Examples of Using Stata v11.0 with JRR replicate weights Provided in the NHANES data set

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1 Examples of Using Stata v110 with JRR replicate weights Provided in the NHANES data set This document is designed to illustrate comparisons of methods to use JRR replicate weights sometimes provided by NCHS and other distributors of public release data sets The NHANES provided JRR replicate weights along with masked strata and PSU variables Demonstrated is how to use the JRR replicate weights directly in Stata along with a comparison of results using both the Taylor Series linearization approach and use of the JRR option in Stata where the replicates are developed by the Stata program Stata Do File: ******************************************************************************************************************** * Use of JRR replicate weights with NHANES dataset * Comparison to Linearization and JRR with "Masked Weights" ******************************************************************************************************************** * read in dataset with demo variables and blood pressure readings use "F:\brahms\summerclasses\complexanalysis2007\demo_bpx_nhanes9900dta", clear * do surveyset with 52 replicate weights svyset _n [pweight=wtint2yr], jkrweight(wtmrep01 wtmrep02 wtmrep03 wtmrep04 wtmrep05 wtmrep06 wtmrep07 wtmrep08 wtmrep09 /// wtmrep10 wtmrep11 wtmrep12 wtmrep13 wtmrep14 wtmrep15 wtmrep16 wtmrep17 wtmrep18 wtmrep19 /// wtmrep20 wtmrep21 wtmrep22 wtmrep23 wtmrep24 wtmrep25 wtmrep26 wtmrep27 wtmrep28 wtmrep29 /// wtmrep30 wtmrep31 wtmrep32 wtmrep33 wtmrep34 wtmrep35 wtmrep36 wtmrep37 wtmrep38 wtmrep39 /// wtmrep40 wtmrep41 wtmrep42 wtmrep43 wtmrep44 wtmrep45 wtmrep46 wtmrep47 wtmrep48 wtmrep49 /// wtmrep50 wtmrep51 wtmrep52) vce(linearized) svydes * run analyses using the JRR replicate weights provided by project staff svy, vce(jackknife): mean bpxdi1 * note that riagendr =1 = male and =2= female svy, vce(jackknife): tabulate riagendr, cell se svy, vce(jackknife): regress bpxdi1 iriagendr * reset svy using masked stratum and psu variables svyset sdmvpsu [pweight=wtint2yr], strata(sdmvstra) vce(linearized) svydes * use of linearized approach svy, vce(linearized): mean bpxdi1 svy, vce(linearized): regress bpxdi1 iriagendr * use of JRR approach with strata and psu variables and Stata creates the JRR replicates for us svy, vce(jackknife): mean bpxdi1 svy, vce(jackknife): regress bpxdi1 iriagendr

