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# Modeling binary outcome with repeated measurements

Dear SAS community,

I have a data with 26 subjects in total and 70 observations with non-equally spaced time assessments and non-equal number of assessments.  I have tried GENMOD, GLIMMIX, NLMIXED and regular logistic regression regardless the correlation.

Each method come out with very different results. I understand each method use different algorism for parameter estimation,   but why the results come out so differently, how can I make judgement as which one should I report?  below please find the code and correspondent results. I can send the SAS data if necessary.

SHa

proc nlmixed data=vocal;

*Where Time <6;

parms beta0 =-1.1938 beta1=0.0408;

eta = u+ beta0+beta1*CVR_Cube;

expeta = exp(eta);

p = expeta/(1 + expeta);

model Hydrops ~ binary(p);

random u ~normal(0, s2u) subject=subj_id;

run;

 Parameter Estimates Parameter Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper Gradient beta0 -5.1376 2.0194 25 -2.54 0.0175 0.05 -9.2967 -0.9786 1.10E-06 beta1 0.8079 0.3884 25 2.08 0.0479 0.05 0.00808 1.6077 5.48E-06 s2u 13.3115 10.7961 25 1.23 0.229 0.05 -8.9235 35.5465 -8.14E-

PROC GLIMMIX DATA= VOCAL;

CLASS Subj_Id Time;

MODEL hydrops = cvr_Cube / Dist=binomial Link=logit SOLUTION;

RANDOM intercept / SUBJECT = Subj_Id;

RUN;

 Solutions for Fixed Effects Effect Estimate Standard Error DF t Value Pr > |t| Intercept -2.8561 0.9429 25 -3.03 0.0056 CVR_CUBE 0.5005 0.2228 42 2.25 0.0300

PROC GENMOD DATA = VOCAL DESCENDING;

CLASS Subj_Id ;

MODEL hydrops = cvr_Cube / Dist= Binomial Link = Logit;

REPEATED Subject = Subj_Id / TYPE = CS Corrw Covb;

RUN;

Quit;

 Analysis Of GEE Parameter Estimates Empirical Standard Error Estimates Parameter Estimate Standard Error 95% Confidence Limits Z Pr > |Z| Intercept -1.1938 0.4486 -2.0730 -0.3147 -2.66 0.0078 CVR_CUBE 0.0408 0.0289 -0.0159 0.0975 1.41 0.1585

PROC LOGISTIC DATA = VOCAL DESCENDING;

MODEL hydrops = cvr_cube;

RUN;

 Analysis of Maximum Likelihood Estimates Parameter DF Estimate Standard Error Wald Chi-Square Pr > ChiSq Intercept 1 -2.5059 0.5524 20.5783 <.0001 CVR_CUBE 1 0.4921 0.1297 14.4012 0.0001

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