Hello, I would appreciate if someone could help me with the SAS code for storing final results at a file and retrieving the information directly from the stored dataset and using it without going through all the several steps. Please find below this dataset and the SAS procedures used to arrive at the final dataset t2b. Eg. if I complete work today and write final results to dataset t2b. Is there a code to directly obtain the stored information and use to for for say logistic regression without going though all the many steps? /*DATASET 1*/ data idnew1; input id$ job idchem; datalines; os1 1 990005 os1 1 990021 os1 1 211700 os1 2 211700 os1 2 990021 os1 2 210701 os1 2 990005 os2 1 210701 os2 1 990005 os2 2 990021 os2 3 210701 os2 3 990005 os3 3 210701 os3 1 211700 os4 1 210701 os4 1 990005 os4 1 211700 ; proc format; value idchem 990005 = "cla_exp" 990021 = "bio_exp" 210701 = "amo_exp" 211700 = "chl_exp"; run; data temp; set idnew1; dum = 1; format idchem idchem.; id_job=catx('_', id, job); put _all_; run; proc sort data=temp; by id job id_job; run; proc transpose data=temp out=idnew2(drop=_name_); by id job id_job; id idchem; var dum; run; proc print data=idnew2; Title "Table 1: Merged exposure files for cla, bio, amo and chl pollutants"; run; /*Replacing missing values(.)with zeros(0) ie. unexposed*/ data t; set idnew2; proc stdize out=t2 reponly missing=0; drop _type_; run; proc sort; by id job; Title " Table 2: Merged exposure files for cla, bio, amo and chl pollutants: missing values replaced with zeros"; proc print data=t2; run; /*FINDING FREQUENCIES OF ASSOCIATIONS*/ proc freq data=t2; tables cla_exp*bio_exp cla_exp*amo_exp cla_exp*chl_exp bio_exp*amo_exp bio_exp*chl_exp bio_exp*chl_exp; Title "Frequencies of associations between cla, bio, amo and chl pollutants"; run; /* COUNTING EXPOSED IDS PER AGENT! GOOD! KEEP IT!*/ /* CHECKING NUMBER OF UNIQUE IDS*/ Proc summary data= t2 nway ; class id; var cla_exp bio_exp amo_exp chl_exp; output out=t2b(drop=_type_ _freq_) max=; run; Title "Table of unique IDs for cla bio amo and chl"; proc print; proc freq data=t2b; tables cla_exp bio_exp amo_exp chl_exp; Title "Unique ids frequecies for cla, bio, amo and chl"; run; proc freq data=t2b; tables cla_exp*bio_exp cla_exp*amo_exp cla_exp*chl_exp bio_exp*amo_exp bio_exp*chl_exp bio_exp*chl_exp; Title "Frequencies of UNIQUE IDS exposure associations between cla, bio, amo and chl agents"; run; Next time,I wish to start work directly form dataset t2b and not to run entire work again ie. starting from dataset idnew1. Please find attached SAS log. Thank you in advance. ak. ... View more

Can somebody please help me with the right SAS code to model lung(Ca case) vs. cla_exp? I was expecting pop cont to be zero(0) in the response profile in the output. Also the class level information(0(non-substantial) and 1(substantial) for the output did not appear; this is an error . I wish to model lung cancer case (“Ca case”)-dependent variable with cla_exp( independent variable), where 0=unexposed and 1=exposed to the pollutant.  I want to find the association between lung cancer (Ca case) and the pollutant cla _exp at substantial and Non-substantial levels using their estimates and Odds ratio(OR). My class variable is ‘level’. My ultimate  aim is to find the estimates(OR) for the  number of Ca case and pop cont for “Unexposed vs. Exposed” , Substantial vs. Non-Substantial. Please find below code and log. Output attached. Thanks. ak.     /*Pollutants*/ data d1; input id$ 1-5 job 7 id_job$ 9-15 hcl_exp 17 amo_exp 19 bio_exp 21 cla_exp 23; datalines; OSa03 4 OSa03_4 1 0 0 0 OSa06 3 OSa06_3 0 1 0 0 OSa13 1 OSa13_1 0 1 1 0 OSa13 3 OSa13_3 0 1 1 1 OSa29 2 OSa29_2 0 0 0 1 OSa29 4 OSa29_4 0 1 1 0 OSa30 4 OSa30_4 0 0 1 0 OSa30 1 OSa30_1 1 0 0 0 OSa30 2 OSa30_2 0 1 1 1 OSa54 3 OSa54_3 0 1 0 0 OSa64 3 OSa64_3 0 1 0 0 OSa73 3 OSa73_3 0 0 0 1 OSa74 3 OSa74_3 1 0 0 0 OSa78 3 OSa78_3 0 1 0 0 ; proc sort data=d1; by id; run; /* Cancer subjects*/ data d2; input id$ 1-5 lung$ 7-15; datalines; OSa01 Pop cont OSa06 Ca cont OSa11 Pop cont OSa13 Ca case OSa29 Ca cont OSa30 Ca case OSa31 Ca cont OSa54 Pop cont OSa73 Pop cont ; proc sort data=d2; by id; run; /* Exposure Duration*/ data d4; input id$ 1-5 idchem 7-12 status$ 14-15 duration 16-18; datalines; OSa03 211701 S 6 OSa06 210701 S 9 OSa13 210701 S 37 OSa13 990005 S 5 OSa13 990021 S 37 OSa29 210701 NS 12 OSa29 990005 S 2 OSa30 210701 S 8 