One Year Survival
##
## ######################################################
## ###################### age _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.589 -1.052 -0.813 1.193 1.700
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.13267 0.77679 -2.745 0.00604 **
## age 0.03673 0.01421 2.585 0.00974 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 169.13 on 126 degrees of freedom
## AIC: 173.13
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### age.cat _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2773 -0.9804 -0.9804 1.0807 1.3881
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4829 0.2361 -2.045 0.0409 *
## age.cat> 55 0.7147 0.3657 1.954 0.0507 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 172.45 on 126 degrees of freedom
## AIC: 176.45
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### sex _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.279 -1.043 -1.043 1.318 1.318
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2364 0.3454 0.684 0.494
## sexF -0.5618 0.4043 -1.390 0.165
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.11 on 126 degrees of freedom
## Residual deviance: 173.16 on 125 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 177.16
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### race _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.133 -1.133 -1.038 1.222 1.323
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1054 0.2297 -0.459 0.647
## raceNon-White -0.2311 0.3721 -0.621 0.535
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 170.74 on 123 degrees of freedom
## Residual deviance: 170.35 on 122 degrees of freedom
## (4 observations deleted due to missingness)
## AIC: 174.35
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### ethnicity _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1478 -1.1478 -0.4854 1.2074 2.0963
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.079 1.061 -1.961 0.0499 *
## ethnicityNH 2.009 1.077 1.865 0.0621 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 169.53 on 122 degrees of freedom
## Residual deviance: 164.18 on 121 degrees of freedom
## (5 observations deleted due to missingness)
## AIC: 168.18
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### bmi _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4071 -1.0712 -0.9491 1.2178 1.4510
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.37619 0.83541 -1.647 0.0995 .
## bmi 0.03637 0.02510 1.449 0.1473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 167.95 on 121 degrees of freedom
## Residual deviance: 165.80 on 120 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 169.8
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### bmi.cat _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1897 -1.1897 -0.9005 1.1651 1.4823
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6931 0.5000 -1.386 0.166
## bmi.cat(25,30] 0.2877 0.6075 0.474 0.636
## bmi.cat(30,80] 0.7221 0.5550 1.301 0.193
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 167.95 on 121 degrees of freedom
## Residual deviance: 165.67 on 119 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 171.67
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### diabetes _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3911 -0.9293 -0.9293 0.9778 1.4477
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6162 0.2388 -2.580 0.00988 **
## diabetesTRUE 1.1057 0.3767 2.935 0.00333 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.11 on 126 degrees of freedom
## Residual deviance: 166.17 on 125 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 170.17
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### CAD _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.231 -1.063 -1.063 1.296 1.296
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2754 0.2071 -1.330 0.184
## CADTRUE 0.4006 0.4104 0.976 0.329
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.11 on 126 degrees of freedom
## Residual deviance: 174.15 on 125 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 178.15
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### cause.esrd _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4006 -1.0302 -0.8519 0.9695 1.5425
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3567 0.3485 -1.024 0.3060
## cause.esrddiabetes 0.8675 0.4586 1.892 0.0585 .
## cause.esrdglomerulonephritis -0.4700 0.5717 -0.822 0.4110
## cause.esrdhypertension -0.4055 0.5753 -0.705 0.4809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.11 on 126 degrees of freedom
## Residual deviance: 165.37 on 123 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 173.37
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### pd.ini.dose _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1173 -1.1173 -0.9695 1.2388 1.4006
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1431 0.2188 -0.654 0.513
## pd.ini.dose3.5 -0.3677 0.4750 -0.774 0.439
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 148.38 on 107 degrees of freedom
## Residual deviance: 147.77 on 106 degrees of freedom
## (20 observations deleted due to missingness)
## AIC: 151.77
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### pd_method2 _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1586 -1.1586 -0.9751 1.1963 1.3942
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.04445 0.21087 -0.211 0.833
