R/methods-moments.R

Defines functions ci.moments

Documented in ci.moments

#' @title Confidence intervals for Directly Standardised Rates using 
#' moment match methods
#' @aliases ci.dobson
#' 
#' @description Confidence intervals for directly standardized 
#' rates based on the approximation proposed by Dobson, Kuulasmaa, 
#' Eberle and Scherer (1991). In addition to the method proposed by 
#' Dobson et al, the various approximations implemented by Ng, 
#' Filardo & Zheng (2008) are implemented.
#' @details Following Dobson et al (1991), an approximate 
#' confidence interval can be obtained as a linear function of the 
#' confidence interval for a single Poisson paramenter 
#'  (\eqn{X = \sum_{i=1}^k X_i}) where the confidence interval 
#'  for this *unweighted*  sum of poisson parameters 
#'  \eqn{\sum_{i=1}^k \theta_i} is  \eqn{\left(X_L,X_U \right)}{(X_L,X_U)}.
#' An approximate confidence interval for the *weighted*
#'  sum of \eqn{\theta} is
#' \deqn{T_L = Y + \sqrt{\frac{\upsilon}{y}}\left(X_L - X\right)}{T_L=\sqrt(\upsilon/y)(X_L-X)}
#' \deqn{T_U = Y + \sqrt{\frac{\upsilon}{y}}\left(X_U - X\right)}{T_U=\sqrt(\upsilon/y)(X_U-X)}
#' A number of methods for estimating \eqn{\left(X_L,X_U\right)}{(X_L,X_U)} 
#' are implemented in `dsrci`
#'
#' - **Dobson (exact)**: `type = "dobson"` (Ng et al method M1)
#' - **Boise-Monson**: `type = "boise.monson"` (Ng et al method M2)
#' - **Normal approximation**: `type = "normal"` (Ng et al method M3)
#' - **Wilson-Hilferty**: `type = "wilson.hilferty"` (Ng et al method M4)
#' - **Byar**: `type = "byar"` (Ng et al method M5)
#' - **Exact Mid-p** `type = "midp"` (Ng et al method M7) - implemented using 
#' `exactci::poison.exact`
#' - **Approximation to Mid-p**: `type = "approx.midp"` (Ng et al method M8)
#' - **Simple approximation to Mid-p**: `type = "simple.midp"` (Ng et al method M9)
#' 
#' @return a vector with the lower and upper bound of the confidence 
#' interval.  The estimate of the directly standardised rate and the 
#' level of confidence are returned as attributes to this vector. 
#' @param x a vector of stratum-specific counts of events
#' @param w a vector of stratum-specific weights
#' @param level confidence level for the returned confidence interval
#' @param type type of approximation for the poisson confidence 
#' interval of the unweighted sum
#' @references 
#' Dobson, AJ, Kuulasmaa, K, Eberle, E and Scherer, J (1991) 
#' 'Confidence intervals for weighted sums of Poisson parameters', 
#' *Statistics in Medicine*, **10**: 457--462.
#' \doi{doi:10.1002/sim.4780100317}
#'  
#' Ng, Filardo, & Zheng (2008). 'Confidence interval estimating 
#' procedures for standardized incidence rates.' 
#' *Computational Statistics and Data Analysis* **52** 3501--3516. 
#' \doi{doi:10.1016/j.csda.2007.11.004}
#' 
#' Fay MP (2010). 'Two-sided Exact Tests and Matching Confidence 
#' Intervals for Discrete Data'.  *R Journal* **2**(1):53--58. 
#' \CRANpkg{exactci}
#' @importFrom exactci poisson.exact
#' @export
ci.moments <- function(x, w, level, type = 
 c("dobson", "boise.monson", "normal", "wilson.hilferty",
   "byar", "midp", "approx.midp", "simple.midp")){
  type = match.arg(type)
  y <- sum(x*w)
  v <- sum(w^2*x)
  X <- sum(x)
  X01 <- X+c(0,1)
  z <- stats::qnorm(alpha(level))
  dobson <- function(CI, Y = y,S = sqrt(v/X), Xt = X){Y +S*(CI - Xt)}
  ci <- dobson(
    switch(type,
    "dobson" = stats::qgamma(alpha(level),X01),
    "boise.monson" = exp(log(X) + z/sqrt(X)),
    "normal" = X + z^2/2 + z * sqrt(X + z^2/4),
    "wilson.hilferty" = X + (2*z^2 + 1)/6 + (0.5 + z *sqrt(X + (z^2+2)/18-0.5)),
    "byar" = X01*(1 - 1/(9*X01) + z/(3*sqrt(X01)))^3,
    "midp" = exactci::poisson.exact(X, midp = TRUE)[['conf.int']],
    "approx.midp" = (X+0.5)*(1 - 1/(9*sqrt(X+0.5))  + z/(3*sqrt(X+0.5))),
    "simple.midp" = (sqrt(X+0.5) + z/2)^2)
    )
  attr(ci, "estimate") <- y
  attr(ci, "level") <- level
  attr(ci, "method.arg") <- type
  ci
  }
mnel/dsrci documentation built on May 22, 2017, 11:58 a.m.