Skip to contents

Convert information level into an approximate sample size requirement for a one-stage design

Source: R/information_to_n.R
information_to_n.Rd

These functions provide an asymptotic approximation to the information (i.e. precision, inverse of the variance) provided by two samples under assumed values of nuisance parameters for continuous and binary outcomes.

Usage

information_to_n_continuous_1_to_1(
  information,
  sigma_0,
  sigma_1,
  round_up = TRUE
)

information_to_n_binary_1_to_1(
  information,
  pi_0 = NULL,
  pi_1 = NULL,
  delta = NULL,
  round_up = TRUE
)

Arguments

information

Numeric vector containing the information level

sigma_0

Variance of outcomes in the population of individuals receiving the control intervention

sigma_1

Variance of outcomes in the population of individuals receiving the active intervention

round_up

Logical scalar: should the sample size be rounded up to an integer value?

pi_0

Probability of event in the population of individuals receiving the control intervention

pi_1

Probability of event in the population of individuals receiving the control intervention

delta

The risk difference (i.e. pi_1 - pi_0)

Value

When all parameters are scalars, the result is a scalar, indicating the approximate sample size requirement. When multiple values are specified, a grid of unique parameters are constructed, and the approximate information is computed for each value of the parameters.

See also

asymptotic_information_difference_means(), asymptotic_information_difference_proportions(), and asymptotic_information_mann_whitney_fm() for an asymptotic approximation of the information for a given sample size and values of the nuisance parameters.

Examples

# To add