## pd_method2CCPD -0.45199 0.39922 -1.132 0.258
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.11 on 126 degrees of freedom
## Residual deviance: 173.80 on 125 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 177.8
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### pd.vintage _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.222 -1.117 -0.892 1.193 1.622
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.12173 0.28399 0.429 0.668
## pd.vintage -0.12212 0.08864 -1.378 0.168
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 174.34 on 126 degrees of freedom
## AIC: 178.34
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### total.vintage _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2431 -1.1297 -0.8596 1.2051 1.6548
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.17409 0.27633 0.630 0.5287
## total.vintage -0.10074 0.06015 -1.675 0.0939 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 173.32 on 126 degrees of freedom
## AIC: 177.32
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### hx.hemo _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.123 -1.123 -1.076 1.233 1.282
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1292 0.2545 -0.508 0.612
## hx.hemoTRUE -0.1144 0.3554 -0.322 0.747
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 176.22 on 126 degrees of freedom
## AIC: 180.22
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### switch.hemo _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.177 -1.042 -1.042 1.177 1.319
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.192e-17 2.722e-01 0.000 1.000
## switch.hemoTRUE -3.272e-01 3.600e-01 -0.909 0.363
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 175.49 on 126 degrees of freedom
## AIC: 179.49
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### sts.order _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2130 -1.1359 -0.6876 1.2195 1.7653
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.08338 0.28893 0.289 0.7729
## sts.orderlow -1.40514 0.63257 -2.221 0.0263 *
## sts.ordernone -0.18182 0.38628 -0.471 0.6379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 170.43 on 125 degrees of freedom
## AIC: 176.43
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.sts _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.123 -1.123 -1.076 1.233 1.282
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1292 0.2545 -0.508 0.612
## drug.stsTRUE -0.1144 0.3554 -0.322 0.747
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 176.22 on 126 degrees of freedom
## AIC: 180.22
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.sensipar _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.103 -1.103 -1.089 1.254 1.268
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.17825 0.21166 -0.842 0.400
## drug.sensiparTRUE -0.03306 0.38890 -0.085 0.932
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 176.31 on 126 degrees of freedom
## AIC: 180.31
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.warfrin _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.374 -1.056 -1.056 1.304 1.304
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2930 0.1927 -1.520 0.128
## drug.warfrinTRUE 0.7450 0.5205 1.431 0.152
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 174.21 on 126 degrees of freedom
## AIC: 178.21
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.acearb _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1193 -1.1193 -0.9005 1.2366 1.4823
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1382 0.1861 -0.742 0.458
## drug.acearbTRUE -0.5550 0.6400 -0.867 0.386
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 175.53 on 126 degrees of freedom
## AIC: 179.53
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.cabinder _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.113 -1.097 -1.097 1.260 1.260
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.19189 0.18736 -1.024 0.306
## drug.cabinderTRUE 0.03774 0.58705 0.064 0.949
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 176.31 on 126 degrees of freedom
## AIC: 180.31
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.calcitriol _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1608 -1.1608 -0.8576 1.1941 1.5353
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.03922 0.19807 -0.198 0.8430
## drug.calcitriolTRUE -0.77171 0.46881 -1.646 0.0997 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 173.46 on 126 degrees of freedom
## AIC: 177.46
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.doxere _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.108 -1.108 -1.046 1.248 1.315
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1655 0.1922 -0.861 0.389
## drug.doxereTRUE -0.1529 0.5028 -0.304 0.761
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 176.23 on 126 degrees of freedom
## AIC: 180.23
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.enoxaparin _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.482 -1.090 -1.090 1.267 1.267
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2088 0.1799 -1.161 0.246
## drug.enoxaparinTRUE 0.9019 1.2379 0.729 0.466
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 175.75 on 126 degrees of freedom
## AIC: 179.75
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.esa _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.354 -1.007 -1.007 1.358 1.358
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4055 0.3450 1.175 0.2399
