topic Do you still need to include "site" as a random effect when modeling matched data set? in Statistical Procedures

Do you still need to include "site" as a random effect when modeling matched data set?

UcheOkoro — Mon, 20 Jun 2022 17:10:25 GMT

Hello,

Please, I need some guidance. I am working on a multicenter propensity matched cohort study. The primary outcome is binary while the secondary outcome is continuous. In this study, cases were matched with controls within the same site or a different site with similar characteristics. This was after I performed multiple imputation to address the missing data. I then performed within imputation conditional logistic regression for the binary variable and pulled the results. For the secondary outcome I used negative binomial regression including the match ID in the class statement and as a repeated statement. Do I need to include 'site' as a random statement in the model? I don't know if this is possible in conditional logistic regression. What would be the best way to model this data after matching?

Thank you!

Re: Do you still need to include "site" as a random effect when modeling matched data set?

SteveDenham — Tue, 21 Jun 2022 12:43:14 GMT

It generally is a good idea to account for site variability in an analysis, but be cautious. If there are a small number of sites, you may not be able to estimate a variance component due to site. You might need to fit it as a fixed effect. This is a fairly common occurrence in ecological modeling.

SteveDenham

Re: Do you still need to include "site" as a random effect when modeling matched data set?

UcheOkoro — Tue, 21 Jun 2022 14:41:45 GMT

Thank you so much for your prompt response. There are 23 sites in this study which I believe is a good number.

Please, are there reasons you think 'site' should be a fixed rather than random effect?

How would one fit a fixed or random effect in the conditional logistic regression model?

Please, find below my SAS codes for conditional logistic regression and the conditional negative binomial regression.

Thank you.


/****conditional logistic regression************/
proc logistic data = Avera_matched_data ;
CLASS telemed_use(DESC);
  model vasopressors_24h(event='1') = telemed_use ;
  strata subclass;
ODS OUTPUT PARAMETERESTIMATES=vaso_24hrs ODDSRATIOS=lgsodds;
  BY _Imputation_;
   title " Model of need for vasopressor within first 24hrs by telemed ";
run;
PROC MIANALYZE PARMS(CLASSVAR=CLASSVAL)=vaso_24hrs;
 CLASS telemed_use;
 MODELEFFECTS telemed_use;
 ODS OUTPUT PARAMETERESTIMATES=vaso_24hrs2;
RUN;

*** Log-transform odds ratio estimates
 and obtain standard error from confidence intervals ***;
DATA lgsodds_t; SET lgsodds(WHERE=(INDEX(EFFECT,"telemed_use")));
 log_or_lr_value=LOG(ODDSRATIOEST);
 log_or_lr_se=(LOG(UPPERCL)-LOG(LOWERCL))/(2*1.96);
RUN;
*** Combine transformed estimates;
PROC MIANALYZE DATA=lgsodds_t;
 ODS OUTPUT PARAMETERESTIMATES=mian_lgsodds_t;
 MODELEFFECTS log_or_lr_value;
 STDERR log_or_lr_se;
RUN;
*** Back-transform combined values;
DATA mian_lgsodds_bt; SET mian_lgsodds_t;
 Estimate_back = EXP(ESTIMATE); *Pooled odds ratio;
 LCL_back=Estimate_back*EXP(-1.96*STDERR); *Pooled lower limit;
 UCL_back=Estimate_back*EXP(+1.96*STDERR); *Pooled upper limit;
RUN; 
proc print data=mian_lgsodds_bt;run;

/*****Conditional Negative binomial regression********************/

proc genmod data=Avera_matched_data descending;
class  subclass telemed_use(DESC) age2(desc); 
model  Hospital_free_days4= telemed_use totalbeds EDVolume_2019 age2
/ dist=negbin ;
 repeated sub=subclass /type=cs corrw covb printmle;
 lsmeans telemed_use / diff exp cl;
 ODS OUTPUT ParameterEstimates=Hosp_free2;
  BY _Imputation_;
   title " Model of 28-day hospital free days by telemed";
run;


PROC MIANALYZE PARMS(CLASSVAR=LEVEL)=Hosp_free2;
 CLASS  telemed_use ;
 MODELEFFECTS telemed_use totalbeds EDVolume_2019 age2;
ODS OUTPUT PARAMETERESTIMATES=Hosp_new2;
RUN;