Help for package ssutil

Title:

Sample Size Calculation Tools

Version:

1.0.0

Description:

Functions for sample size estimation and simulation in clinical trials. Includes methods for selecting the best group using the Indifference-zone approach, as well as designs for non-inferiority, equivalence, and negative binomial models. For the sample size calculation for non-inferiority of vaccines, the approach is based on Fleming, Powers, and Huang (2021) <doi:10.1177/1740774520988244>. The Indifference-zone approach is based on Sobel and Huyett (1957) <doi:10.1002/j.1538-7305.1957.tb02411.x> and Bechhofer, Santner, and Goldsman (1995, ISBN:978-0-471-57427-9).

License:

AGPL (≥ 3)

Encoding:

UTF-8

RoxygenNote:

7.3.2

Depends:

R (≥ 4.1)

Suggests:

knitr, rmarkdown, spelling, testthat (≥ 3.0.0)

Language:

en-US

Imports:

stats, stringr, broom, MASS, gsDesign, mvtnorm, tibble

Config/testthat/edition:

VignetteBuilder:

knitr

URL:

https://johnaponte.github.io/ssutil/, https://github.com/johnaponte/ssutil

BugReports:

https://github.com/johnaponte/ssutil/issues

NeedsCompilation:

Packaged:

2025-06-10 07:44:40 UTC; master

Author:

John J. Aponte

[aut, cre], Chris Gast [ctb]

Maintainer:

John J. Aponte <john.j.aponte@gmail.com>

Repository:

CRAN

Date/Publication:

2025-06-12 14:10:02 UTC

ssutil: Utilities for Sample Size calculation

Description

Utilities for the sample size estimation and simulations of clinical trials.

Author(s)

Maintainer: John J. Aponte john.j.aponte@gmail.com (ORCID)

Other contributors:

Chris Gast cgast@path.org [contributor]

Create an Empirical Power Result object

Description

Constructs an S3 object of class empirical_power_result, storing the estimated power, its confidence interval, and the number of simulations used to compute it.

Usage

empirical_power_result(x, n, conf.level = 0.95)

Arguments

x

Number of successes

n

Number of trials.

conf.level

Confidence level for the returned confidence interval power.

Details

It is a wrap to binom.test

Value

An object of class empirical_power_result, a list with components:

power: Estimated power.
conf.low: Lower bound of confidence interval.
conf.high: Upper bound of confidence interval.
conf.level: Confidence level for the returned confidence interval.
nsim: Number of simulations.

Examples

result <- empirical_power_result(
  x = 10,
  n = 100,
  conf.level = 0.95
)
print(result)

Format method for power_single_rate class

Description

Format method for power_single_rate class

Usage

## S3 method for class 'power_single_rate'
format(x, digits = 3, ...)

Arguments

x

an R object of class power_single_rate

digits

a positive integer indicating how many significant digits are to be used for numeric x.

...

further arguments passed to or from other methods

Value

A character string with a human-readable summary of the detection power or a markdown-style table, depending on the number of rows.

Check if an object is a sim_power_result

Description

Check if an object is a sim_power_result

Usage

is.empirical_power_result(x)

Arguments

x

Any R object.

Value

Logical. TRUE if x inherits from "sim_power_result".

Calculate the Multivariate Normal Probability

Description

Computes the multivariate normal probabilities with arbitrary correlation matrices It is the inverse of the multz function

Usage

multp(q, k, rho, seed = NULL)

Arguments

q

Numeric. Quantile of the distribution.

k

Integer. Number of variables in the multivariate normal distribution. Must be >= 1.

rho

Numeric. Common correlation coefficient between variables (typically between 0 and 1).

seed

Optional. An object specifying if and how the random number generator should be initialized. Passed to pmvnorm.

Value

Numeric. The multivariate probability

Examples

q <- 1.3      
k <- 3        
rho <- 0.5    
multp(q, k, rho)

Calculate the Upper Equicoordinate Point of a Multivariate Normal Distribution

Description

Computes the upper equicoordinate quantile for a multivariate standard normal distribution with unit variances and a common correlation coefficient rho. That is, it returns the value z such that the joint probability P(X_1 \le z, \ldots, X_n \le z) = 1 - \alpha.

Usage

multz(alpha, k, rho, seed = NULL)

Arguments

alpha

Numeric. Significance level (e.g., 0.05 for a 95% confidence level).

k

Integer. Number of variables in the multivariate normal distribution. Must be >= 1.

rho

Numeric. Common correlation coefficient between variables (typically between 0 and 1).

seed

Optional. An object specifying if and how the random number generator should be initialized. Passed to qmvnorm.

Value

Numeric. The upper equicoordinate point z such that the joint probability of all variables being less than or equal to z is 1 - \alpha.

Examples

alpha <- 0.1  # Significance level (10%)
k <- 3        # Number of variables
rho <- 0.5    # Common correlation coefficient
multz(alpha, k, rho)

Power to Correctly Select the Best Group in a Binomial Test

Description

Computes the exact probability of correctly identifying the best group when the outcome follows a binomial distribution. It assumes that p1 is the probability of success in the best group, and that the success probability in all other groups is lower by a fixed difference dif.

Usage

power_best_binomial(p1, dif, ngroups, npergroup)

Arguments

p1

Numeric. Probability of success in the best group (must be in [0, 1]).

dif

Numeric. Difference in success probability between the best group and the next best (must be > 0).

ngroups

Integer. Number of groups (must be greater than 1).

npergroup

Integer. Number of subjects per group (must be positive).

Details

The formula is based on the exact method described by Sobel and Huyett (1957).

Value

A numeric value representing the probability of correctly identifying the best group.

References

Sobel, M., & Huyett, M. J. (1957). Selecting the Best One of Several Binomial Populations. Bell System Technical Journal, 36(2), 537–576. doi:10.1002/j.1538-7305.1957.tb02411.x

Examples

power_best_binomial(p1 = 0.8, dif = 0.2, ngroups = 4, npergroup = 50)

Power calculation for the Indifferent-zone approach for normal outcomes

Description

Estimate the probability of correctly select the best group among ngroups groups if the difference between the best group and the next best is at least dif(the Indifferent-Zone), and the standard deviation is sd

Usage

power_best_normal(dif, sd, ngroups, npergroup, seed = NULL)

Arguments

dif

Numeric. Indifferent-zone. Minimum difference that is considered meaningful.

sd

Numeric. Common standard deviation of the response variable.

ngroups

Integer. Number of groups (treatments) being compared.

npergroup

Integer. Number in each group.

seed

Optional. Integer seed to use in the internal call to multp().

Value

Integer. Sample size required per group to achieve the specified power.

Note

The function uses the quantile function multp(), which computes critical values for the selection procedure. This implementation assumes equal variances and independent samples.

Examples

power_best_normal(dif = 0.5, sd = 1, ngroups = 3, npergroup = 11)

Detectable Event Rate with Specified Power and Sample Size

Description

Estimates the minimum true proportion of events needed to detect at least one event, given a sample size and desired statistical power.

Usage

power_single_rate(subjects, power)

Arguments

subjects

Integer or vector of integers. Sample size(s).

power

Numeric or vector of numerics. Desired power(s), between 0 and 1.

Value

A matrix of class power_single_rate with columns:

n: Sample size
power: Requested power
proportion: Minimum detectable event rate to observe at least one event

Examples

power_single_rate(30, 0.9)
power_single_rate(c(30, 50, 100), 0.9)

Print method for empirical_power_result

Description

Nicely formats the output of an object of class empirical_power_result, showing the power estimate, confidence interval, and number of simulations.

Usage

## S3 method for class 'empirical_power_result'
print(x, ...)

Arguments

x

An object of class "empirical_power_result".