## drug.esaTRUE -0.8199 0.4049 -2.025 0.0429 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 172.13 on 126 degrees of freedom
## AIC: 176.13
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.heparin _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.119 -1.119 -1.023 1.237 1.340
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1388 0.1995 -0.696 0.486
## drug.heparinTRUE -0.2359 0.4396 -0.537 0.592
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 176.03 on 126 degrees of freedom
## AIC: 180.03
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.iron _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2537 -1.2537 -0.8549 1.1030 1.5387
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1777 0.2259 0.787 0.43156
## drug.ironTRUE -0.9960 0.3836 -2.597 0.00941 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 169.26 on 126 degrees of freedom
## AIC: 173.26
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.loinsulin _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.101 -1.098 -1.098 1.258 1.258
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.18859 0.18572 -1.015 0.310
## drug.loinsulinTRUE 0.00627 0.63337 0.010 0.992
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 176.32 on 126 degrees of freedom
## AIC: 180.32
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.miscinjections _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.100 -1.100 -1.093 1.257 1.264
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.18572 0.19328 -0.961 0.337
## drug.miscinjectionsTRUE -0.01495 0.48926 -0.031 0.976
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 176.32 on 126 degrees of freedom
## AIC: 180.32
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.ncabinder _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.136 -1.136 -1.032 1.219 1.330
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.09764 0.22113 -0.442 0.659
## drug.ncabinderTRUE -0.25376 0.37224 -0.682 0.495
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 175.85 on 126 degrees of freedom
## AIC: 179.85
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.nvitd _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.177 -1.091 -1.091 1.267 1.267
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2076 0.1867 -1.112 0.266
## drug.nvitdTRUE 0.2076 0.6068 0.342 0.732
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 176.20 on 126 degrees of freedom
## AIC: 180.2
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.pari _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.123 -1.123 -1.123 1.233 1.233
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1292 0.1800 -0.718 0.473
## drug.pariTRUE -16.4369 1199.7724 -0.014 0.989
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 171.38 on 126 degrees of freedom
## AIC: 175.38
##
## Number of Fisher Scoring iterations: 15
##
##
##
## ######################################################
## ###################### drug.statin _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1261 -1.1261 -0.8576 1.2296 1.5353
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1219 0.1868 -0.652 0.514
## drug.statinTRUE -0.6890 0.6293 -1.095 0.274
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 175.05 on 126 degrees of freedom
## AIC: 179.05
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.steroid _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.177 -1.097 -1.097 1.260 1.260
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1911 0.1790 -1.067 0.286
## drug.steroidTRUE 0.1911 1.4255 0.134 0.893
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 176.30 on 126 degrees of freedom
## AIC: 180.3
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.activevitd _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2172 -1.2172 -0.8607 1.1381 1.5315
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.09309 0.21590 0.431 0.6663
## drug.activevitdTRUE -0.89544 0.39752 -2.253 0.0243 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 171.01 on 126 degrees of freedom
## AIC: 175.01
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.shinsulin _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.482 -1.072 -1.072 1.287 1.287
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2534 0.1848 -1.371 0.170
## drug.shinsulinTRUE 0.9466 0.7309 1.295 0.195
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 174.53 on 126 degrees of freedom
## AIC: 178.53
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.binder _1yr ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.177 -1.133 -1.028 1.222 1.335
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1054 0.2297 -0.459 0.647
## drug.binderboth -0.1823 0.7976 -0.229 0.819
## drug.bindercabinder 0.1054 0.8482 0.124 0.901
## drug.binderncabinder -0.2575 0.3984 -0.646 0.518
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.32 on 127 degrees of freedom
## Residual deviance: 175.83 on 124 degrees of freedom
## AIC: 183.83
##
## Number of Fisher Scoring iterations: 4
Survival by STS (yes, no)
