, n_sim=10, method=list("hplm_level", "rand", "tauU_meta"))

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning