inst/doc/epiR_sample_size-html.R

In epiR: Tools for the Analysis of Epidemiological Data

## ----echo = FALSE, message = FALSE--------------------------------------------

# Use tinytex (instead of MiKTex) to generate PDFs. See -Tools -Global options -Sweave. Use tinytex.
# tinytex::install_tinytex()

# Use this code to update packages:
# tinytex::tlmgr_update(self = TRUE, all = TRUE)

knitr::opts_chunk$set(collapse = T, comment = "#>")
options(tibble.print_min = 4L, tibble.print_max = 4L)
flextable::set_flextable_defaults(fonts_ignore = TRUE)

# Settings to get warning messages to wrap:
knitr::opts_chunk$set(comment = "#>")

wrap_hook <- function(type, width = 80) {
  old <- knitr::knit_hooks$get(type)
  function(x, options) {
    pre <- options$comment
    x <- unlist(strsplit(x, "\n"))
    x <- sub(paste0("^", pre, " ?"), "", x)          # strip the comment prefix
    x <- strwrap(paste(x, collapse = " "), width = width - nchar(pre) - 1)
    x <- paste0(paste(pre, x, collapse = "\n"), "\n") # re-add prefix to each line
    old(x, options)
  }
}

knitr::knit_hooks$set(
  warning = wrap_hook("warning"),
  message = wrap_hook("message"),
  error   = wrap_hook("error")
)

## ----message = FALSE----------------------------------------------------------
library(epiR)
epi.sssimpleestb(N = NA, Py = 0.15, epsilon = 0.20, error = "relative", 
   se = 1, sp = 1, nfractional = FALSE, conf.level = 0.95)

## ----message = FALSE----------------------------------------------------------
epi.sssimpleestb(N = 4000, Py = 0.15, epsilon = 0.20, error = "relative", 
   se = 1, sp = 1, nfractional = FALSE, conf.level = 0.95)

## ----message = FALSE----------------------------------------------------------
epi.sscohortt(irexp1 = 70/1000, irexp0 = 50/1000, FT = 5, n = NA, 
   power = 0.80, r = 1, design = 1, sided.test = 2, nfractional = FALSE, 
   conf.level = 0.95)$n.total

## ----message = FALSE----------------------------------------------------------
epi.sscohortt(irexp1 = 70/1000, irexp0 = 50/1000, FT = 5, n = 1500, 
   power = NA, r = 1, design = 1, sided.test = 2, nfractional = FALSE, 
   conf.level = 0.95)$power

## ----message = FALSE----------------------------------------------------------
epi.sscc(N = NA, OR = 2.0, p1 = NA, p0 = 0.30, n = NA, power = 0.80, 
   r = 1, phi.coef = 0, design = 1, sided.test = 2, conf.level = 0.95, 
   method = "unmatched", nfractional = FALSE, fleiss = FALSE)$n.total

## ----message = FALSE----------------------------------------------------------
epi.ssninfb(treat = 0.85, control = 0.65, delta = 0.10, n = NA, 
   r = 1, power = 0.80, nfractional = FALSE, alpha = 0.05)$n.total

## ----message = FALSE----------------------------------------------------------
epi.ssclus1estb(N.psu = NA, b = 75, Py = 0.46, epsilon = 0.10, 
   error = "relative", rho = 0.20, nfractional = FALSE, conf.level = 0.95)$n.psu

## ----message = FALSE----------------------------------------------------------
epi.ssclus1estb(b = c(75,35), Py = 0.46, epsilon = 0.10, 
   error = "relative", rho = 0.20, nfractional = FALSE, conf.level = 0.95)$n.psu

## ----message = FALSE----------------------------------------------------------
# From first principles:
n.crude <- epi.sssimpleestb(N = NA, Py = 0.20, epsilon = 0.05 / 0.20, 
   error = "relative", se = 1, sp = 1, nfractional = FALSE, conf.level = 0.95)
n.crude

# A total of 246 individuals (SSUs) need to be enrolled into the study. 
# Calculate the design effect:
rho <- 0.02; b <- 20
D <- rho * (b - 1) + 1; D
# The design effect is 1.38 so our crude sample size estimate needs to be 
# increased by a factor of 1.38.

n.adj <- ceiling(n.crude * D)
n.adj
# After accounting for lack of independence in the data a total of 340 
# individuals (SSUs) need to be enrolled into the study. 

# How many villages (PSUs) are required?
ceiling(n.adj / b)
# A total of 17 villages need to be sampled to meet the requirements of 
# the study.

# Do all of the above using epi.ssclus2estb:
epi.ssclus2estb(N.psu = NA, b = 20, Py = 0.20, epsilon = 0.05 / 0.20, 
   error = "relative", rho = 0.02, nfractional = FALSE, conf.level = 0.95)

## ----message = FALSE----------------------------------------------------------
n.crude <- epi.sssimpleestb(N = NA, Py = 0.15, epsilon = 0.20,
   error = "relative", se = 1, sp = 1, nfractional = FALSE, conf.level = 0.95)
n.crude

rho <- 0.09; b <- 10
D <- rho * (b - 1) + 1; D

n.adj <- ceiling(n.crude * D)
n.adj

# Similar to the example above, we can do all of these calculations 
# using epi.ssclus2estb:
epi.ssclus2estb(N.psu = NA, b = 10, Py = 0.15, epsilon = 0.20, 
   error = "relative", rho = 0.09, nfractional = FALSE, conf.level = 0.95)