...

Further arguments passed to or from other methods (ignored).

Value

Invisibly returns the object passed in.

Print method for class power_single_rate

Description

Print method for class power_single_rate

Usage

## S3 method for class 'power_single_rate'
print(x, ...)

Arguments

x

an object of class power_single_rate

...

further arguments passed to or from other methods

Value

Invisibly returns the object passed in.

Calculate Event Probability in the Experimental Group Given a Hazard Ratio

Description

Computes the event probability in the experimental group based on the event probability in the control group and a specified hazard ratio, assuming proportional hazards.

Usage

prophr(p0, hr)

Arguments

p0

Numeric scalar. Probability of an event in the control group (between 0 and 1).

hr

Numeric scalar. Hazard ratio (must be > 0).

Details

This is useful for sample size calculations, for example in PASS (TM), which does not automatically adjust the event rate for the experimental group.

Value

Numeric. The probability of an event in the experimental group.

Examples

prophr(0.05, 0.6)

Simulate Power to Rank the Best Group Using Binomial Outcomes

Description

Estimates the empirical power to rank the most promising group as the best, based on binomial outcomes, via simulation.

Usage

sim_power_best_bin_rank(
  noutcomes,
  p1,
  dif,
  weights,
  ngroups,
  npergroup,
  nsim,
  conf.level = 0.95
)

Arguments

noutcomes

Integer. Number of outcomes to evaluate.

p1

Numeric. Event probability in the best group (scalar or vector of length noutcomes).

dif

Numeric. Difference between the best group and the rest (scalar or vector of length noutcomes).

weights

Numeric vector. Weights for each outcome. If scalar, applied equally.

ngroups

Integer. Number of groups.

npergroup

Integer or vector. Sample size per group.

nsim

Integer. Number of simulations.

conf.level

Numeric. Confidence level for the empirical power estimate#'

Details

Each outcome is assumed to follow an independent binomial distribution. The best group is defined as having a probability at least dif higher than the other groups. The function sums weighted ranks across multiple outcomes to determine the top group.

If multiple outcomes are defined, weights can be applied to prioritize some outcomes over others. Weights are automatically scaled to sum 1. The group with the lowest total rank is considered the best.

Value

An S3 object of class empirical_power_result, which contains the estimated empirical power and its confidence interval. The object can be printed, formatted, or further processed using associated S3 methods. See also empirical_power_result.

Examples

  sim_power_best_bin_rank(
  noutcomes = 2,
  p1 = 0.80,
  dif = 0.15,
  weights = 1,
  ngroups = 3,
  npergroup = 30,
  nsim = 1000,
  conf.level = 0.95)

Simulate Power to Select the Best Group Using Binomial Outcomes

Description

Estimates the empirical power to correctly identify the best group as having the highest outcome, under a binomial distribution. Assumes that the most promising group has a higher success probability than the others by at least dif, and that outcomes are independent.

Usage

sim_power_best_binomial(
  noutcomes,
  p1,
  dif,
  ngroups,
  npergroup,
  nsim,
  conf.level = 0.95
)

Arguments

noutcomes

Integer. Number of outcomes to evaluate.

p1

Numeric. Probability in the most promising group (scalar or vector).

dif

Numeric. Difference between the best group and the rest.

ngroups

Integer. Number of groups.

npergroup

Integer or vector. Number of subjects per group.

nsim

Integer. Number of simulations.

conf.level

Numeric. Confidence level for the empirical power estimate

Details

Multiple outcomes can be evaluated simultaneously. The power is estimated as the proportion of simulations where the most promising group is selected best in all outcomes.

Value

Examples

sim_power_best_binomial(
  noutcomes = 1,
  p1 = 0.7,
  dif = 0.2,
  ngroups = 3,
  npergroup = 30,
  nsim = 1000
)

Simulate Power to Select Best Group by Ranks (Normal Outcomes)

Description

Estimates the empirical power to identify the most promising group as best, using weighted ranks across outcomes, assuming normally distributed outcomes.

