graphicalMCP (version 0.2.5)

graph_calculate_power: Calculate power values for a graphical multiple comparison procedure

Description

Under the alternative hypotheses, the distribution of test statistics is assumed to be a multivariate normal distribution. Given this distribution, this function calculates power values for a graphical multiple comparison procedure. By default, it calculate the local power, which is the probability to reject an individual hypothesis, the probability to reject at least one hypothesis, the probability to reject all hypotheses, the expected number of rejections, and the probability of user-defined success criteria. See vignette("shortcut-testing") and vignette("closed-testing") for more illustration of power calculation.

Usage

graph_calculate_power(
  graph,
  alpha = 0.025,
  power_marginal = rep(alpha, length(graph$hypotheses)),
  test_groups = list(seq_along(graph$hypotheses)),
  test_types = c("bonferroni"),
  test_corr = rep(list(NA), length(test_types)),
  sim_n = 1e+05,
  sim_corr = diag(length(graph$hypotheses)),
  sim_success = NULL,
  verbose = FALSE
)

Value

A power_report object with a list of 3 elements:

• inputs - Input parameters, which is a list of:

  • graph - Initial graph,

  • alpha - Overall significance level,

  • test_groups - Groups of hypotheses for different types of tests,

  • test_types - Different types of tests,

  • test_corr - Correlation matrices for parametric tests,

  • sim_n - Number of simulations,

  • power_marginal - Marginal power of all hypotheses

  • sim_corr - Correlation matrices for simulations,

  • sim_success - User-defined success criteria.

• power - A list of power values

  • power_local - Local power of all hypotheses, which is the proportion of simulations in which each hypothesis is rejected,

  • rejection_expected - Expected (average) number of rejected hypotheses,

  • power_at_least_1 - Power to reject at least one hypothesis,

  • power_all - Power to reject all hypotheses,

  • power_success - Power of user-defined success, which is the proportion of simulations in which the user-defined success criterion

  • sim_success is met.

• details - An optional list of datasets showing simulated p-values and results for each simulation.

Arguments

graph

An initial graph as returned by graph_create().

alpha

A numeric value of the one-sided overall significance level, which should be between 0 & 1. The default is 0.025 for one-sided hypothesis testing. Note that only one-sided tests are supported.

power_marginal

A numeric vector of marginal power values to use when simulating p-values. See Details for more on the simulation process.

test_groups

A list of numeric vectors specifying hypotheses to test together. Grouping is needed to correctly perform Simes and parametric tests.

test_types

A character vector of test types to apply to each test group. This is needed to correctly perform Simes and parametric tests. The length should match the number of elements in test_groups.

test_corr

(Optional) A list of numeric correlation matrices. Each entry in the list should correspond to each test group. For a test group using Bonferroni or Simes tests, its corresponding entry in test_corr should be NA. For a test group using parametric tests, its corresponding entry in test_corr should be a numeric correlation matrix specifying the correlation between test statistics for hypotheses in this test group. The length should match the number of elements in test_groups.

sim_n

An integer scalar specifying the number of simulations. The default is 1e5.

sim_corr

A numeric matrix of correlations between test statistics for all hypotheses. The dimensions should match the number of hypotheses in graph.

sim_success

A list of user-defined functions to specify the success criteria. Functions must take one simulation's logical vector of results as an input, and return a length-one logical vector. For instance, if "success" means rejecting hypotheses 1 and 2, use sim_success = list("1 and 2" = function(x) x[1] && x[2]). If the list is not named, the function body will be used as the name. Lambda functions also work starting with R 4.1, e.g. sim_success = list(\(x) x[3] || x[4]).

verbose

A logical scalar specifying whether the details of power simulations should be included in results. The default is verbose = FALSE.

