prec_riskdiff: Sample size or precision for risk difference
In a-lenz/presize: Precision Based Sample Size Calculation

Description Usage Arguments Details References Examples

View source: R/differences.R

prec_riskdiff returns the risk difference and the sample size or the precision for the provided proportions

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prec_riskdiff(p1, p2, n1 = NULL, conf.width = NULL, r = 1,
  conf.level = 0.95, method = c("newcombe", "mn", "ac", "wald"),
  tol = .Machine$double.eps^0.25)

`p1`	Risk among unexposed
`p2`	Risk among exposed
`n1`	Number of patients in unexposed group
`conf.width`	precision (the full width of the conficende interval)
`r`	allocation ratio (relative size of unexposed and exposed cohort (`n1` / `n2`))
`conf.level`	confidence level
`method`	Exactly one of `newcombe` (default), `mn` (Miettinen-Nurminen), `ac` (Agresti-Caffo), `wald`. Methods can be abbreviated.
`tol`	numerical tolerance used in root finding, the default providing (at least) four significant digits

Exactly one of the parameters n1, conf.width must be passed as NULL, and that parameter is determined from the other.

Newcombe (newcombe) proposed a confidence interval based on the wilson score method for the single proportion (see prec_prop). The confidence interval without continuity correction is implemented from equation 10 in Newcombe (1998)

Miettinen-Nurminen (mn) provide a closed from equation for the restricted maximum likelihood estimate . The implementation is based on code provided by Yongyi Min on http://users.stat.ufl.edu/~aa/cda/R/two-sample/R2/index.html

Agresti-Caffo (ac) confidence interval is based on the Wald confidence interval, adding 1 success to each cell of the 2 x 2 table (see Agresti and Caffo 2000).

uniroot is used to solve n for the newcombe, ac, and mn method.

Agresti A (2003) Categorical Data Analysis, Second Edition, Wiley Series in Probability and Statistics, doi:10.1002/0471249688

Agresti A and Caffo B (2000) Simple and Effective Confidence Intervals for Proportions and Differences of Proportions Result from Adding Two Successes and Two Failures, The American Statistician, 54(4):280-288

Miettinen O and Nurminen M (1985) Comparative analysis of two rates, Statistics in Medicine, 4:213-226

Newcombe RG (1998) Interval estimation for the difference between independent proportions: comparison of eleven methods, Statistics in Medicine, 17:873-890

Fagerland MW, Lydersen S, and Laake P (2015). Recommended confidence intervals for two independent binomial proportions, Statistical methods in medical research 24(2):224-254.

# Validate Newcombe (1998)
prec_riskdiff(p1 = 56/70, p2 = 48/80, n1 = 70, r = 70/80, met = "newcombe")  # Table IIa
prec_riskdiff(p1 = 10/10, p2 = 0/10, n1 = 10, met = "newcombe")  # Table IIh

prec_riskdiff(p1 = c(56/70, 9/10, 6/7, 5/56),
              p2 = c(48/80, 3/10, 2/7, 0/29),
              n1 = c(70, 10, 7, 56),
              r = c(70/80, 1, 1, 56/29),
              method = "wald")