RMSD : A2.0 Aamavaata
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 333 0.958 0.00225 0.954 0.962
## 2 4133 95 0.936 0.00313 0.930 0.942
## 3 3287 30 0.927 0.00347 0.921 0.934
## 4 2873 30 0.918 0.00386 0.910 0.925
## 7 2203 45 0.901 0.00456 0.892 0.910
## 13 1594 33 0.885 0.00525 0.875 0.895
## 25 919 53 0.847 0.00722 0.833 0.861
## 37 519 18 0.824 0.00878 0.807 0.842
## 49 256 11 0.801 0.01110 0.779 0.823
## 61 110 3 0.787 0.01339 0.761 0.814
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 108 0.983 0.00162 0.980 0.986
## 2 2929 19 0.977 0.00217 0.972 0.981
## 3 2352 4 0.975 0.00232 0.970 0.980
## 4 2087 5 0.973 0.00254 0.968 0.978
## 7 1656 9 0.968 0.00302 0.962 0.974
## 13 1206 13 0.959 0.00389 0.951 0.966
## 25 728 15 0.944 0.00536 0.934 0.955
## 37 430 3 0.939 0.00619 0.927 0.951
## 49 218 3 0.930 0.00818 0.914 0.946
## 61 106 0 0.930 0.00818 0.914 0.946
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -1.0414 0.3530 0.0842 -12.4 <2e-16
##
## Likelihood ratio test=181 on 1 df, p=0
## n= 14291, number of events= 832
RMSD : A2.1 Aamavaata - Kaphaja
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 10 0.999 0.000398 0.998 1.000
## 2 4224 1 0.999 0.000463 0.998 0.999
## 3 3391 1 0.998 0.000549 0.997 0.999
## 4 2976 0 0.998 0.000549 0.997 0.999
## 7 2294 0 0.998 0.000549 0.997 0.999
## 13 1670 1 0.998 0.000753 0.996 0.999
## 25 972 0 0.998 0.000753 0.996 0.999
## 37 553 0 0.998 0.000753 0.996 0.999
## 49 276 0 0.998 0.000753 0.996 0.999
## 61 118 0 0.998 0.000753 0.996 0.999
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 4 0.999 0.000315 0.999 1
## 2 2961 0 0.999 0.000315 0.999 1
## 3 2387 0 0.999 0.000315 0.999 1
## 4 2123 0 0.999 0.000315 0.999 1
## 7 1687 0 0.999 0.000315 0.999 1
## 13 1227 0 0.999 0.000315 0.999 1
## 25 746 0 0.999 0.000315 0.999 1
## 37 444 0 0.999 0.000315 0.999 1
## 49 230 0 0.999 0.000315 0.999 1
## 61 110 0 0.999 0.000315 0.999 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -0.936 0.392 0.572 -1.64 0.1
##
## Likelihood ratio test=3.08 on 1 df, p=0.0791
## n= 14291, number of events= 17
RMSD : A2.2 Aamavaata - Pittaja
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 3 1.000 0.000218 0.999 1
## 2 4224 0 1.000 0.000218 0.999 1
## 3 3391 0 1.000 0.000218 0.999 1
## 4 2976 1 0.999 0.000400 0.999 1
## 7 2295 0 0.999 0.000400 0.999 1
## 13 1672 0 0.999 0.000400 0.999 1
## 25 974 0 0.999 0.000400 0.999 1
## 37 554 0 0.999 0.000400 0.999 1
## 49 277 0 0.999 0.000400 0.999 1
## 61 119 0 0.999 0.000400 0.999 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 2 1.000 0.000222 0.999 1
## 2 2964 1 0.999 0.000404 0.999 1
## 3 2388 0 0.999 0.000404 0.999 1
## 4 2124 0 0.999 0.000404 0.999 1
## 7 1688 0 0.999 0.000404 0.999 1
## 13 1228 0 0.999 0.000404 0.999 1
## 25 747 0 0.999 0.000404 0.999 1
## 37 444 0 0.999 0.000404 0.999 1
## 49 230 0 0.999 0.000404 0.999 1
## 61 110 0 0.999 0.000404 0.999 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -0.0307 0.9698 0.7641 -0.04 0.97
##
## Likelihood ratio test=0 on 1 df, p=0.968
## n= 14291, number of events= 7
RMSD : A2.3 Aamavaata - Vaataja
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 24 0.997 0.000616 0.996 0.998