Simulation details

The power calculation is based on simulations. The distribution to simulate from is determined as a multivariate normal distribution by power_marginal and sim_corr. In particular, power_marginal is a vector of marginal power values for all hypotheses. The marginal power is the power to reject the null hypothesis at the significance level alpha without multiplicity adjustment. This value could be readily available from standard software and other R packages. Then we can determine the mean of the multivariate normal distribution as $$\Phi^{-1}\left(1-\alpha\right)-\Phi^{-1}\left(1-d_i\right)$$, which is often called the non-centrality parameter or the drift parameter. Here \(d_i\) is the marginal power power_marginal of hypothesis \(i\). Given the correlation matrix sim_corr, we can simulate from this multivariate normal distribution using the mvtnorm R package (Genz and Bretz, 2009).

Each set simulated values can be used to calculate the corresponding one-sided p-values. Then this set of p-values are plugged into the graphical multiple comparison procedure to determine which hypotheses are rejected. This process is repeated n_sim times to produce the power values as the proportion of simulations in which a particular success criterion is met.

References

Bretz, F., Posch, M., Glimm, E., Klinglmueller, F., Maurer, W., and Rohmeyer, K. (2011a). Graphical approaches for multiple comparison procedures using weighted Bonferroni, Simes, or parametric tests. Biometrical Journal, 53(6), 894-913.

Bretz, F., Maurer, W., and Hommel, G. (2011b). Test and power considerations for multiple endpoint analyses using sequentially rejective graphical procedures. Statistics in Medicine, 30(13), 1489-1501.

Genz, A., and Bretz, F. (2009). Computation of Multivariate Normal and t Probabilities, series Lecture Notes in Statistics. Springer-Verlag, Heidelberg.

Lu, K. (2016). Graphical approaches using a Bonferroni mixture of weighted Simes tests. Statistics in Medicine, 35(22), 4041-4055.

Xi, D., Glimm, E., Maurer, W., and Bretz, F. (2017). A unified framework for weighted parametric multiple test procedures. Biometrical Journal, 59(5), 918-931.

Examples

# A graphical multiple comparison procedure with two primary hypotheses (H1
# and H2) and two secondary hypotheses (H3 and H4)
# See Figure 4 in Bretz et al. (2011a).
alpha <- 0.025
hypotheses <- c(0.5, 0.5, 0, 0)
delta <- 0.5
transitions <- rbind(
  c(0, delta, 1 - delta, 0),
  c(delta, 0, 0, 1 - delta),
  c(0, 1, 0, 0),
  c(1, 0, 0, 0)
)
g <- graph_create(hypotheses, transitions)

marginal_power <- c(0.8, 0.8, 0.7, 0.9)
corr1 <- matrix(0.5, nrow = 2, ncol = 2)
diag(corr1) <- 1
corr <- rbind(
  cbind(corr1, 0.5 * corr1),
  cbind(0.5 * corr1, corr1)
)
success_fns <- list(
  # Probability to reject both H1 and H2
  `H1andH2` = function(x) x[1] & x[2],
  # Probability to reject both (H1 and H3) or (H2 and H4)
  `(H1andH3)or(H2andH4)` = function(x) (x[1] & x[3]) | (x[2] & x[4])
)
set.seed(1234)
# Bonferroni tests
# Reduce the number of simulations to save time for package compilation
power_output <- graph_calculate_power(
  g,
  alpha,
  sim_corr = corr,
  sim_n = 1e2,
  power_marginal = marginal_power,
  sim_success = success_fns
)

# Parametric tests for H1 and H2; Simes tests for H3 and H4
# User-defined success: to reject H1 or H2; to reject H1 and H2
# Reduce the number of simulations to save time for package compilation
graph_calculate_power(
  g,
  alpha,
  test_groups = list(1:2, 3:4),
  test_types = c("parametric", "simes"),
  test_corr = list(corr1, NA),
  sim_n = 1e2,
  sim_success = list(
    function(.) .[1] || .[2],
    function(.) .[1] && .[2]
  )
)