epitools (version 0.5-2)

ageadjust.indirect: Age standardization by indirect method, with exact confidence intervals

Description

Calculates age standardized (adjusted) rates and "exact" confidence intervals using the indirect method

Usage

ageadjust.indirect(count, pop, stdcount, stdpop, stdrate = NULL,
conf.level = 0.95)

Arguments

count

vector of age-specific count of events

pop

vector of age-specific person-years or population estimates

stdcount

vector of age-specific standard counts

stdpop

vector of age-specific standarad population

stdrate

vector of age-specific standard rates

conf.level

confidence level (default = 0.95)

Value

• $sir
  observed, expected, standardized incidence ratio, and confidence interval
• $rate
  crude.rate, adjusted rate, and confidence interval

Details

To make valid comparisons between rates from different groups (e.g., geographic area, ethnicity), one must often adjust for differences in age distribution to remove the confounding affect of age. When the number of events or rates are very small (as is often the case for local area studies), the normal approximation method of calculating confidence intervals may give a negative number for the lower confidence limit. To avoid this common pitfall, one can approximate exact confidence intervals. This function implements this method (Anderson 1998).

References

Anderson RN, Rosenberg HM. Age Standardization of Death Rates: Implementation of the Year 200 Standard. National Vital Statistics Reports; Vol 47 No. 3. Hyattsville, Maryland: National Center for Health Statistics. 1998, pp. 13-19. Available at http://www.cdc.gov/nchs/data/nvsr/nvsr47/nvs47_03.pdf.

Steve Selvin. Statistical Analysis of Epidemiologic Data (Monographs in Epidemiology and Biostatistics, V. 35), Oxford University Press; 3rd edition (May 1, 2004)

See Also

See also ageadjust.direct

Examples

##From Selvin (2004)
##enter data
dth60 <- scan()
141 926 1253 1080 1869 4891 14956 30888 41725 26501 5928

pop60 <- scan()
1784033 7065148 15658730 10482916 9939972 10563872 9114202
6850263 4702482 1874619 330915

dth40 <- scan()
45 201 320 670 1126 3160 9723 17935 22179 13461 2238

pop40 <- scan()
906897 3794573 10003544 10629526 9465330 8249558 7294330
5022499 2920220 1019504 142532

##calculate age-specific rates
rate60 <- dth60/pop60
rate40 <- dth40/pop40

#create array for display
tab <- array(c(dth60, pop60, round(rate60*100000,1), dth40, pop40,
round(rate40*100000,1)),c(11,3,2))
agelabs <- c("<1", "1-4", "5-14", "15-24", "25-34", "35-44", "45-54",
"55-64", "65-74", "75-84", "85+")
dimnames(tab) <- list(agelabs,c("Deaths", "Population", "Rate"),
c("1960", "1940"))
tab

##implement direct age standardization using 'ageadjust.direct'
dsr <- ageadjust.direct(count = dth40, pop = pop40, stdpop = pop60)
round(100000*dsr, 2) ##rate per 100,000 per year

##implement indirect age standardization using 'ageadjust.indirect'
isr <- ageadjust.indirect(count = dth40, pop = pop40,
                          stdcount = dth60, stdpop = pop60)
round(isr$sir, 2)         ##standarized incidence ratio
round(100000*isr$rate, 1) ##rate per 100,000 per year