Table 5: Cox Regression out50$p10 = K=50, Model 10 out50$p11 = K=50, Model 11 out50$p0 = K=50, Proportional Hazards Model out20$p10 = K=20, Model 10 out20$p11 = K=20, Model 11 out20$p0 = K=20, Proportional Hazards Model Va= substitue Ui with Hi * Ui in sandwich estimator of variance where H[i,,]<- diag( 1/sqrt( 1- pmin(.75,diag( omegaivm )))) out50$p10 = K=50, Model 10 > print.sim(out50$p10, out50$ci10) [1] "P-Values" Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 0.195 0.079 0.075 0.063 0.049 0.075 Va,F,d.tilde.with.H Va,F,d.hat.with.H 0.059 0.044 [1] "Mean ci" Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 1.015353 1.470275 1.508286 1.592477 1.669901 1.509199 Va,F,d.tilde.with.H Va,F,d.hat.with.H 1.632539 1.722789 out50$p11 = K=50, Model 11 > print.sim(out50$p11, out50$ci11) [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 0.073 0.091 0.083 0.066 0.049 0.083 Va,F,d.tilde.with.H Va,F,d.hat.with.H 0.054 0.039 [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 0.6144856 0.5926651 0.6079872 0.656153 0.7285184 0.6130399 Va,F,d.tilde.with.H Va,F,d.hat.with.H 0.6862955 0.7804608 out50$p0 = K=50, Proportional Hazards Model > print.sim(out50$p0, out50$ci0) [1] "P-Values" Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 0.042 0.063 0.06 0.043 0.037 0.058 Va,F,d.tilde.with.H Va,F,d.hat.with.H 0.037 0.032 [1] "Mean ci" Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 0.6135 0.5666387 0.581288 0.6249836 0.6717662 0.5850548 Va,F,d.tilde.with.H Va,F,d.hat.with.H 0.6522604 0.7149354 out20$p10 = K=20, Model 10 > print.sim(out20$p10, out20$ci10) [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 0.18 0.119 0.094 0.08 0.047 0.098 Va,F,d.tilde.with.H Va,F,d.hat.with.H 0.068 0.039 [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 1.706536 2.262481 2.425195 2.894189 3.288788 2.436261 Va,F,d.tilde.with.H Va,F,d.hat.with.H 3.129418 3.686196 out20$p11 = K=20, Model 11 > print.sim(out20$p11, out20$ci11) [1] "P-Values" Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 0.104 0.137 0.124 0.089 0.076 0.127 Va,F,d.tilde.with.H Va,F,d.hat.with.H 0.073 0.059 [1] "Mean ci" Vm,Chi2 Vs,Chi2 Vs,F,K-p Vs,F,d.tilde.no.H Vs,F,d.hat.no.H Va,Chi2 1.127675 1.008458 1.080985 1.278747 1.383341 1.076189 Va,F,d.tilde.with.H Va,F,d.hat.with.H 1.402548 1.552893 out20$p0 = K=20, Proportional Hazards Model > print.sim(out20$p0, out20$ci0) [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 0.103 0.089 0.051 0.055 0.09 Va,F,d.tilde.with.H Va,F,d.hat.with.H 0.043 0.038 [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 1.129021 0.9912406 1.062529 1.250025 1.332266 1.05411 Va,F,d.tilde.with.H Va,F,d.hat.with.H 1.367629 1.476209