Creating an AI Assistant for SAS Viya in 5 steps (@sassoftware/viya-assistantjs) - Part I
Recent Library Articles
Recently in the SAS Community Library: SAS' @kumardeva debunks the myth that developing AI assistants is too hard. He shows you how to use the @sassoftware/viya-assistantjs library to jump start your development.
A new update is available for SAS Web Infrastructure Data Base JDBC Drivers , version 9.43 : Hot Fix: M5U002 - Published 15MAY2024 , Download link for M5U002 Component name: SAS Web Infrastructure Data Base JDBC Drivers Related SAS release: 9.4 Issues addressed in M5U002 This list of notes might be incomplete. For a complete list of issues addressed by this hot fix, visit the hot fix page for M5U002 Note: A comprehensive list of all SAS hot fixes is available from support.sas.com. You can use the SAS Hot Fix Analysis, Download, and Deployment (SASHFADD) tool to manage your SAS hot fixes.
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A new update is available for Base SAS , version 9.4_M8 : Hot Fix: L8X053 - Published 15MAY2024 , Download link for L8X053 Component name: Base SAS Related SAS release: 9.4 Issues addressed in L8X053 SAS Note 70797 Running SAS ® 9.4M7 (TS1M7) or SAS® 9.4M8 (TS1M8) with non-English European languages might result in an error This list of notes might be incomplete. For a complete list of issues addressed by this hot fix, visit the hot fix page for L8X053 Note: A comprehensive list of all SAS hot fixes is available from support.sas.com. You can use the SAS Hot Fix Analysis, Download, and Deployment (SASHFADD) tool to manage your SAS hot fixes.
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Hello,
I used the codes at below to estimate the propensity score and logistic regression for inverse probability weighting.
How can I test the balance of the standardised mean differences before and after adjustment?
How to obtain the synthetic n values derived from weights?
Thanks
/***CREATING PROPENSITY SCORES********/
proc sort data=tab_imput; by _imputation_;run;
proc logistic data=tab_imput desc;
class var1 var2 var3 var4 var5 var6 var7 var8 var9 ;
model mut= var var1 var2 var3 var4 var5 var6 var7 var8 var9/link=logit rsquare ;
output out=denom p=d;
by _imputation_;
run;
proc logistic data=tab_imput desc;
model mut=;
output out=num p=n;
by _imputation_;
run;
proc sort data=tab_imput ;
by anonymat;run;
proc sort data=denom;
by anonymat;run;
proc sort data=num;
by anonymat;run;
data tab_imput_pscore;
merge tab_imput denom num;
by anonymat;
if mut=1 then uw=1/d; else if mut=0 then uw=1/(1-d);
if mut=1 then sw=n/d; else if mut=0 then sw=(1-n)/(1-d);
run;
proc sort data=tab_imput_pscore; by _imputation_;run;
/***PROPENSITY SCORE WEIGHTED OUTCOME MODEL****/
ods graphics on;
proc logistic data=tab_imput_pscore desc;
class mut(ref='no') / param=reference ;
model vif (event='no') = mut/ rsquare clodds=wald lackfit ;
weight sw ;
by _imputation_;
oddsratio mut;
ods output parameterEstimates = ipw_mut ;
run;
ods graphics off;
proc mianalyze parms=ipw_mut ;
modeleffects mut;
ods output parameterEstimates = ipw_mut1;
run;
data ipw_mut2; set ipw_mut1;
OR_est=EXP(ESTIMATE);
LCI_OR=OR_est*EXP(-1.96*STDERR);
UCI_OR=OR_est*EXP(+1.96*STDERR);
run;
proc print data=ipw_mut2;
var Parm OR_est LCI_OR UCI_OR Probt ;
run;
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