Fixed Effects estimate using person level data

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Presentation transcript:

Fixed Effects estimate using person level data

Some person level data data ipdtmp; set dpc.ipd_student(keep=cohort d_chd age bmi chol sbp diab currsmok); run; proc contents data=ipdtmp position;run;

proc sql; select count(distinct cohort) from ipdtmp ; quit;

Logistic Regression We assume we have a sample and that

Create the “effect” variables and their standard errors proc logistic data=ipdtmp descending outest=betas covout noprint; by cohort; model d_chd=age bmi chol sbp diab currsmok; run; proc print data=betas;

Create separate tables with effect sizes and variances proc sql; create table bmibetas as select cohort,bmi as bmibeta from betas where _type_ eq "PARMS" order by cohort ; select * from bmibetas select count(*) create table bmivar as select cohort, bmi as varbeta label="Variance of estimate", 1/varbeta as precbeta label="Precision of estimate" where _type_ eq "COV" and _name_ eq "bmi" from bmivar quit;

Put them together data metabetas; merge bmibetas bmivar; by cohort; run; proc print data=metabetas;run;

Calculate Fixed Effects Estimate PROC SQL; SELECT SUM(bmibeta*(1/varbeta))/SUM((1/varbeta)) AS estimate, 1/SUM(1/varbeta) AS VarEst FROM metabetas; QUIT;

An aside – the data step version data bmibetas1(keep=cohort bmi) bmivar1(keep=cohort bmivar); set betas; if _type_="PARMS" then output bmibetas1; else if _type_ eq "COV" and _name_ eq "bmi" then do; bmivar=bmi; output bmivar1; end; run; proc sort data=bmibetas;by cohort;run; proc sort data=bmivar;by cohort;run; data metabetas1; merge bmibetas1 bmivar1;by cohort;run; proc print data=metabetas1;run;