svystandardize: Direct standardization within domains

In survey: Analysis of Complex Survey Samples

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)

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.

Value

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

Note

The standard error estimates do not exactly match the NCHS estimates

References

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

See Also

postStratify, svyby

Examples

## 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)		 

survey documentation built on May 3, 2023, 9:12 a.m.