PROC GLIMMIX Categorical Mean Difference

mekono — Sun, 07 Mar 2021 04:54:02 GMT

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;

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

Re: PROC GLIMMIX Categorical Mean Difference

SteveDenham — Mon, 08 Mar 2021 13:28:32 GMT

This is the same question you posed here: Mean Difference of 4-Level Categorical Variable

Please see the reply by @StatDave regarding use of the %NLmeans macro.

And I think we could do with some additional information, such as what you mean by a standardized mean difference. Are you referring to something like a protected least significant difference? If that is the case, you could only derive such a thing on the logit scale, as on the original scale the standard errors will be different depending on the value of the mean.

SteveDenham

topic Re: PROC GLIMMIX Categorical Mean Difference in Statistical Procedures

PROC GLIMMIX Categorical Mean Difference

Re: PROC GLIMMIX Categorical Mean Difference