## Call: survfit(formula = Surv(fu1y, death1y) ~ drug.sts, data = dat)
##
## n events median 0.95LCL 0.95UCL
## drug.sts=FALSE 62 29 NA 0.504 NA
## drug.sts=TRUE 66 29 0.969 0.553 NA
## Call: survfit(formula = Surv(fu1y, death1y) ~ drug.sts, data = dat)
##
## drug.sts=FALSE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 62 0 1.000 0.0000 1.000 1.000
## 0.5 35 23 0.615 0.0632 0.503 0.752
## 1.0 29 6 0.510 0.0654 0.396 0.655
##
## drug.sts=TRUE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 66 0 1.000 0.0000 1.000 1.000
## 0.5 35 21 0.651 0.0621 0.540 0.785
## 1.0 21 8 0.497 0.0675 0.381 0.648
## Call:
## coxph(formula = Surv(fu1y, death1y) ~ drug.sts, data = dat)
##
## n= 128, number of events= 58
##
## coef exp(coef) se(coef) z Pr(>|z|)
## drug.stsTRUE -0.1176 0.8891 0.2631 -0.447 0.655
##
## exp(coef) exp(-coef) lower .95 upper .95
## drug.stsTRUE 0.8891 1.125 0.5309 1.489
##
## Concordance= 0.535 (se = 0.034 )
## Rsquare= 0.002 (max possible= 0.983 )
## Likelihood ratio test= 0.2 on 1 df, p=0.6551
## Wald test = 0.2 on 1 df, p=0.655
## Score (logrank) test = 0.2 on 1 df, p=0.6548
Survival by STS (high, low, none)
## Call: survfit(formula = Surv(fu1y, death1y) ~ sts.order, data = dat)
##
## n events median 0.95LCL 0.95UCL
## sts.order=high 48 25 0.567 0.40 NA
## sts.order=low 19 4 NA NA NA
## sts.order=none 61 29 NA 0.49 NA
## Call: survfit(formula = Surv(fu1y, death1y) ~ sts.order, data = dat)
##
## sts.order=high
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 48 0 1.000 0.0000 1.000 1.000
## 0.5 23 18 0.592 0.0747 0.462 0.758
## 1.0 12 7 0.404 0.0784 0.276 0.591
##
## sts.order=low
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 19 0 1.000 0.0000 1.000 1.000
## 0.5 13 3 0.812 0.0976 0.642 1.000
## 1.0 10 1 0.750 0.1083 0.565 0.995
##
## sts.order=none
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 61 0 1.000 0.0000 1.000 1.000
## 0.5 34 23 0.609 0.0639 0.495 0.748
## 1.0 28 6 0.501 0.0660 0.387 0.649
## Call:
## coxph(formula = Surv(fu1y, death1y) ~ sts.order, data = dat)
##
## n= 128, number of events= 58
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sts.orderlow -1.1733 0.3093 0.5390 -2.177 0.0295 *
## sts.ordernone -0.1051 0.9002 0.2737 -0.384 0.7009
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sts.orderlow 0.3093 3.233 0.1076 0.8897
## sts.ordernone 0.9002 1.111 0.5264 1.5393
##
## Concordance= 0.557 (se = 0.036 )
## Rsquare= 0.05 (max possible= 0.983 )
## Likelihood ratio test= 6.56 on 2 df, p=0.03766
## Wald test = 4.79 on 2 df, p=0.09129
## Score (logrank) test = 5.3 on 2 df, p=0.07079
Survival by Sensipar (yes, no)
## Call: survfit(formula = Surv(fu1y, death1y) ~ drug.sensipar, data = dat)
##
## n events median 0.95LCL 0.95UCL
## drug.sensipar=FALSE 90 41 0.969 0.553 NA
## drug.sensipar=TRUE 38 17 NA 0.518 NA
## Call: survfit(formula = Surv(fu1y, death1y) ~ drug.sensipar, data = dat)
##
## drug.sensipar=FALSE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 90 0 1.000 0.0000 1.000 1.000
## 0.5 48 31 0.632 0.0530 0.536 0.745
## 1.0 34 10 0.497 0.0564 0.398 0.621
##
## drug.sensipar=TRUE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 38 0 1.000 0.0000 1.000 1.000
## 0.5 22 13 0.643 0.0797 0.505 0.820
## 1.0 16 4 0.526 0.0839 0.385 0.719
## Call:
## coxph(formula = Surv(fu1y, death1y) ~ drug.sensipar, data = dat)
##
## n= 128, number of events= 58
##
## coef exp(coef) se(coef) z Pr(>|z|)
## drug.sensiparTRUE -0.0724 0.9302 0.2885 -0.251 0.802
##
## exp(coef) exp(-coef) lower .95 upper .95
## drug.sensiparTRUE 0.9302 1.075 0.5284 1.637
##
## Concordance= 0.508 (se = 0.032 )
## Rsquare= 0 (max possible= 0.983 )
## Likelihood ratio test= 0.06 on 1 df, p=0.8009
## Wald test = 0.06 on 1 df, p=0.8019
## Score (logrank) test = 0.06 on 1 df, p=0.8018
Survival by Warfrin (yes, no)
## Call: survfit(formula = Surv(fu1y, death1y) ~ drug.warfrin, data = dat)
##
## n events median 0.95LCL 0.95UCL
## drug.warfrin=FALSE 110 47 NA 0.567 NA
## drug.warfrin=TRUE 18 11 0.665 0.400 NA
## Call: survfit(formula = Surv(fu1y, death1y) ~ drug.warfrin, data = dat)
##
## drug.warfrin=FALSE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 110 0 1.000 0.0000 1.000 1.000
## 0.5 61 36 0.652 0.0472 0.566 0.751
## 1.0 44 11 0.533 0.0504 0.443 0.641
##
## drug.warfrin=TRUE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 18 0 1.000 0.000 1.000 1.000
## 0.5 9 8 0.542 0.120 0.351 0.836
## 1.0 6 3 0.361 0.117 0.192 0.681
## Call:
## coxph(formula = Surv(fu1y, death1y) ~ drug.warfrin, data = dat)
##
## n= 128, number of events= 58
##
## coef exp(coef) se(coef) z Pr(>|z|)
## drug.warfrinTRUE 0.3297 1.3906 0.3351 0.984 0.325
##
## exp(coef) exp(-coef) lower .95 upper .95
## drug.warfrinTRUE 1.391 0.7191 0.7211 2.682
##
## Concordance= 0.514 (se = 0.024 )
## Rsquare= 0.007 (max possible= 0.983 )
## Likelihood ratio test= 0.9 on 1 df, p=0.3419
## Wald test = 0.97 on 1 df, p=0.3251
## Score (logrank) test = 0.98 on 1 df, p=0.3229
Two Years Survival
##
## ######################################################
## ###################### age _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.722 -1.137 0.761 1.118 1.537
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.74206 0.76358 -2.281 0.0225 *
## age 0.03559 0.01421 2.506 0.0122 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 170.19 on 126 degrees of freedom
## AIC: 174.19
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### age.cat _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4190 -1.1110 0.9537 1.2453 1.2453
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1582 0.2301 -0.688 0.492
## age.cat> 55 0.7103 0.3686 1.927 0.054 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 173.16 on 126 degrees of freedom
## AIC: 177.16
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### sex _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5023 -1.1501 0.8842 1.2049 1.2049
