Hi,   I'm performing an analysis of 2 treatment groups (group a: ~700; group b: ~1400) and need to calculate the standardized mean difference between the groups. Propensity score adjusted was performed rather than propensity score matching to maintain higher sample size.  The majority of category variables are binary but I have a handful that are 3-4 levels, like patient location; patient race; patient marital status.  I am using proc glimmix to calculate the adjusted SMD of the categorical variables, but am not sure how to translate the outcomes into actual mean differences across the two treatment groups. Any help would be appreciated!   <code>     proc glimmix data=work.clinical_data;       class trx_group region ;       model region (descending)=  trx_group propensity_weight            /  link=cumlogit  dist=multi  solution;       output out=region predicted(blup ilink)=predProbs lcl(blup ilink)=lower ucl(blup ilink)=upper;;       run;     <Output>   Class Level Information Class Levels Values TRX_group 2 0 1 REGION 4 WEST SOUTH NORTHEAST MIDWEST     Number of Observations Read 2156 Number of Observations Used 2156     Response Profile Ordered Value REGION Total Frequency 1 WEST 316 2 SOUTH 820 3 NORTHEAST 541 4 MIDWEST 479 The GLIMMIX procedure is modeling the probabilities of levels of REGION having lower Ordered Values in the Response Profile table.     Dimensions Columns in X 6 Columns in Z 0 Subjects (Blocks in V) 1 Max Obs per Subject 2156     Optimization Information Optimization Technique Newton-Raphson Parameters in Optimization 5 Lower Boundaries 0 Upper Boundaries 0 Fixed Effects Not Profiled     Iteration History Iteration Restarts Evaluations Objective Function Change Max Gradient 0 0 4 2868.0488989 . 48.79174 1 0 3 2840.3830991 27.66579987 4.798868 2 0 3 2840.3402403 0.04285876 0.024003 3 0 3 2840.3402388 0.00000149 8.377E-7     Convergence criterion (GCONV=1E-8) satisfied.     Fit Statistics -2 Log Likelihood 5680.68 AIC  (smaller is better) 5690.68 AICC (smaller is better) 5690.71 BIC  (smaller is better) 5719.06 CAIC (smaller is better) 5724.06 HQIC (smaller is better) 5701.06     Parameter Estimates Effect REGION TRX_group Estimate Standard Error DF t Value Pr > |t| Intercept WEST   -1.4138 0.1248 2151 -11.33 <.0001 Intercept SOUTH   0.4960 0.1203 2151 4.12 <.0001 Intercept NORTHEAST   1.6671 0.1256 2151 13.27 <.0001 TRX_group   0 -0.00609 0.09912 2151 -0.06 0.9510 TRX_group   1 0 . . . . Propensity_score     -1.1431 0.1834 2151 -6.23 <.0001     Type III Tests of Fixed Effects Effect Num DF Den DF F Value Pr > F TRX_group 1 2151 0.00 0.9510 Propensity_score 1 2151 38.87 <.0001       ... View more

Hi!   I'm performing an analysis of 2 treatment groups (group a: ~700; group b: ~1400) and need to calculate the standardized mean difference between the groups. Propensity score adjusted was performed rather than propensity score matching to maintain higher sample size.  The majority of category variables are binary but I have a handful that are 3-4 levels, like patient location; patient race; patient marital status.  I used a macro (http://www.lerner.ccf.org/qhs/software/lib/stddiff.sas ) to get the unadjusted SMD, and am using proc glimmix to calculate the adjusted SMD of the categorical variables. I found another macro but it was more geared to propensity score matching.    What is the best way to interpret the proc glimmix output to come up with a single mean difference value between the two treatment groups?   The code I'm using is below- I was advised to not include treatment group as random effect- and outcome.   <code>     proc glimmix data=work.clinical_data;       class trx_group region ;       model region (descending)=  trx_group propensity_weight            /  link=cumlogit  dist=multi  solution;       output out=region predicted(blup ilink)=predProbs lcl(blup ilink)=lower ucl(blup ilink)=upper;;       run;     <Output>   Class Level Information Class Levels Values TRX_group 2 0 1 REGION 4 WEST SOUTH NORTHEAST MIDWEST     Number of Observations Read 2156 Number of Observations Used 2156     Response Profile Ordered Value REGION Total Frequency 1 WEST 316 2 SOUTH 820 3 NORTHEAST 541 4 MIDWEST 479 The GLIMMIX procedure is modeling the probabilities of levels of REGION having lower Ordered Values in the Response Profile table.     Dimensions Columns in X 6 Columns in Z 0 Subjects (Blocks in V) 1 Max Obs per Subject 2156     Optimization Information Optimization Technique Newton-Raphson Parameters in Optimization 5 Lower Boundaries 0 Upper Boundaries 0 Fixed Effects Not Profiled     Iteration History Iteration Restarts Evaluations Objective Function Change Max Gradient 0 0 4 2868.0488989 . 48.79174 1 0 3 2840.3830991 27.66579987 4.798868 2 0 3 2840.3402403 0.04285876 0.024003 3 0 3 2840.3402388 0.00000149 8.377E-7     Convergence criterion (GCONV=1E-8) satisfied.     Fit Statistics -2 Log Likelihood 5680.68 AIC  (smaller is better) 5690.68 AICC (smaller is better) 5690.71 BIC  (smaller is better) 5719.06 CAIC (smaller is better) 5724.06 HQIC (smaller is better) 5701.06     Parameter Estimates Effect REGION TRX_group Estimate Standard Error DF t Value Pr > |t| Intercept WEST   -1.4138 0.1248 2151 -11.33 <.0001 Intercept SOUTH   0.4960 0.1203 2151 4.12 <.0001 Intercept NORTHEAST   1.6671 0.1256 2151 13.27 <.0001 TRX_group   0 -0.00609 0.09912 2151 -0.06 0.9510 TRX_group   1 0 . . . . Propensity_score     -1.1431 0.1834 2151 -6.23 <.0001     Type III Tests of Fixed Effects Effect Num DF Den DF F Value Pr > F TRX_group 1 2151 0.00 0.9510 Propensity_score 1 2151 38.87 <.0001       ... View more

I'm using Proc Glimmix to get the adjusted means cost for my research question. The output gives the standard error estimate but is there a direct was to get the standard deviation from the model? I know I can calculate it based on the standard error but the deviations are looking larger than expected.   proc glimmix data= final_population; class patient_group survival  gender (ref="0") daibetes (ref="0") smoking (ref="0") obesity  (ref="0") ; model costs=age gender daibetes smoking obesity propensity_score / dist=binary link=logit solution; random intercept / subject=patient_group; lsmeans patient_group / pdiff ilink oddsratio cl ;  run; ... View more

Hi, For a project, I have confirmed the sample size, but am wanting to know what is the average difference that’s needed across the two groups to be considered significantly different. Any idea on where to start with this? I have 618 in each cohort , looking for power at 0.80 and alpha of 0.05 ... View more