popEpi (version 0.4.12)

rate: Direct-Standardised Incidence/Mortality Rates

Description

rate calculates adjusted rates using preloaded weights data or user specified weights.

Usage

rate(
  data,
  obs = NULL,
  pyrs = NULL,
  print = NULL,
  adjust = NULL,
  weights = NULL,
  subset = NULL
)

Value

Returns a data.table with observations, person-years, rates and adjusted rates, if available. Results are stratified by print. Adjusted rates are identified with suffix .adj and .lo and .hi are for confidence intervals lower and upper 95% bounds, respectively. The prefix SE. stands for standard error.

Arguments

data

aggregated data (see e.g. lexpand, aggre if you have subject-level data)

obs

observations variable name in data. Flexible input, typically e.g. obs = obs.

pyrs

person-years variable name in data. Flexible input, typically e.g. pyrs = pyrs.

print

variable name to stratify the rates. Flexible input, typically e.g. print = sex or print = list(sex, area).

adjust

variable for adjusting the rates. Flexible input, typically e.g. adjust = agegroup.

weights

typically a list of weights or a character string specifying an age group standardization scheme; see the dedicated help page and examples.

subset

a logical expression to subset data.

Author

Matti Rantanen, Joonas Miettinen

Details

Input data needs to be in aggregated format with observations and person-years. For individual data use lexpand, or ltable and merge person-years manually.

The confidence intervals are based on the normal approximation of the logarithm of the rate. The variance of the log rate that is used to derive the confidence intervals is derived using the delta method.

See Also

lexpand, ltable

Other main functions: Surv(), relpois(), relpois_ag(), sir(), sirspline(), survmean(), survtab(), survtab_ag()

Other rate functions: rate_ratio()

Examples

## Prepare data with lexpand and then reformat agegroup.
data(sibr)
x <- lexpand(sibr, birth = bi_date, entry = dg_date, exit = ex_date,  
             breaks = list(per = c(1990,2000,2010,2020), age = c(0:17*5,Inf)),
             aggre = list(agegroup = age, year.cat = per),
             status =  status != 0)

x$agegroup <- cut(x$agegroup,  c(0:17*5,Inf), right = FALSE)

## calculate rates for selected periods with Nordic 2000 weights:
r1 <- rate( data = x, obs = from0to1, pyrs = pyrs, print = year.cat, 
            adjust = agegroup, weights = 'nordic')
r1

## use total person-years by stratum as weights (some have zero)
w <- ltable(x, by.vars = "agegroup", expr = sum(pyrs))
w[is.na(w$V1),]$V1 <- 0

r2 <- rate( data = x, obs = from0to1, pyrs = pyrs, print = year.cat, 
            adjust = agegroup,
            weights = w$V1)
r2

## use data.frame of weights:
names(w) <- c("agegroup", "weights")
r2 <- rate( data = x, obs = from0to1, pyrs = pyrs, print = year.cat, 
            adjust = agegroup,
            weights = w)
r2

## internal weights (same result as above)
r3 <- rate( data = x, obs = from0to1, pyrs = pyrs, print = year.cat, 
            adjust = agegroup,
            weights = "internal")
r3