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)

Arguments

design

survey design object

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.

References

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

Examples

Run this code

# NOT RUN {
## 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)		 

# }