Usage

sim_power_best_norm_rank(
  noutcomes,
  sd,
  dif,
  weights,
  ngroups,
  npergroup,
  nsim,
  conf.level = 0.95
)

Arguments

noutcomes

Integer. Number of outcomes to evaluate.

sd

Numeric vector. Standard deviations for each outcome.

dif

Numeric vector. Difference in means between the best and other groups.

weights

Numeric vector. Weights per outcome.

ngroups

Integer. Number of groups.

npergroup

Integer or vector. Number of subjects per group.

nsim

Integer. Number of simulations.

conf.level

Numeric. Confidence level for the empirical power estimate

Details

Each outcome is independent and normally distributed. The most promising group is assumed to have a mean at least dif higher than the others. Ranks are weighted and summed per group across outcomes.

If weights is specified, it is internally scaled to sum to 1. The most promising group is always considered to be the first group.

Value

Examples

sim_power_best_norm_rank(
  noutcomes = 3,
  sd = c(1, 0.8, 1.5),
  dif = c(0.2, 0.15, 0.3),
  weights = c(0.5, 0.3, 0.2),
  ngroups = 3,
  npergroup = c(30, 25, 25),
  nsim = 1000
)

Simulate Power to Select Best Group (Normal Outcomes)

Description

Estimates the empirical power to identify the most promising group as the best, when outcomes are normally distributed and independent.

Usage

sim_power_best_normal(
  noutcomes,
  sd,
  dif,
  ngroups,
  npergroup,
  nsim,
  conf.level = 0.95
)

Arguments

noutcomes

Integer. Number of outcomes to evaluate.

sd

Numeric vector. Standard deviations for each outcome. Can be a single value.

dif

Numeric vector. Difference in means between the best and the other groups.

ngroups

Number of groups to compare.

npergroup

Number of subjects per group. Can be scalar or vector of length ngroups.

nsim

Integer. Number of simulations to perform.

conf.level

Numeric. Confidence level for the empirical power estimate

Details

The best group (group 1) is assumed to have mean 0, and the rest of the groups have mean -dif.

Multiple outcomes can be evaluated simultaneously. The power is estimated as the proportion of simulations where the most promising group is the best in all outcomes.

The number of subjects per group can be the same or specified per group. In either case, the first group is assumed to be the most promising.

Value

Examples

  sim_power_best_normal(
   noutcomes = 2,
   sd = c(1, 1.2),
   dif = c(0.2, 0.25),
   ngroups = 3,
   npergroup = c(30, 25, 25),
   nsim = 1000
  )

Empirical Power for Equivalence (Normal Outcomes)

Description

Estimates the empirical power to detect equivalence among multiple groups assuming no true difference in normally distributed outcomes. Pairwise two-sample t-tests are used, and equivalence is declared if all confidence intervals for differences between group means lie entirely within the interval defined by llimit and ulimit.

Usage

sim_power_equivalence_normal(
  ngroups,
  npergroup,
  sd,
  llimit,
  ulimit,
  nsim,
  t_level = 0.95,
  conf.level = 0.95
)

Arguments

ngroups

Integer. Number of groups to compare

npergroup

Integer. Number of observations per group.

sd

Numeric. Standard deviation of the outcome distribution (common across groups).

llimit

Numeric. Lower equivalence limit.

ulimit

Numeric. Upper equivalence limit.

nsim

Integer. Number of simulations to perform.

t_level

Numeric. Confidence level used for the t-tests (e.g., 0.95 for 95% CI).

conf.level

Numeric. Confidence level for the empirical power estimate

Details

This function simulates data under the null hypothesis of no difference between groups and calculates the proportion of simulations in which all pairwise comparisons fall within the specified equivalence limits.

