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Confidence intervals for rate difference (RD) with independent rates.

Source: R/rdci.R
rdci.Rd

Confidence intervals for comparisons of two binomial or Poisson rates. This convenience wrapper function produces a selection of the methods below as appropriate for the selected distribution (binomial or Poisson) for the rate difference (RD) contrast, with or without continuity adjustment.

  • SCAS (skewness-corrected asymptotic score)

  • Miettinen-Nurminen, Mee, Koopman, Gart-Nam Asymptotic Score methods

  • MOVER Wilson (aka Newcombe Hybrid Score for binomial RD)

  • MOVER Jeffreys

  • Agresti-Caffo (binomial RD only)

  • Approximate normal (Wald) methods (strongly advise this is not used for any purpose but included for reference)

Usage

rdci(
  x1,
  n1,
  x2,
  n2,
  distrib = "bin",
  level = 0.95,
  std_est = TRUE,
  cc = FALSE,
  precis = 8
)

Arguments

x1, x2

Numeric vectors of numbers of events in group 1 & group 2 respectively.

n1, n2

Numeric vectors of sample sizes (for binomial rates) or exposure times (for Poisson rates) in each group.

distrib

Character string indicating distribution assumed for the input data:
"bin" = binomial (default),
"poi" = Poisson.

level

Number specifying confidence level (between 0 and 1, default 0.95).

std_est

logical, specifying if the crude point estimate for the contrast value should be returned (TRUE, default) or the method-specific alternative point estimate consistent with a 0% confidence interval (FALSE).

cc

Number or logical (default FALSE) specifying (amount of) continuity adjustment. Numeric value between 0 and 0.5 is taken as the gamma parameter in Laud 2017, Appendix S2 (cc = TRUE translates to 0.5 for 'conventional' Yates adjustment).

precis

Number (default 8) specifying precision (i.e. number of decimal places) to be used in root-finding subroutine for the score confidence intervals. (Note other methods use closed-form calculations so are not affected.)

References

Laud PJ. Equal-tailed confidence intervals for comparison of rates. Pharmaceutical Statistics 2017; 16:334-348. (Appendix A.4)

Author

Pete Laud, p.j.laud@sheffield.ac.uk