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7376 0.3666 2.012 0.0442 *
## sexF -0.8021 0.4212 -1.904 0.0569 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.42 on 126 degrees of freedom
## Residual deviance: 171.63 on 125 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 175.63
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### race _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.315 -1.315 1.046 1.046 1.249
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3185 0.2323 1.371 0.170
## raceNon-White -0.4855 0.3713 -1.307 0.191
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 171.38 on 123 degrees of freedom
## Residual deviance: 169.66 on 122 degrees of freedom
## (4 observations deleted due to missingness)
## AIC: 173.66
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### ethnicity _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.268 -1.268 1.089 1.089 1.482
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6931 0.7071 -0.980 0.327
## ethnicityNH 0.9045 0.7318 1.236 0.216
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 169.86 on 122 degrees of freedom
## Residual deviance: 168.23 on 121 degrees of freedom
## (5 observations deleted due to missingness)
## AIC: 172.23
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### bmi _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.443 -1.201 1.011 1.130 1.239
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.63510 0.82257 -0.772 0.44
## bmi 0.02374 0.02489 0.954 0.34
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 168.60 on 121 degrees of freedom
## Residual deviance: 167.68 on 120 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 171.68
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### bmi.cat _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.317 -1.202 1.044 1.044 1.374
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4520 0.4835 -0.935 0.350
## bmi.cat(25,30] 0.5091 0.5900 0.863 0.388
## bmi.cat(30,80] 0.7736 0.5415 1.429 0.153
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 168.60 on 121 degrees of freedom
## Residual deviance: 166.44 on 119 degrees of freedom
## (6 observations deleted due to missingness)
## AIC: 172.44
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### diabetes _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5518 -1.0579 0.8446 1.3018 1.3018
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2877 0.2303 -1.249 0.2116
## diabetesTRUE 1.1350 0.3851 2.948 0.0032 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.42 on 126 degrees of freedom
## Residual deviance: 166.25 on 125 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 170.25
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### CAD _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4614 -1.1685 0.9178 1.1864 1.1864
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.02105 0.20521 -0.103 0.918
## CADTRUE 0.66768 0.42502 1.571 0.116
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.42 on 126 degrees of freedom
## Residual deviance: 172.87 on 125 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 176.87
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### cause.esrd _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5698 -0.9964 0.8305 1.1774 1.4224
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.372e-16 3.430e-01 0.000 1.0000
## cause.esrddiabetes 8.873e-01 4.674e-01 1.898 0.0577 .
## cause.esrdglomerulonephritis -4.418e-01 5.479e-01 -0.806 0.4200
## cause.esrdhypertension -5.596e-01 5.604e-01 -0.999 0.3180
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.42 on 126 degrees of freedom
## Residual deviance: 164.71 on 123 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 172.71
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### pd.ini.dose _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.281 -1.281 1.078 1.078 1.323
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2392 0.2198 1.088 0.276
## pd.ini.dose3.5 -0.5757 0.4688 -1.228 0.219
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 149.39 on 107 degrees of freedom
## Residual deviance: 147.86 on 106 degrees of freedom
## (20 observations deleted due to missingness)
## AIC: 151.86
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### pd_method2 _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.293 -1.293 1.066 1.066 1.247
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2683 0.2127 1.261 0.207
## pd_method2CCPD -0.4308 0.3925 -1.097 0.272
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175.42 on 126 degrees of freedom
## Residual deviance: 174.21 on 125 degrees of freedom
## (1 observation deleted due to missingness)
## AIC: 178.21
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### pd.vintage _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.371 -1.222 1.011 1.106 1.505
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.46258 0.28534 1.621 0.105
## pd.vintage -0.13095 0.08675 -1.509 0.131
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 174.60 on 126 degrees of freedom
## AIC: 178.6
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### total.vintage _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3977 -1.2164 0.9818 1.0780 1.5452
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.52704 0.27865 1.891 0.0586 .