Value

Examples

#Equivalence testing for three groups with log-scale outcome
sim_power_equivalence_normal(
  ngroups = 3,
  npergroup = 172,
  sd = 0.403,
  llimit = log10(2/3),
  ulimit = log10(3/2),
  nsim = 1000,
  t_level = 0.95
)

Empirical Power for Negative Binomial Comparison

Description

Estimates empirical power to detect a relative risk either above or below a specified boundary, depending on the direction of the alternative hypothesis. Simulates count data with over dispersion, fits a model with glm.nb, and evaluates the power to reject the null hypothesis using a negative binomial model.

Usage

sim_power_nbinom(
  n1,
  n2,
  ir1,
  tm,
  rr,
  boundary,
  dispersion,
  alpha,
  nsim,
  conf.level = 0.95
)

Arguments

n1

Integer. Number of participants in group 1.

n2

Integer. Number of participants in group 2.

ir1

Numeric. Incidence rate in group 1.

tm

Numeric. Average exposure time per subject (assumed equal across subjects).

rr

Numeric. True relative risk between groups (group 2 rate = rr × group 1 rate).

boundary

Numeric. Relative risk boundary under the null hypothesis.

dispersion

Numeric. Dispersion parameter (\phi) for the negative binomial distribution.

alpha

Numeric. Type I error rate (two-sided).

nsim

Integer. Number of simulation iterations.

conf.level

Numeric. Confidence level for the empirical power estimate

Value

Note

Uses the alternative parameterization of the negative binomial: mu is the mean, and size = 1/dispersion. In glm.nb, dispersion is estimated as theta. The 'boundary' parameter defines the relative risk under the null hypothesis. When rr < 1, rejection occurs if the upper limit of the confidence interval is below the boundary. When rr > 1, rejection occurs if the lower limit is above the boundary.

The alpha parameter is two-sided as it is used to estimate two-sided confidence intervals

Author(s)

Chris Gast

John J. Aponte

Examples


sim_power_nbinom(
 n1 = 150, n2 = 150,
 ir1 = 0.55, tm = 1.7,
 rr = 0.6, boundary = 1,
 dispersion = 2,
 alpha = 0.05,
 nsim = 1000
)

Empirical Power for Non-Inferiority (Normal Outcomes)

Description

Estimates empirical power to declare non-inferiority between two groups across multiple outcomes using t-tests. Simulates normally distributed data under the null (no difference) and applies non-inferiority rules based on user-defined required and optional tests.

Usage

sim_power_ni_normal(
  nsim,
  npergroup,
  ntest,
  ni_limit,
  test_req,
  test_opt,
  sd,
  corr = 0,
  t_level = 0.95,
  conf.level = 0.95
)

Arguments

nsim

Integer. Number of simulations to perform.

npergroup

Integer. Number of observations per group.

ntest

Integer. Number of tests (outcomes) to compare.

ni_limit

Numeric. Limit to declare non-inferiority. Can be a scalar or vector of length ntest.

test_req

Integer. Number of required tests that must show non-inferiority (first test_req tests).

test_opt

Integer. Number of optional tests that must also show non-inferiority from the remaining tests.

sd

Numeric. Standard deviation(s) of the outcomes. Scalar or vector of length ntest.

corr

Numeric. Correlation between the tests. Scalar (common correlation), or vector of length ntest*(ntest-1)/2.

t_level

Numeric. Confidence level used for the t-tests (e.g., 0.95 for 95% CI).scalar or vector of length ntest.

conf.level

Numeric. Confidence level for the empirical power estimate

Details

A test is considered non-inferior if the lower bound of its confidence interval is greater than the specified non-inferiority limit. Overall non-inferiority is declared if all test_req and at least test_opt of the remaining tests are non-inferior.

Value

Note

If only one test is used, correlation is ignored.

