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I read on this forum that IF and WHERE statements can be used in Data step, but in proc steps only IF can be used. I've tried it and in proc means, for example, if can't be used but where can. Is this true for all proc steps? Thanks!

_maldini_

I am running a multivariable linear regression and want to compare adjusted means of the dependent variable (mean_sbp) at given values of the independent variable (joint_years). Both variables are continuous. I am using proc glm for the regression. I want to be able to easily compare the adjusted mean_sbp at 5 joint_years to 15 joint_years. Or, compare the mean_sbp of the median of joint_years to the 99th percentile of joint_years. Googling and ChatGPT suggest using the CONTRAST v. ESTIMATE statements, but I can't seem to figure it out. I realize I could create categorical versions of joint_years and use it as a categorical variable, but I'd like something a little more flexible. Here are some approaches I have tried: proc mixed data=work.finallabel_6; class race1c gender1 income6 educ1 htn_meds lipid_meds dm_meds; model mean_sbp = joint_years race1c gender1 age6c income6 educ1 drinks_wk pkyrs6c pamvcm6c bmi6c tchol_hdl_ratio hba1c6 glucose6 htn_meds lipid_meds dm_meds totmed6; estimate 'Mean at joint_years=5' joint_years 1 / atmean joint_years=5; estimate 'Mean at joint_years=15' joint_years 1 / atmean joint_years=15; format race1c race1c. gender1 gender1f. income6 income6fmt. educ1 educ1fmt. htn_meds lipid_meds dm_meds yes_no_fmt.; run; proc glm data=mesa.finallabel_6; class race1c gender1 income6 educ1 htn_meds lipid_meds dm_meds; model mean_sbp = joint_years race1c gender1 age6c income6 educ1 drinks_wk pkyrs6c pamvcm6c bmi6c tchol_hdl_ratio hba1c6 glucose6 htn_meds lipid_meds dm_meds totmed6 / solution clparm; /* estimate 'Difference at joint_years=0 vs joint_years=23' joint_years 1 -1; */ /* Use the estimate statement to compare the adjusted mean SBP at any 2 values of joint_years; */ /* this essentially multiplies the beta by 10 */ /* estimate joint_years 10; */ /* contrast 'Effect of increasing joint_years from 5 to 15' joint_years 10; */ lsmeans joint_years / at = (joint_years = 5) (joint_years = 15) adjust=e; format race1c race1c. gender1 gender1f. income6 income6fmt. educ1 educ1fmt. htn_meds lipid_meds dm_meds yes_no_fmt. ; run; Thanks for your help.