## total.vintage -0.10961 0.05878 -1.865 0.0622 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 173.26 on 126 degrees of freedom
## AIC: 177.26
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### hx.hemo _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.318 -1.152 1.043 1.083 1.203
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3254 0.2574 1.264 0.206
## hx.hemoTRUE -0.3860 0.3562 -1.084 0.278
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 175.77 on 126 degrees of freedom
## AIC: 179.77
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### switch.hemo _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.241 -1.224 1.115 1.132 1.132
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.14842 0.27291 0.544 0.587
## switch.hemoTRUE -0.04021 0.35874 -0.112 0.911
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.93 on 126 degrees of freedom
## AIC: 180.93
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### sts.order _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.323 -1.306 1.038 1.054 1.634
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.33647 0.29277 1.149 0.2504
## sts.orderlow -1.36609 0.59761 -2.286 0.0223 *
## sts.ordernone -0.03922 0.39083 -0.100 0.9201
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 170.33 on 125 degrees of freedom
## AIC: 176.33
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.sts _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.289 -1.177 1.069 1.177 1.177
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2595 0.2561 1.013 0.311
## drug.stsTRUE -0.2595 0.3553 -0.730 0.465
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.41 on 126 degrees of freedom
## AIC: 180.41
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.sensipar _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.268 -1.215 1.089 1.140 1.140
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.08895 0.21103 0.421 0.673
## drug.sensiparTRUE 0.12236 0.38855 0.315 0.753
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.85 on 126 degrees of freedom
## AIC: 180.85
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.warfrin _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.734 -1.162 0.709 1.193 1.193
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.03637 0.19072 -0.191 0.8488
## drug.warfrinTRUE 1.28913 0.59817 2.155 0.0312 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 171.53 on 126 degrees of freedom
## AIC: 175.53
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.acearb _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.237 -1.237 1.119 1.119 1.177
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1382 0.1861 0.742 0.458
## drug.acearbTRUE -0.1382 0.6066 -0.228 0.820
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.89 on 126 degrees of freedom
## AIC: 180.89
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.cabinder _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3824 -1.2146 0.9854 1.1407 1.1407
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.08701 0.18668 0.466 0.641
## drug.cabinderTRUE 0.38299 0.59987 0.638 0.523
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.53 on 126 degrees of freedom
## AIC: 180.53
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.calcitriol _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.297 -1.297 1.063 1.063 1.382
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2763 0.1999 1.382 0.1670
## drug.calcitriolTRUE -0.7463 0.4500 -1.658 0.0972 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 174.12 on 126 degrees of freedom
## AIC: 178.12
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.doxere _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.265 -1.265 1.093 1.093 1.315
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2025 0.1925 1.052 0.293
## drug.doxereTRUE -0.5210 0.5030 -1.036 0.300
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 175.86 on 126 degrees of freedom
## AIC: 179.86
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.enoxaparin _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.482 -1.225 1.130 1.130 1.130
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1121 0.1792 0.626 0.531
## drug.enoxaparinTRUE 0.5810 1.2378 0.469 0.639
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.71 on 126 degrees of freedom
## AIC: 180.71
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.esa _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4074 -1.1683 0.9636 1.1866 1.1866
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.5261 0.3498 1.504 0.133
## drug.esaTRUE -0.5476 0.4067 -1.346 0.178
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 175.09 on 126 degrees of freedom
## AIC: 179.09
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.heparin _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.254 -1.254 1.103 1.103 1.209
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1787 0.1998 0.894 0.371
## drug.heparinTRUE -0.2528 0.4339 -0.583 0.560
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.61 on 126 degrees of freedom