Use correlation 0 for independent outcomes

When using a correlation vector, it must match the number of test pairs: ntest*(ntest-1)/2, in this order: (1,2), (1,3), ..., (1,ntest), (2,3), ..., (ntest-1,ntest).

The covariance matrix is derived from the correlation matrix and the standard deviations.

For example: with ntest = 3 and corr = c(0.2, 0.3, 0.4), the resulting correlation matrix is:

	[,1]	[,2]	[,3]
[1, ]	1	0.2	0.3
[2, ]	0.2.	1	0.4
[3, ]	0.3.	0.4	1

Examples

sim_power_ni_normal(
  nsim = 1000,
  npergroup = 250,
  ntest = 7,
  ni_limit = log10(2/3),
  test_req = 2,
  test_opt = 3,
  sd = 0.4,
  corr = 0,
  t_level = 0.05
)

Sample Size to Select the Best Group in a Binomial Test

Description

Computes the minimum sample size per group required to achieve a target probability of correctly selecting the best group in a binomial test. The best group is assumed to have success probability p1, and the other groups have p1 - dif.

Usage

ss_best_binomial(power, p1, dif, ngroups, max_n = 1000)

Arguments

power

Numeric. Desired probability of correctly selecting the best group (in [0, 1]).

p1

Numeric. Probability of success in the best group (in [0, 1]).

dif

Numeric. Difference in success probability with the next best group (> 0).

ngroups

Integer. Number of groups (must be > 1).

max_n

Integer. Maximum sample size to evaluate (default is 1000).

Details

The function searches for the smallest npergroup such that the power from power_best_binomial is at least the target power.

Value

An integer representing the minimum sample size per group required to reach the specified power.

Examples

ss_best_binomial(power = 0.9, p1 = 0.8, dif = 0.2, ngroups = 4)

Sample Size for Selecting the Best Treatment in a Normal Response (Indifference-Zone)

Description

Calculates the minimum common sample size per group needed to achieve a specified probability (power) of correctly selecting the best group using the indifference-zone approach. This method assumes normally distributed responses with a known and common standard deviation.

Usage

ss_best_normal(power, dif, sd, ngroups, seed = NULL)

Arguments

power

Numeric. Desired probability of correctly selecting the best group.

dif

Numeric. Indifferent-zone. Minimum difference that is considered meaningful.

sd

Numeric. Common standard deviation of the response variable.

ngroups

Integer. Number of groups (treatments) being compared.

seed

Optional. Integer seed to use in the internal call to multz().

Details

The indifference-zone approach guarantees that the probability of correct selection is at least power, assuming the best group's mean exceeds the others by at least dif. The calculation is based on Bechhofer's Procedure Nb.

Value

Integer. Sample size required per group to achieve the specified power.

Note

The function uses the quantile function multz(), which computes critical values for the selection procedure. This implementation assumes equal variances and independent samples.

References

Bechhofer, R.E., Santner, T.J., & Goldsman, D.M. (1995). Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons. Wiley Series in Probability and Statistics. ISBN: 0-471-57427-9.

Examples

ss_best_normal( power = 0.8, dif = 0.5, sd = 1, ngroups = 3)

Sample Size and Non-Inferiority Margin for Vaccine Efficacy Trials

Description

Computes the non-inferiority margin, number of events, and maximum hazard ratio (HR) to declare non-inferiority in vaccine efficacy (VE) trials, based on the approach described by Fleming et al. (2021).

Usage

ss_ni_ve(ve_lci, alpha = 0.025, power = 0.9, use70 = FALSE, preserve = 0.5)

Arguments

ve_lci

Numeric. Lower bound of the current vaccine's efficacy (e.g., 0.95 for 95% VE).

alpha

Numeric. Type I error rate (default = 0.025).

power

Numeric. Desired power for the test (default = 0.90).

use70

Logical. If TRUE, assumes at least 30% VE for the new vaccine (the 90–70 rule); otherwise, preserves a fixed fraction of the reference VE.

preserve

Numeric. Proportion of the current vaccine's efficacy to preserve under use70 = FALSE (default = 0.5).