dvandeventer

I have a macro for performing HLM and logistic regression and then outputting a results table with the odds ratios and 95% confidence intervals from both models. When I apply the macro and try to run it, it is not able to generate the upper 95% CI for the logistic regression (OR_lr95UCI). If I remove this line from the code, it generates a table with all other results but when I include this variable, the code does not run. I am calculating it the same way as the lower limit (OR_lr95LCI), so I don't know what is causing the issue. I have copied the macro below, with the problem variable bolded. %MACRO HLM2(data=, outcome=, covariates=, data2=, covariates2=, b2index=); /* Run PROC LOGISTIC, result is in the data set logistic_output */ proc logistic data=&DATA desc COVOUT OUTEST=logistic_output; model &OUTCOME = &COVARIATES ; run; proc iml; /**************************************************************** *** Hirearchical model using empirical-Bayes or semi-Bayes estimation. *** ****************************************************************/ start hm_reg(b,v,z,t2,max_iter,c_criter,b_hm,bcov_hm,b2); /**************************************************************** ** Input: b : 1st-stage parameter estimates ** v : 1st-stage covariance matrix estimates for b ** z : 2nd-stage design matrix (prior design matrix) ** t2 : prior variance for semi-Bayes estimation (vector), ** (0 if prior variance is to be estimated using ** emirical-Bayes estimation.) ** max_iter: maximum number of iteration (default is 20) ** c_criter: convergence criteria (default is 1e-3) ** Output: b_hm : posterier estimates ** bcov_hm : covariance matrix for b ** b2 : 2nd-stage parameter estimates ****************************************************************/ /*** Number of first-stage parameters.***/ np = nrow(Z); ncolz = ncol(Z); /*** Second-stage degrees of freedom. */ df2 = np-ncolz; Inp = I(np); /*** Empirical-Bayes estimation, else semi-Bayes estimation. ***/ if (min(t2) <= 0) then t2pass = 0; else t2pass = 1; /*** Maximum number of iterations, and convergence criterion for t2. ***/ if max_iter = 0 then _maxiter = 20; else _maxiter = max_iter; if c_criter = 0 then _criter = 1e-3; else _criter = c_criter; if t2pass = 0 then do; print np[format=4.0] " first stage parameters were regressed on" ncolz[format=4.0] " second stage covariate(s)", " using empirical-Bayes estimation method (prior variance (t2) to be estimated)."; end; else do; print np[format=4.0] " first stage parameters were regressed on" ncolz[format=4.0] " second stage covariate(s)", " using semi-Bayes estimation with prior variance (t2) fixed at" t2[format=8.4]; end; /*** Undertake second-stage regression: ***/ t2old = t2+1; do iter = 1 to _maxiter while(abs(t2-t2old) > _criter); t2old = t2; /*** Weight matrix. ***/ w = inv(v+t2#Inp); wv = w*v; wz = w*z; ws = sum(w); /*** Invert 2nd-stage information. ***/ vs = inv(t(Z)*w*Z); /*** 2nd-stage coefficient estimates. ***/ b2 = vs*(t(wz)*b); if t2pass=0 then do; /*** Residual from 2nd-stage estimates. ***/ e = b - (Z*b2); /*** Total residual sums of squares. ***/ rsst = t(e)*w*e; /*** Residual mean square. ***/ rms = np*rsst/(df2*ws); /*** Update t2 for empirical Bayes. ***/ t2 = max(rms-sum(wv)/ws,0); end; end; /*** Calculate posterior expectations of 1st-stage parameters: ***/ /*** Weight for prior expectations of the 1st-stage parameters (curvature correction for EB.) ***/ if t2pass=0 then wvc = ((df2-2)/df2)*wv; else wvc = wv; /*** Projection of b to prior mean. ***/ hatw = Z*vs*t(wz); st2 = sqrt(t2*t(t2)); /*** Projection of b to posterior mean. ***/ hatp = wvc*hatw + w#st2 + (wv-wvc); /*** Estimated posterior mean. ***/ b_hm = hatp*b; /*** Estimated posterior covariance. ***/ bcov_hm = v - wvc*(Inp-hatw)*v; /*** Approx df in hierarchical model. ***/ npa = trace(hatp); if t2pass=0 then do; /*** Add variance component due to estimation of the prior variance: ***/ wvce = wvc*(e#(sqrt((sum(wv)/ws + t2)/(vecdiag(v)+t2)))); bcov_hm = bcov_hm + (2/(df2-2))*wvce*t(wvce); /*** Check for fitting problems: ***/ if iter >= _maxiter then print " Empirical-Bayes did not converge."; else do; if t2=0 then print " Empirical-Bayes converged after" iter[format=4.0] " iterations," " but potential underdispersion: t2 set to zero."; else print " Empirical-Bayes converged after" iter[format=4.0] " iterations," " with prior variance (t2) estimate:" t2[format=8.4]; end; end; do i=1 to np; if bcov_hm[i,i]<0 then do; print "Negative second-stage variance: set to zero."; bcov_hm[i] = 0; end; end; finish; /****************************************************************/ /**************************************************************** *** Main procedure for the estimation. Check model setting for each run *** ****************************************************************/ /************************* **** Ordinary Logistic Regression Model *** *************************/ bnames = {INTERCEPT &COVARIATES}`; /* Make use of logistic_output */ use logistic_output; read all into lr_est; b_lr = lr_est[1,1:(ncol(lr_est)-1)]`; bcov_lr = lr_est[2:nrow(lr_est),1:(ncol(lr_est)-1)]; /********************************* *** Output logistic result *** *********************************/ SE_lr = sqrt(vecdiag(bcov_lr)); ward = (b_lr # b_lr) / (se_lr # se_lr); P_value = 1 - probchi(ward,1); print "Ordinary logistic regression estimates:"; print bnames b_lr[format=10.4] se_lr[format=10.4] p_value[format=8.4]; /******************* *** HLM Model *** *******************/ /*** Initialize 1st-stage parameters to be modelled in the hierarchical model ***/ b_lr0 = b_lr[&b2index]; v_lr0 = bcov_lr[&b2index,&b2index]; bnames_hm = bnames[&b2index]; /*** Specify 2nd-stage data and parameters to be modelled in the hierarchical model ***/ use &DATA2 var {&COVARIATES2}; read all into Z_mat; bnames2 = {&COVARIATES2}`; print bnames2 Z_mat; Z_mat = repeat(1,nrow(b_lr0),1); bnames2 = {INTERCEPT}; t2 = 0.5; run hm_reg(b_lr0, v_lr0, Z_mat, t2, 0, 0, b_hm, bcov_hm, b_hm2); /**************************** *** Output HLM result *** ****************************/ SE_hm = sqrt(vecdiag(bcov_hm)); ward = (b_hm # b_hm) / (SE_hm # SE_hm); P_value = 1 - probchi(ward,1); print "Hierarchical logistic regression estimates:"; print bnames_hm b_hm[format=10.4] SE_hm[format=10.4] P_value[format=8.4]; /*** OR estimates from both ordinary logistic model and hierarchical model ***/ OR_lr = exp(b_lr[&b2index]); OR_lr95LCI = exp(b_lr[&b2index] - 1.96*SE_lr[&b2index]); OR_lr95UCI = exp(b_lr[&b2index] + 1.96*SE_lr[&b2index]); OR_hm = exp(b_hm); OR_hm95LCI = exp(b_hm - 1.96*SE_hm); OR_hm95UCI = exp(b_hm + 1.96*SE_hm); /*** Print results: ***/ print "Second stage parameter estimates using hierarchical model.", bnames2 b_hm2[format=10.4]; print " Odds Ratios and 95% CIs", " Conventional Model Hierarchical Model", bnames_hm OR_lr[format=8.2] OR_lr95LCI[format=8.2] OR_lr95UCI[format=8.2] OR_hm[format=8.2] OR_hm95LCI[format=8.2] OR_hm95UCI[format=8.2]; quit; run; %MEND;

