help for distrate

Directly standardized rates with improved confidence interval

distrate
casevar
popvar
using filename
[if exp]
[in range]
,
standstrata(stratavars)
[by(varlist)
popstand(varname)
list(varlist)
sepby(varlist)
format:(%fmt)
formatn(#)
mult(#)
fay
dobson
refrate:(#)
level(#)
saving(filename[,replace])
prefix(string)
postfix(string) ]

Description

distrate estimates directly standardized rates and confidence intervals based on the
gamma distribution as proposed by Fay and Feuer (1997). Tiwari, Clegg and Zou (2006) modified the formula of the upper
confidence limit showing by simulations that this modified gamma confidence interval performs better
than the gamma interval of Fay and Feuer and the other intervals.
This method produces valid confidence intervals even when the number of cases is very small.
When by(varlist) is specified ratios of directly standardized rates (SRR) are also computed.
Modified F intervals of SRR are estimated as proposed by Tiwari.

Data must be in aggregate form, i.e. each record must contain the total number of deaths (or events)
and population for each stratum as follows

Age strata death pop

0-44 164 47346
45-54 143 83173
55-64 202 186108
65-74 208 322065
75+ 283 362051

using filename specifies a file containing standard population weigths, typically stratified
by age and optionally by other variables. This file must be sorted by the variable specified in standstrata()

Options

standstrata(stratavars) specifies the variables defining strata across which
to average stratum-specific rates. These variables must be present in the study population and in the standard population
file. This is most often a unique variable containing age categories.

by(varlist) produces directly standard rates for each group identified by equal values of the
by() variables taking on integer or string values.

popstand(varname) specifies the variable in the using file that contains
the standard population weights. If not specified distrate assumes that it is named as popvar in the
study population.

list(varlist) specifies the variables to be listed.

sepby(varlist) draws a separator line whenever varlist values change.

format:(%fmt) specifies the format for variables containing the estimates.

formatn(#) specifies the # of digits for the format of the N (population) variable.

mult(#) specifies the units to be used in reported results. For example, if the analysis time is in years,
specifying mult(100000) results in rates per 100,000 person-years.

fay additionally displays Fay and Feuer (1997) upper confidence limit. Note that Fay and Feuer lower confidence limit is
equal to Tiwari, Clegg and Zou lower limit.

dobson additionally displays Dobson, Kuulasmaa, Eberle and Scherer (1991) confidence limits.

refrate(#) specifies the directly standardized rate (DSR) to be used in the denominator when standardized rate ratios
(SRR) are computed. When DSR are required for each level of the variables in by(varlist)
SRR are calculated as ratio of each DSR in the numerator with the DSR listed as first in the denominator.
refrate(#) allows to use a different DSR in the denominator.

level(#) specifies the confidence level, in percent, for the confidence
interval of the adjusted rate; see help level.

saving(filename[,replace]) allows to save the estimates in a file.

prefix(string) or postfix(string) adds a prefix or a
suffix to the variable names when the estimates are saved.

Example

use "C:\Data\SuffolkCounty.dta", clear

collapse (sum) deaths pop,by(cindpov agegr)

distrate deaths pop using year2000st, stand(agegr) by(cindpov) mult(100000)

Further options

distrate deaths pop using year2000st, stand(agegr) by(cindpov) saving(DirectSuffolk,replace)
format(%8.2f) mult(100000) fay dobson level(90) list(rateadj lb_gam ub_gam ub_f lb_d ub_d srr lb_srr ub_srr)

Downloading ancillary files in one of your `"`c(adopath)'"' directory you can run this example.

(click to run)

Saved results

distrate saves the following in r():

Scalars

r(k) number of groups identified by distinct values of the by() variables

Matrices

r(Nobs) 1 x k vector of study population

r(NDeath) 1 x k vector of number of events

r(crude) 1 x k vector of crude rates

r(adj) 1 x k vector of adjusted rates

r(lb_G) 1 x k vector of lower bound of Tiwari CI of adjusted rates

r(ub_G) 1 x k vector of upper bound of Tiwari CI of adjusted rates

r(se_gam) 1 x k vector of standard error of adjusted rates

r(ub_F 1 x k vector of upper bound of Fay CI of adjusted rates

r(lb_D) 1 x k vector of lower bound of Dobson CI of adjusted rates

r(ub_D) 1 x k vector of upper bound of Dobson CI of adjusted rates

r(srr) 1 x k vector of standardized rate ratios

r(lb_srr) 1 x k vector of lower bound of standardized rate ratios

r(ub_srr) 1 x k vector of upper bound of standardized rate ratios

Authors

Enzo Coviello (enzo.coviello@tin.it)

Dario Consonni

Carlotta Buzzoni

Carolina Mensi

References

Dobson AJ, Kuulasmaa K, Ederle E, Scherer J. Confidence intervals for weighted sums of Poisson parameters. Statistics in Medicine 1991;10: 457-462.

Fay MP, Feuer EJ. Confidence intervals for directly standardized rates: a method based on the gamma distribution.
Statistics in Medicine 1997; 16:791-801.

Tiwari RC, Clegg LX, Zou Z. Efficient interval estimation for age-adjusted cancer rates.
Statistical Methods in Medical Research 2006; 15: 547-569.

Consonni D, Coviello E, Buzzoni C, Mensi C. A command to calculate age-standardized rates with efficient interval estimation.
Stata Journal 2012; 12: 688-701.

Public Health Disparities Geocoding Project Monograph.
CASE EXAMPLE: Analysis of all cause mortality rates in Suffolk County, Massachusetts, 1989-1991, by CT poverty strata

Also see

On-line: help for dstdize