svystandardize: Direct standardization within domains

Description

In health surveys it is often of interest to standardize domains to have the same distribution of, eg, age as in a target population. The operation is similar to post-stratification, except that the totals for the domains are fixed at the current estimates, not at known population values. This function matches the estimates produced by the (US) National Center for Health Statistics.

Usage

svystandardize(design, by, over, population, excluding.missing = NULL)

Value

A new survey design object of the same type as the input.

Arguments

design: survey design object
by: A one-sided formula specifying the variables whose distribution will be standardised
over: A one-sided formula specifying the domains within which the standardisation will occur, or ~1 to use the whole population.
population: Desired population totals or proportions for the levels of combinations of variables in by
excluding.missing: Optionally, a one-sided formula specifying variables whose missing values should be dropped before calculating the domain totals.

References

National Center for Health Statistics https://www.cdc.gov/nchs/tutorials/NHANES/NHANESAnalyses/agestandardization/age_standardization_intro.htm

Examples

Run this code

## matches http://www.cdc.gov/nchs/data/databriefs/db92_fig1.png
data(nhanes)
popage <- c( 55901 , 77670 , 72816 , 45364 )
design<-svydesign(id=~SDMVPSU, strata=~SDMVSTRA, weights=~WTMEC2YR, data=nhanes, nest=TRUE)
stdes<-svystandardize(design, by=~agecat, over=~race+RIAGENDR, 
   population=popage, excluding.missing=~HI_CHOL)
svyby(~HI_CHOL, ~race+RIAGENDR, svymean, design=subset(stdes,
agecat!="(0,19]"))


data(nhanes)
nhanes_design <- svydesign(ids = ~ SDMVPSU, strata = ~ SDMVSTRA, 
                        weights = ~ WTMEC2YR, nest = TRUE, data = nhanes)

## These are the same
nhanes_adj <- svystandardize(update(nhanes_design, all_adults = "1"),
                 by = ~ agecat, over = ~ all_adults,
                 population = c(55901, 77670, 72816, 45364), 
                 excluding.missing = ~ HI_CHOL)
svymean(~I(HI_CHOL == 1), nhanes_adj, na.rm = TRUE)		 

nhanes_adj <- svystandardize(nhanes_design,
                 by = ~ agecat, over = ~ 1,
                 population = c(55901, 77670, 72816, 45364), 
                 excluding.missing = ~ HI_CHOL)
svymean(~I(HI_CHOL == 1), nhanes_adj, na.rm = TRUE)