## AIC: 180.61
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.iron _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.344 -1.130 1.019 1.019 1.302
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.3844 0.2292 1.677 0.0935 .
## drug.ironTRUE -0.6721 0.3686 -1.823 0.0682 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 173.58 on 126 degrees of freedom
## AIC: 177.58
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.loinsulin _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4224 -1.2139 0.9508 1.1413 1.1413
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.08552 0.18507 0.462 0.644
## drug.loinsulinTRUE 0.47409 0.65353 0.725 0.468
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.40 on 126 degrees of freedom
## AIC: 180.4
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.miscinjections _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.257 -1.257 1.100 1.100 1.264
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1857 0.1933 0.961 0.337
## drug.miscinjectionsTRUE -0.3864 0.4893 -0.790 0.430
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.32 on 126 degrees of freedom
## AIC: 180.32
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.ncabinder _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.252 -1.219 1.104 1.136 1.136
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.09764 0.22113 0.442 0.659
## drug.ncabinderTRUE 0.07671 0.36948 0.208 0.836
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.90 on 126 degrees of freedom
## AIC: 180.9
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.nvitd _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.323 -1.222 1.038 1.134 1.134
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1035 0.1859 0.557 0.578
## drug.nvitdTRUE 0.2329 0.6143 0.379 0.705
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.80 on 126 degrees of freedom
## AIC: 180.8
##
## Number of Fisher Scoring iterations: 3
##
##
##
## ######################################################
## ###################### drug.pari _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.261 -1.261 1.096 1.096 1.096
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1942 0.1805 1.076 0.282
## drug.pariTRUE -16.7602 1199.7724 -0.014 0.989
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 170.74 on 126 degrees of freedom
## AIC: 174.74
##
## Number of Fisher Scoring iterations: 15
##
##
##
## ######################################################
## ###################### drug.statin _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.260 -1.260 1.097 1.097 1.382
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1919 0.1874 1.024 0.306
## drug.statinTRUE -0.6619 0.6001 -1.103 0.270
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 175.69 on 126 degrees of freedom
## AIC: 179.69
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.steroid _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.218 -1.218 1.137 1.137 1.137
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 9.531e-02 1.784e-01 0.534 0.593
## drug.steroidTRUE 1.547e+01 1.029e+03 0.015 0.988
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 174.39 on 126 degrees of freedom
## AIC: 178.39
##
## Number of Fisher Scoring iterations: 14
##
##
##
## ######################################################
## ###################### drug.activevitd _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3841 -1.3841 0.9839 0.9839 1.4350
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4738 0.2217 2.137 0.03263 *
## drug.activevitdTRUE -1.0616 0.3910 -2.715 0.00663 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 169.27 on 126 degrees of freedom
## AIC: 173.27
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.shinsulin _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.734 -1.199 0.709 1.156 1.156
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.05043 0.18340 0.275 0.783
## drug.shinsulinTRUE 1.20233 0.82249 1.462 0.144
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 174.43 on 126 degrees of freedom
## AIC: 178.43
##
## Number of Fisher Scoring iterations: 4
##
##
##
## ######################################################
## ###################### drug.binder _2yrs ######################
## ######################################################
##
## Call:
## glm(formula = formula, family = binomial, data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4823 -1.1999 0.9792 1.1551 1.1551
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.05264 0.22950 0.229 0.819
## drug.binderboth 0.23504 0.79750 0.295 0.768
## drug.bindercabinder 0.64050 0.89592 0.715 0.475
## drug.binderncabinder 0.10151 0.39477 0.257 0.797
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 176.95 on 127 degrees of freedom
## Residual deviance: 176.34 on 124 degrees of freedom
## AIC: 184.34
##
## Number of Fisher Scoring iterations: 4
Survival by STS (yes, no)