OSa30 211701 NS 8 OSa30 990005 S 8 OSa30 990021 S 15 OSa54 210701 NS 14 OSa64 210701 S 15 OSa74 211701 NS 21 OSa78 210701 NS 20 OSa78 990005 S 20 OSa78 990021 S 20 OSa86 990005 S 14 OSa93 210701 S 4 OSa93 990005 S 13 ; proc sort data=d4; by id; run; Proc format; value lung 0='Pop cont' 1='Ca case' 2='Ca cont'; value cla_exp 0='Unexposed' 1='Exposed'; value status 0='NS' 1='S'; run; /* Merging d1 & d2*/ data mg124; merge d1(in=a) d2(in=b) d4(in=c); by id; if a and b and c; if status eq "NS" then level="0"; if status eq "S" then level="1"; run; proc print data=mg124; title ' Table 1: Merged datasets d1, d2 and d4'; run; proc logistic data=mg124; class level(param=ref ref='0'); model lung(event='1') = cla_exp; run;   1 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK; 72 73 /*Pollutants*/ 74 data d1; 75 input id$ 1-5 job 7 id_job$ 9-15 hcl_exp 17 amo_exp 19 bio_exp 21 cla_exp 23; 76 datalines;   NOTE: The data set WORK.D1 has 14 observations and 7 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds     91 ; 92 proc sort data=d1; by id; run;   NOTE: There were 14 observations read from the data set WORK.D1. NOTE: The data set WORK.D1 has 14 observations and 7 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.01 seconds cpu time 0.01 seconds     93 94 95 /* Cancer subjects*/ 96 data d2; 97 input id$ 1-5 lung$ 7-15; 98 datalines;   NOTE: The data set WORK.D2 has 9 observations and 2 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds     108 ; 109 proc sort data=d2; by id; run;   NOTE: There were 9 observations read from the data set WORK.D2. NOTE: The data set WORK.D2 has 9 observations and 2 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.01 seconds cpu time 0.00 seconds     110 111 112 /* Exposure Duration*/ 113 data d4; 114 input id$ 1-5 idchem 7-12 status$ 14-15 duration 16-18; 115 datalines;   NOTE: The data set WORK.D4 has 20 observations and 4 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds     136 ; 137 138 proc sort data=d4; by id; run;   NOTE: There were 20 observations read from the data set WORK.D4. NOTE: The data set WORK.D4 has 20 observations and 4 variables. NOTE: PROCEDURE SORT used (Total process time): real time 0.00 seconds cpu time 0.02 seconds     139 140 Proc format; 141 value lung 0='Pop cont' 1='Ca case' 2='Ca cont'; NOTE: Format LUNG is already on the library WORK.FORMATS. NOTE: Format LUNG has been output. 142 value cla_exp 0='Unexposed' 1='Exposed'; NOTE: Format CLA_EXP is already on the library WORK.FORMATS. NOTE: Format CLA_EXP has been output. 143 value status 0='NS' 1='S'; NOTE: Format STATUS is already on the library WORK.FORMATS. NOTE: Format STATUS has been output. 144 run;   NOTE: PROCEDURE FORMAT used (Total process time): real time 0.00 seconds cpu time 0.01 seconds     145 146 147 /* Merging d1 & d2*/ 148 data mg124; 149 merge d1(in=a) d2(in=b) d4(in=c); by id; 150 if a and b and c; 151 if status eq "NS" then level="0"; 152 if status eq "S" then level="1"; 153 run;   NOTE: MERGE statement has more than one data set with repeats of BY values. NOTE: There were 14 observations read from the data set WORK.D1. NOTE: There were 9 observations read from the data set WORK.D2. NOTE: There were 20 observations read from the data set WORK.D4. NOTE: The data set WORK.MG124 has 11 observations and 12 variables. NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.02 seconds     154 155 proc print data=mg124; 156 title ' Table 1: Merged datasets d1, d2 and d4'; 157 run;   NOTE: There were 11 observations read from the data set WORK.MG124. NOTE: PROCEDURE PRINT used (Total process time): real time 0.26 seconds cpu time 0.26 seconds     158 159 160 proc logistic data=mg124; 161 class level(param=ref ref='0'); 162 model lung(event='1') = cla_exp; 163 run;   NOTE: Option EVENT= is ignored since LINK=CLOGIT. NOTE: PROC LOGISTIC is fitting the cumulative logit model. The probabilities modeled are summed over the responses having the lower Ordered Values in the Response Profile table. Use the response variable option DESCENDING if you want to reverse the assignment of Ordered Values to the response levels. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: There were 11 observations read from the data set WORK.MG124. NOTE: PROCEDURE LOGISTIC used (Total process time): real time 0.50 seconds cpu time 0.46 seconds     164 165 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK; 177     ... View more