Results for Table 3: GEE with logistic regression, independence working correlation

Va= substitue Ui with Hi * Ui in sandwich estimator of variance where 
        H[i,,]<- diag( 1/sqrt( 1- pmin(.75,diag( omegaivm ))))

parameters are in order, 2=female, 3=age, etc. 



FEMALE
> print.sim(out$p2, out$ci2, out$erri2)
[1] "Number Converged= 1000"
[1] "P-Values"
 Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 
   0.048   0.078    0.065             0.051           0.038   0.047
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
               0.026               0.018
[1] "Mean ci"
  Vm,Chi2  Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H  Va,Chi2 
 2.578491 2.412011 2.506733          2.703043        3.055962 2.660668
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
            3.018727            3.467534

AGE
> print.sim(out$p3, out$ci3, out$erri3)
[1] "Number Converged= 1000"
[1] "P-Values"
 Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 
   0.052   0.067    0.061             0.057           0.039   0.041
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
               0.029               0.018
[1] "Mean ci"
  Vm,Chi2  Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H  Va,Chi2 
 2.383567 2.317671 2.408688          2.459857        2.682431 2.572346
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
            2.741429            3.057724

DAYACC
> print.sim(out$p4, out$ci4, out$erri4)
[1] "Number Converged= 1000"
[1] "P-Values"
 Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 
   0.049   0.076    0.067             0.058           0.044   0.062
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
               0.037               0.031
[1] "Mean ci"
   Vm,Chi2   Vs,Chi2  Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H   Va,Chi2 
 0.2577529 0.2415929 0.2510804         0.2687245       0.2861672 0.2602407
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
           0.2961043           0.3288492


SEVERE
> print.sim(out$p5, out$ci5, out$erri5)
[1] "Number Converged= 1000"
[1] "P-Values"
 Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 
   0.051   0.062    0.056             0.049           0.029   0.031
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
               0.017                0.01
[1] "Mean ci"
  Vm,Chi2  Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H  Va,Chi2 
 1.415124 1.355397 1.408625          1.458026        1.616223 1.557922
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
            1.688515            1.867273


TOILET
> print.sim(out$p6, out$ci6, out$erri6)
[1] "Number Converged= 1000"
[1] "P-Values"
 Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 
   0.055    0.08    0.069             0.056            0.04   0.048
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
               0.028               0.016
[1] "Mean ci"
   Vm,Chi2   Vs,Chi2  Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H   Va,Chi2 
 0.3495851 0.3297853 0.3427363         0.3745463       0.4052991 0.4008734
 Va,F,d.tilde.with.H Va,F,d.hat.with.H 
           0.5513374           0.6361539