Conditional logistic regression using proc logistic or Proc genmod ?

[THS]

Hello~

I'm trying to estimate the Odds ratio of a treatment after adjusting confounding factors in a 1:2 matched data sets (ex file in the attachement)

Due to the dependence between these data, I'm using conditional logistic regression as statistical method.

PROC Logistic & PROC GENMOD all seems able to deal with matched data well but reported differently.
I'm wondering what is the difference between proc logistic  & proc genmod for dealing with matched data
and which would be better or more accurate ? .

## ex data set:

https://www.dropbox.com/s/k3b4yglsofsr4u4/ex.sas7bdat?dl=0

Treatment option: Tx; Outcome: Outcome;  Confounding factors: B1, C1, B1*Tx;

MATCH_ID	C1	B1	Tx	OUTCOME	STR
0023	78	1	0	0	1
0023	64	1	1	0	2
0023	84	1	1	1	3
0051	76	1	0	0	1
0051	67	1	1	1	2
0051	68	1	1	0	3

## The Code I used in PROC Logistic for conditional logistic regression;

PROC LOGISTIC DATA= EX ;

class Tx(REF='0') B1(REF='0')  /param=ref;

MODEL OUTCOME(EVENT='0')= Tx B1 C1 B1*Tx  / expb  ;

strata match_ID;

run;

## The Code I used in PROC GENMOD for 1:2 matched data ;

PROc GENMOD DATA=ex;

CLASS MATCH_ID Tx(REF='0') B1(REF='0') /param=ref;

MODEL OUTCOME  =Tx B1 C1 B1*Tx  /dist=BIN link=logit  ;

repeated subject=MATCH_ID/type=exch;

run;

Sincerely thanks for your help ~

[StatDave]

The two procedures use entirely different estimation methods.  Conditional logistic regression in PROC LOGISTIC maximizes a conditional likelihood, while PROC GENMOD uses the Generalized Estimating Equations (GEE) method which is not a likelihood-based method. These methods, and others, are compared in the book "Logistic Regression Using SAS: Theory and Application, Second Edition," (Allison, P., SAS Institute, 2012).