2 Output : * read in dataset with demo variables and blood pressure readings use "F:\brahms\summerclasses\complexanalysis2007\demo_bpx_nhanes9900dta", clear * do surveyset with 52 replicate weights svyset _n [pweight=wtint2yr], jkrweight(wtmrep01 wtmrep02 wtmrep03 wtmrep04 wtmrep05 wtmrep06 wtmrep07 wtmrep08 wtmrep09 /// > wtmrep10 wtmrep11 wtmrep12 wtmrep13 wtmrep14 wtmrep15 wtmrep16 wtmrep17 wtmrep18 wtmrep19 /// > wtmrep20 wtmrep21 wtmrep22 wtmrep23 wtmrep24 wtmrep25 wtmrep26 wtmrep27 wtmrep28 wtmrep29 /// > wtmrep30 wtmrep31 wtmrep32 wtmrep33 wtmrep34 wtmrep35 wtmrep36 wtmrep37 wtmrep38 wtmrep39 /// > wtmrep40 wtmrep41 wtmrep42 wtmrep43 wtmrep44 wtmrep45 wtmrep46 wtmrep47 wtmrep48 wtmrep49 /// > wtmrep50 wtmrep51 wtmrep52) vce(linearized) jkrweight: wtmrep01 wtmrep02 wtmrep03 wtmrep04 wtmrep05 wtmrep06 wtmrep07 wtmrep08 wtmrep09 wtmrep10 wtmrep11 wtmrep12 wtmrep13 wtmrep14 wtmrep15 wtmrep16 wtmrep17 wtmrep18 wtmrep19 wtmrep20 wtmrep21 wtmrep22 wtmrep23 wtmrep24 wtmrep25 wtmrep26 wtmrep27 wtmrep28 wtmrep29 wtmrep30 wtmrep31 wtmrep32 wtmrep33 wtmrep34 wtmrep35 wtmrep36 wtmrep37 wtmrep38 wtmrep39 wtmrep40 wtmrep41 wtmrep42 wtmrep43 wtmrep44 wtmrep45 wtmrep46 wtmrep47 wtmrep48 wtmrep49 wtmrep50 wtmrep51 wtmrep52 Strata 1: <one> SU 1: <observations> svydes Survey: Describing stage 1 sampling units jkrweight: wtmrep01 wtmrep02 wtmrep03 wtmrep04 wtmrep05 wtmrep06 wtmrep07 wtmrep08 wtmrep09 wtmrep10 wtmrep11 wtmrep12 wtmrep13 wtmrep14 wtmrep15 wtmrep16 wtmrep17 wtmrep18 wtmrep19 wtmrep20 wtmrep21 wtmrep22 wtmrep23 wtmrep24 wtmrep25 wtmrep26 wtmrep27 wtmrep28 wtmrep29 wtmrep30 wtmrep31 wtmrep32 wtmrep33 wtmrep34 wtmrep35 wtmrep36 wtmrep37 wtmrep38 wtmrep39 wtmrep40 wtmrep41 wtmrep42 wtmrep43 wtmrep44 wtmrep45 wtmrep46 wtmrep47 wtmrep48 wtmrep49 wtmrep50 wtmrep51 wtmrep52 Strata 1: <one> SU 1: <observations> #Obs per Unit Stratum #Units #Obs min mean max

3 * run analyses using the JRR replicate weights provided by project staff svy, vce(jackknife): mean bpxdi1 Jackknife replications (52) 50 Number of strata = 1 Number of obs = 6457 Population size = Mean Std Err [95% Conf Interval] bpxdi * note that riagendr =1 = male and =2= female svy, vce(jackknife): tabulate riagendr, cell se (running tabulate on estimation sample) Number of strata = 1 Number of obs = 9282 Population size = gender - adjudicat ed proportions se Total Key: proportions = cell proportions se = jackknife standard errors of cell proportions svy, vce(jackknife): regress bpxdi1 iriagendr Jackknife replications (52) 50 Number of strata = 1 Number of obs = 6457 Population size = F( 1, 51) = 7951 Prob > F = R-squared = bpxdi1 Coef Std Err t P> t [95% Conf Interval] riagendr _cons

4 * reset svy using masked stratum and psu variables svyset sdmvpsu [pweight=wtint2yr], strata(sdmvstra) vce(linearized) Strata 1: sdmvstra SU 1: sdmvpsu svydes Survey: Describing stage 1 sampling units Strata 1: sdmvstra SU 1: sdmvpsu #Obs per Unit Stratum #Units #Obs min mean max * use of linearized approach svy, vce(linearized): mean bpxdi1 Linearized Mean Std Err [95% Conf Interval] bpxdi

5 svy, vce(linearized): regress bpxdi1 iriagendr F( 1, 14) = 4162 Prob > F = R-squared = Linearized bpxdi1 Coef Std Err t P> t [95% Conf Interval] riagendr _cons * use of JRR approach with strata and psu variables and Stata creates the JRR replicates for us svy, vce(jackknife): mean bpxdi1 Jackknife replications (27) Replications = 27 Mean Std Err [95% Conf Interval] bpxdi svy, vce(jackknife): regress bpxdi1 iriagendr Jackknife replications (27) Replications = 27 F( 1, 14) = 4155 Prob > F = R-squared = bpxdi1 Coef Std Err t P> t [95% Conf Interval] riagendr _cons