HeidiDT

Hi All! I am running a very simple PROC HTTP to download an Excel spreadsheet from Google docs, and then a subsequent import step to load the data into SAS. I get a "401 Unauthorized" note in my log for the PROC HTTP, and the imported file contains the following: Allow Google Sheets access to your necessary cookies You won't be able to access this content if necessary cookies are turned off Learn moreAllow cookies Sign in to your Google Account You must sign in to access this content Sign in Can't access your Google Account We can't sign you in when the necessary cookies are turned off Learn more So I am obviously able to reach the site, but then need to sign in. I have searched and searched on the web for information on how to handle this, but have just been going down rabbit holes. Here is my code: filename _httpin temp; proc http url="https://docs.google.com/spreadsheets/d/1sFshkC35_cY2qwinv1hNHoFjC7u965yhiHV9ga8t9lk/edit?pli=1#gid=503427334" method="get" out=_httpin WEBUSERNAME="my email" WEBPASSWORD='my google password'; debug level=3; run; proc import file=_httpin out=test dbms=csv replace; getnames=no; datarow=1; run; I am able to access the file via my browser, and when I submit the GET method in Postman I get the same output. Please can someone who has successfully downloaded Google docs files provide some assistance? Thanks!

DevaCh

Hi All, Could any one guide me detailed steps that How to Upgrade SAS Version from M7 to M8 in Grid Environment contains 1 Meta Data Server and 2 Compute Grid Nodes & 1 Middletier server. And post upgrade what are the configuration files need to validate ??? Deva.