Details

The method applies either the 95–95 rule or 90–70 rule, depending on whether a minimum VE of 30% is assumed (use70 = TRUE) or 50% of the current VE is preserved.

This implementation approximates Table 1 of the paper using exact binomial confidence intervals via binom.test and the nBinomial1Sample function from gsDesign.

Value

A named list with:

Upper limit of the HR used to estimate the sample size: Hazard ratio corresponding to ve_lci.
Non-inferior margin in HR scale: Non-inferiority margin expressed as a hazard ratio.
Alpha: The type I error used.
Power: The power used.
Total number of events: Total number of events required in the trial.
Max HR to declare NI: Maximum observed hazard ratio that satisfies the non-inferiority criterion.
Max number of events in the experimental group: Maximum number of events in the experimental group still compatible with non-inferiority.
Non-inferior criteria: Description of the applied non-inferiority rule ("At least 30% VE" or "or preserved effect").

References

Fleming, T.R., Powers, J.H., & Huang, Y. (2021). The use of active controls and non-inferiority studies in evaluating COVID-19 vaccines. Clinical Trials, 18(3), 335–342. doi:10.1177/1740774520988244

Examples

ss_ni_ve(ve_lci = 0.95)

Tidy Method for empirical_power_result

Description

Creates a one-row tibble with the power estimate and confidence interval.

Usage

## S3 method for class 'empirical_power_result'
tidy(x, ...)

Arguments

x

A empirical_power_result object.

...

Ignored.

Value

A tibble with columns: power, conf.low, conf.high, conf.level. nsim.

Worst‐Case Scenario Power for the Best Binomial Group

Description

Searches for the probability in the best‐performing group that yields the lowest statistical power, given an indifference zone specification, a number of groups, and a number of subjects per group.

Usage

wcs_power_best_binomial(dif, ngroups, npergroup)

Arguments

dif

Numeric. Indifference zone specification (difference threshold).

ngroups

Integer. Number of groups to compare.

npergroup

Integer. Number of subjects per group.

Details

Defines an internal function fx that wraps power_best_binomial with the supplied parameters, then uses optimize over the interval [0,1] to find the probability p1 that minimizes the resulting power.

Value

A named list with components:

p1: Numeric. Probability in the best group that yields the minimum power.
minimum_power: Numeric. The minimum power achieved at p1.

Examples

wcs_power_best_binomial(dif = 0.1, ngroups = 3, npergroup = 50)

ssutil: Utilities for Sample Size calculation

Description

Author(s)

See Also

Create an Empirical Power Result object

Description

Usage

Arguments

Details

Value

Examples

Format method for power_single_rate class

Description

Usage

Arguments

Value

Check if an object is a sim_power_result

Description

Usage

Arguments

Value

Calculate the Multivariate Normal Probability

Description

Usage

Arguments

Value

Examples

Calculate the Upper Equicoordinate Point of a Multivariate Normal Distribution

Description

Usage

Arguments

Value

Examples

Power to Correctly Select the Best Group in a Binomial Test

Description

Usage

Arguments

Details

Value

References

Examples

Power calculation for the Indifferent-zone approach for normal outcomes

Description

Usage

Arguments

Value

Note

Examples

Detectable Event Rate with Specified Power and Sample Size

Description

Usage

Arguments

Value

Examples

Print method for empirical_power_result

Description

Usage

Arguments

Value

Print method for class power_single_rate

Description

Usage

Arguments

Value

Calculate Event Probability in the Experimental Group Given a Hazard Ratio

Description

Usage

Arguments

Details

Value

Examples

Simulate Power to Rank the Best Group Using Binomial Outcomes

Description

Usage

Arguments

Details

Value

See Also

Examples

Simulate Power to Select the Best Group Using Binomial Outcomes