Calculate optimized sample size for detecting differential transmission

Function to calculate optimized sample size by solving the transcendental equation that occurs when you replace the R values with ones that account for sensitivity and specificity.

relR_samplesize_simsolve(
  R_a,
  R_b,
  p_a,
  N,
  alpha = 0.05,
  alternative = c("two_sided", "less", "greater"),
  power = 0.8,
  sensitivity = 1,
  specificity = 1,
  overdispersion = NULL,
  epsilon = 0.01,
  nsims = 1e+05,
  tolerance = 10
)

Arguments

R_a: Numeric (Positive). The assumed R among the group in the denominator of the ratio. Input value must be greater than 0.
R_b: Numeric (Positive). The assumed R among the group in the numerator of the ratio. Input value must be greater than 0.
p_a: Numeric. The proportion of the population in group a. Must be between 0 and 1.
N: Numeric (Positive). The size of the infected pool. Only one of rho or N should be specified.
alpha: Numeric. The desired alpha level. Default: 0.05
alternative: Character. Specifies the alternative hypothesis. Must be: two_sided (Default), less, or greater
power: Numeric. The desired power. Must be a value between 0 and 1. Default: 0.8.
sensitivity: Numeric. The sensitivity of the linkage criteria. Must be between 0 and 1. Default: 1.
specificity: Numeric. The specificity of the linkage criteria. Must be between 0 and 1. Default: 1.
overdispersion: Numeric (Positive). An overdispersion parameter, set if the assumed distribution of the number of edges is negative binomial. If NULL the assumed distribution is Poisson (equivalent to an overdispersion parameter of infinity) Default: NULL Note that this is equivalent to setting the overdispersion parameter to Inf.
epsilon: Numeric. Dictates the minimum value for R_b = R_a + epsilon attempted in the simulation. Default: 0.01.
nsims: Dictates the number of simulations for each power simulation. Default: 100000
tolerance: Dictates the tolerance for the binary search. Default: 10.

Value

Simulated sample size needed achieve desired type I and II error rates under assumptions. Will return NA and throw a warning if impossible.