dsr: Confidence intervals for age standardized rates and rate...

In tagteam/heaven: Data Preparation Routines for Medical Registry Data

Confidence intervals for age standardized rates and rate ratios

Description

Function to Compute confidence interval for directly standardized rates and rate ratios

Usage

dsr(
  count1,
  pop1,
  count0,
  pop0,
  stdpop,
  conf.level = 0.95,
  method = "gamma",
  crude = TRUE
)

Arguments

count1	counts for group 1 (e.g. exposed)
pop1	number of subjects of person-years in group 1
count0	counts for group 1 (e.g. exposed)
pop0	number of subjects of person-years in group 0
stdpop	number of subjects of person-years in stdpop population
conf.level	confidence level of confidence intervals
method	method for calculating confidence intervals
crude	logical. if TRUE also calculate crude rates

Details

Function to Compute confidence interval for directly standardized rates and rate ratios for sparse data. Method implemented include gamma confidence intervals (for DSR), exact confidence intervals (for crude rates), the inverse of the F distribution (for DSR ratio) and some Wald confidence interval (also on log-scale) for comparison purpose.

Value

List with crude and standardized rates and rate ratios.

Author(s)

Paul F Blanche <[e-mail]> and Thomas A. Gerds <[e-mail]>

References

Fay, Michael P., and Eric J. Feuer. "Confidence intervals for directly standardized rates: a method based on the gamma distribution." Statistics in Medicine 16.7 (1997): 791-801.

Fay, Michael P. "Approximate confidence intervals for rate ratios from directly standardized rates with sparse data." Communications in Statistics-Theory and Methods 28.9 (1999): 2141-2160.

Fay, Michael P., et al. "Estimating average annual percent change for disease rates without assuming constant change." Biometrics 62.3 (2006): 847-854.

Fay, Michael P., Michael A. Proschan, and Erica Brittain. Combining one-sample confidence procedures for inference in the two-sample case. Biometrics 71.1 (2015): 146-156.

Interesting other approaches that avaoid inconsistency between exact CI and p-values are implemented in exactci and exact2x2, see

Fay, Michael P. "Two-sided exact tests and matching confidence intervals for discrete data." R journal 2.1 (2010): 53-58.

See Also

epitools::ageadjust.direct

Examples

library(riskRegression)
library(data.table)
set.seed(84)
 n=160
d <- data.table(e1=rpois(n,lambda=9),
                rt1=rpois(n,lambda=1880),
                e2=rpois(n,lambda=123),
                rt2=rpois(n,lambda=80000))
d[,agegroups:=factor(rep(c("40-50","45-50","50-55","55-60","60-65","65-70","70-75","75-80"),n/8))]
d[,sex:=factor(rep(c("f","m"),c(n/2,n/2)))]
d[,year:=rep(2001:2010,n/10)]
D=d[,.(e1=sum(e1),rt1=sum(rt1),e2=sum(e2),rt2=sum(rt2)),by=c("sex","agegroups")]
D[sex=="m",e1:=e1+rpois(.N,lambda=as.numeric(agegroups)*17)]
D[sex=="m",rt1:=rt1-rpois(.N,lambda=as.numeric(agegroups)*1600)]
dsr(count1=D[sex=="m",e1], pop1=D[sex=="m",rt1],
count0=D[sex=="f",e1], pop0=D[sex=="f",rt1],
stdpop=D[sex=="f",rt1])

tagteam/heaven documentation built on March 24, 2024, 7:58 a.m.