I’m doing a 3-to-1 propensity score matching to compare general (control) and non-general (treatment) anesthesia groups in lumbar spine surgery. I've matched on a number of factors, and I've calculated SMD values for continuous variables and standardized differences for categorical variables. T tests could not be used due to the violation of the assumption of independence between the compared groups. The c statistic is .679, which is within the acceptable range for PS matching. I was given a couple of suggestions to improve the impact since the database is not very granular and PS matching can't adjust unmeasured bias. Also, the post-match results demonstrated that non-GA patients were fully matched -- none was unmatched. That rarely occurs even with PS matching. This is due to highly disproportionate numbers. In this case the generalizability could be an issue because the matched GA patients might have been very special / rare cases but remained in the final analysis simply because there were a lot of GA patients. Therefore, I have been asked to add the results of a sensitivity analysis (I was referred to https://academic.oup.com/aje/article/174/3/345/247053) and to also add the results of a multivariate analysis using PS as a confounding factor (such as Outcome ~ GA/non-GA + PS) so that we can show the effect of the non-GA/GA variable as a coefficient or odds ratio. After my initial matching I put together the following:

I have a few questions:

1. Is each of these steps correct?

2. Is the generalized random block design with fixed block effects the correct choice for the analysis of the continuous outcome variable?

3. In each of the 4 steps above, what are the appropriate statistical results to report?

Code:

```
* Perform sensitivity analysis;
ods graphics off;
proc logistic data=data09_19 (keep = anest_grpn gender outpatient ASA_1-ASA_5 ASA_NA CPT22102 CPT62380 CPT63005 CPT63011 CPT63012 CPT63017 CPT63030 CPT63047 CPT63056 c63035_0 c63035_1 c63035_ge2 c63048_0 c63048_1 c63048_ge2 c63057_0
c63057_1 c63057_ge2 c0275T age1 bmi readm complication tothlos where=(gender NE . and bmi NE . and tothlos NE .));
class anest_grpn gender outpatient ASA_1-ASA_5 CPT22102 CPT62380 CPT63005 CPT63011 CPT63012 CPT63017 CPT63030 CPT63047 CPT63056 c63035_0 c63035_1 c63035_ge2 c63048_0 c63048_1 c63048_ge2 c63057_0 c63057_1 c63057_ge2 c0275T;
model anest_grpn (ref='1') = age1 gender bmi outpatient ASA_1-ASA_5 CPT22102 CPT62380 CPT63005 CPT63011 CPT63012 CPT63017 CPT63030 CPT63047 CPT63056 c63035_0 c63035_1 c63035_ge2 c63048_0 c63048_1 c63048_ge2 c63057_0 c63057_1
c63057_ge2 c0275T;
output out=scores_s p=pscore;
proc psmatch data=scores_s region=allobs (psmax=1);
class anest_grpn;
psdata treatvar=anest_grpn (Treated='2') ps=pscore;
match method=greedy (k=3) distance=lps caliper=0.25;
assess lps var = (age1 gender bmi outpatient ASA_1-ASA_5 CPT22102 CPT63005 CPT63011 CPT63012 CPT63017 CPT63030 CPT63047 CPT63056 CPT62380 c63035_0 c63035_1 c63035_ge2 c63048_0 c63048_1 c63048_ge2 c63057_0 c63057_1 c63057_ge2 c0275T)
/ stddev=pooled (allobs=no) stdbinvar=no varinfo;
output out (obs=match) = outgss lps=_lps matchid=_matchID;
ods output varinfo=var_info_s;
ods output stddiff=smd_s;
ods graphics on;
* Perform conditional logistic regression for specified discrete outcome variable;
%macro clr(v);
proc logistic data=outgss;
strata _matchID;
model &v (event='1') = anest_grpn;
ods output oddsratios=or_&v;
proc print data=or_&v;
proc export data=or_&v
outfile="E:\LogonData\UserFolders\sarinm\anesthesia_lumbar\or_&v..xls"
dbms=xls replace;
run;
%mend clr;
%clr(readm);
%clr(complication);
run;
* Perform generalized random block design with fixed block effects for continuous outcome variable (LOS);
ods graphics on / discretemax=2300;
proc glm data=outgss;
class _matchID anest_grpn;
model tothlos = _matchID anest_grpn _matchID*anest_grpn;
means anest_grpn _matchID*anest_grpn;
ods output modelanova=manova_los;
run;
ods graphics off;
proc print data=manova_los;
proc export data=manova_los
outfile='E:\LogonData\UserFolders\sarinm\anesthesia_lumbar\manova_los.xls'
dbms=xls replace;
run;
* Perform multivariate analysis of specified outcome variable using propensity score as a confounding factor;
%macro glmps(v);
proc glm data=outgss outstat=glmout_&v;
class anest_grpn;
model &v = anest_grpn pscore / ss3;
lsmeans anest_grpn;
ods output overallanova=anovao_&v modelanova=anovam_&v;
proc print data=anovao_&v;
proc export data=anovao_&v
outfile="E:\LogonData\UserFolders\sarinm\anesthesia_lumbar\anovao_&v..xls"
dbms=xls replace;
proc print data=anovam_&v;
proc export data=anovam_&v
outfile="E:\LogonData\UserFolders\sarinm\anesthesia_lumbar\anovam_&v..xls"
dbms=xls replace;
run;
%mend glmps;
%glmps(readm);
%glmps(complication);
%glmps(tothlos);
run;
```

1. Is each of these steps correct?

2. Is the generalized random block design with fixed block effects the correct choice for the analysis of the continuous outcome variable?

3. In each of the 4 steps above, what are the appropriate statistical results to report?

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