## Call: survfit(formula = Surv(fu2y, death2y) ~ drug.sts, data = dat)
##
## n events median 0.95LCL 0.95UCL
## drug.sts=FALSE 62 35 1.122 0.504 NA
## drug.sts=TRUE 66 33 0.969 0.553 NA
## Call: survfit(formula = Surv(fu2y, death2y) ~ drug.sts, data = dat)
##
## drug.sts=FALSE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 62 0 1.000 0.0000 1.000 1.000
## 0.5 35 23 0.615 0.0632 0.503 0.752
## 1.0 29 6 0.510 0.0654 0.396 0.655
## 2.0 21 6 0.403 0.0647 0.294 0.552
##
## drug.sts=TRUE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 66 0 1.000 0.0000 1.000 1.000
## 0.5 35 21 0.651 0.0621 0.540 0.785
## 1.0 21 8 0.497 0.0675 0.381 0.648
## 2.0 8 4 0.381 0.0745 0.260 0.559
## Call:
## coxph(formula = Surv(fu2y, death2y) ~ drug.sts, data = dat)
##
## n= 128, number of events= 68
##
## coef exp(coef) se(coef) z Pr(>|z|)
## drug.stsTRUE -0.07701 0.92588 0.24490 -0.314 0.753
##
## exp(coef) exp(-coef) lower .95 upper .95
## drug.stsTRUE 0.9259 1.08 0.5729 1.496
##
## Concordance= 0.53 (se = 0.032 )
## Rsquare= 0.001 (max possible= 0.99 )
## Likelihood ratio test= 0.1 on 1 df, p=0.7531
## Wald test = 0.1 on 1 df, p=0.7532
## Score (logrank) test = 0.1 on 1 df, p=0.7531
Survival by STS (high, low, none)
## Call: survfit(formula = Surv(fu2y, death2y) ~ sts.order, data = dat)
##
## n events median 0.95LCL 0.95UCL
## sts.order=high 48 28 0.567 0.40 1.16
## sts.order=low 19 5 NA 1.86 NA
## sts.order=none 61 35 1.122 0.49 NA
## Call: survfit(formula = Surv(fu2y, death2y) ~ sts.order, data = dat)
##
## sts.order=high
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 48 0 1.000 0.0000 1.000 1.000
## 0.5 23 18 0.592 0.0747 0.462 0.758
## 1.0 12 7 0.404 0.0784 0.276 0.591
## 2.0 4 3 0.299 0.0782 0.179 0.499
##
## sts.order=low
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 19 0 1.000 0.0000 1.000 1.000
## 0.5 13 3 0.812 0.0976 0.642 1.000
## 1.0 10 1 0.750 0.1083 0.565 0.995
## 2.0 5 1 0.643 0.1358 0.425 0.973
##
## sts.order=none
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 61 0 1.000 0.0000 1.000 1.000
## 0.5 34 23 0.609 0.0639 0.495 0.748
## 1.0 28 6 0.501 0.0660 0.387 0.649
## 2.0 20 6 0.392 0.0650 0.283 0.543
## Call:
## coxph(formula = Surv(fu2y, death2y) ~ sts.order, data = dat)
##
## n= 128, number of events= 68
##
## coef exp(coef) se(coef) z Pr(>|z|)
## sts.orderlow -1.1594 0.3137 0.4868 -2.382 0.0172 *
## sts.ordernone -0.1612 0.8511 0.2574 -0.626 0.5311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## sts.orderlow 0.3137 3.188 0.1208 0.8144
## sts.ordernone 0.8511 1.175 0.5140 1.4094
##
## Concordance= 0.561 (se = 0.034 )
## Rsquare= 0.057 (max possible= 0.99 )
## Likelihood ratio test= 7.53 on 2 df, p=0.02311
## Wald test = 5.68 on 2 df, p=0.05844
## Score (logrank) test = 6.22 on 2 df, p=0.04462
Survival by Sensipar (yes, no)
## Call: survfit(formula = Surv(fu2y, death2y) ~ drug.sensipar, data = dat)
##
## n events median 0.95LCL 0.95UCL
## drug.sensipar=FALSE 90 47 0.969 0.553 NA
## drug.sensipar=TRUE 38 21 1.013 0.518 NA
## Call: survfit(formula = Surv(fu2y, death2y) ~ drug.sensipar, data = dat)
##
## drug.sensipar=FALSE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 90 0 1.000 0.0000 1.000 1.000
## 0.5 48 31 0.632 0.0530 0.536 0.745
## 1.0 34 10 0.497 0.0564 0.398 0.621
## 2.0 21 6 0.398 0.0582 0.299 0.530
##
## drug.sensipar=TRUE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 38 0 1.000 0.0000 1.000 1.000
## 0.5 22 13 0.643 0.0797 0.505 0.820
## 1.0 16 4 0.526 0.0839 0.385 0.719
## 2.0 8 4 0.390 0.0857 0.253 0.600
## Call:
## coxph(formula = Surv(fu2y, death2y) ~ drug.sensipar, data = dat)
##
## n= 128, number of events= 68
##
## coef exp(coef) se(coef) z Pr(>|z|)
## drug.sensiparTRUE 0.02263 1.02288 0.26265 0.086 0.931
##
## exp(coef) exp(-coef) lower .95 upper .95
## drug.sensiparTRUE 1.023 0.9776 0.6113 1.712
##
## Concordance= 0.498 (se = 0.03 )
## Rsquare= 0 (max possible= 0.99 )
## Likelihood ratio test= 0.01 on 1 df, p=0.9315
## Wald test = 0.01 on 1 df, p=0.9314
## Score (logrank) test = 0.01 on 1 df, p=0.9314
Survival by Warfrin (yes, no)
## Call: survfit(formula = Surv(fu2y, death2y) ~ drug.warfrin, data = dat)
##
## n events median 0.95LCL 0.95UCL
## drug.warfrin=FALSE 110 54 1.177 0.567 NA
## drug.warfrin=TRUE 18 14 0.665 0.400 NA
## Call: survfit(formula = Surv(fu2y, death2y) ~ drug.warfrin, data = dat)
##
## drug.warfrin=FALSE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 110 0 1.000 0.0000 1.000 1.000
## 0.5 61 36 0.652 0.0472 0.566 0.751
## 1.0 44 11 0.533 0.0504 0.443 0.641
## 2.0 26 7 0.436 0.0532 0.343 0.554
##
## drug.warfrin=TRUE
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0 18 0 1.000 0.0000 1.000 1.000
## 0.5 9 8 0.542 0.1201 0.351 0.836
## 1.0 6 3 0.361 0.1168 0.192 0.681
## 2.0 3 3 0.181 0.0941 0.065 0.501
## Call:
## coxph(formula = Surv(fu2y, death2y) ~ drug.warfrin, data = dat)
##
## n= 128, number of events= 68
##
## coef exp(coef) se(coef) z Pr(>|z|)
## drug.warfrinTRUE 0.4874 1.6280 0.3005 1.622 0.105
##
## exp(coef) exp(-coef) lower .95 upper .95
## drug.warfrinTRUE 1.628 0.6142 0.9034 2.934
##
## Concordance= 0.522 (se = 0.023 )
## Rsquare= 0.018 (max possible= 0.99 )
## Likelihood ratio test= 2.38 on 1 df, p=0.1226
## Wald test = 2.63 on 1 df, p=0.1049
## Score (logrank) test = 2.68 on 1 df, p=0.1015