## 2 4213 0 0.997 0.000616 0.996 0.998
## 3 3381 1 0.997 0.000683 0.995 0.998
## 4 2966 0 0.997 0.000683 0.995 0.998
## 7 2285 1 0.996 0.000793 0.995 0.998
## 13 1666 1 0.996 0.000919 0.994 0.998
## 25 971 0 0.996 0.000919 0.994 0.998
## 37 553 0 0.996 0.000919 0.994 0.998
## 49 277 0 0.996 0.000919 0.994 0.998
## 61 119 0 0.996 0.000919 0.994 0.998
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 4 0.999 0.000315 0.999 1
## 2 2962 1 0.999 0.000461 0.998 1
## 3 2386 0 0.999 0.000461 0.998 1
## 4 2123 0 0.999 0.000461 0.998 1
## 7 1686 1 0.999 0.000695 0.997 1
## 13 1226 1 0.998 0.000994 0.996 1
## 25 745 0 0.998 0.000994 0.996 1
## 37 442 0 0.998 0.000994 0.996 1
## 49 229 0 0.998 0.000994 0.996 1
## 61 110 0 0.998 0.000994 0.996 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -1.110 0.329 0.424 -2.62 0.0088
##
## Likelihood ratio test=8.25 on 1 df, p=0.00407
## n= 14291, number of events= 34
RMSD : A3.0 Abhighataja Shoola
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 262 0.967 0.00201 0.963 0.971
## 2 4114 28 0.960 0.00235 0.956 0.965
## 3 3276 9 0.958 0.00250 0.953 0.963
## 4 2861 6 0.956 0.00263 0.951 0.961
## 7 2195 9 0.952 0.00288 0.946 0.958
## 13 1582 9 0.948 0.00325 0.941 0.954
## 25 906 8 0.941 0.00397 0.933 0.949
## 37 505 0 0.941 0.00397 0.933 0.949
## 49 247 0 0.941 0.00397 0.933 0.949
## 61 111 0 0.941 0.00397 0.933 0.949
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 338 0.947 0.00281 0.941 0.952
## 2 2845 43 0.933 0.00352 0.926 0.939
## 3 2261 8 0.929 0.00369 0.922 0.936
## 4 1999 1 0.929 0.00372 0.921 0.936
## 7 1568 10 0.923 0.00407 0.915 0.931
## 13 1129 4 0.920 0.00432 0.912 0.929
## 25 687 4 0.916 0.00476 0.907 0.926
## 37 403 0 0.916 0.00476 0.907 0.926
## 49 205 0 0.916 0.00476 0.907 0.926
## 61 98 0 0.916 0.00476 0.907 0.926
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 0.465 1.593 0.074 6.29 3.2e-10
##
## Likelihood ratio test=39.9 on 1 df, p=2.74e-10
## n= 14291, number of events= 739
RMSD : S10.0 Stambha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 0 1 0 1 1
## 2 4227 0 1 0 1 1
## 3 3394 0 1 0 1 1
## 4 2978 0 1 0 1 1
## 7 2296 0 1 0 1 1
## 13 1673 0 1 0 1 1
## 25 974 0 1 0 1 1
## 37 554 0 1 0 1 1
## 49 277 0 1 0 1 1
## 61 119 0 1 0 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 2 1 0.000222 0.999 1
## 2 2964 0 1 0.000222 0.999 1
## 3 2388 0 1 0.000222 0.999 1
## 4 2124 0 1 0.000222 0.999 1
## 7 1688 0 1 0.000222 0.999 1
## 13 1228 0 1 0.000222 0.999 1
## 25 747 0 1 0.000222 0.999 1
## 37 444 0 1 0.000222 0.999 1
## 49 230 0 1 0.000222 0.999 1
## 61 110 0 1 0.000222 0.999 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 1.94e+01 2.79e+08 1.06e+04 0 1
##
## Likelihood ratio test=3.24 on 1 df, p=0.0718
## n= 14291, number of events= 2
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
RMSD : S10.1 Stambha - Baahu Stambha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 4 0.999 0.000252 0.999 1
## 2 4226 0 0.999 0.000252 0.999 1
## 3 3394 0 0.999 0.000252 0.999 1
## 4 2978 0 0.999 0.000252 0.999 1
## 7 2296 0 0.999 0.000252 0.999 1
## 13 1673 0 0.999 0.000252 0.999 1
## 25 974 0 0.999 0.000252 0.999 1
## 37 554 0 0.999 0.000252 0.999 1
## 49 277 0 0.999 0.000252 0.999 1
## 61 119 0 0.999 0.000252 0.999 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 3 1 0.000272 0.999 1
## 2 2962 0 1 0.000272 0.999 1
## 3 2386 0 1 0.000272 0.999 1
## 4 2122 0 1 0.000272 0.999 1
## 7 1687 0 1 0.000272 0.999 1
## 13 1228 0 1 0.000272 0.999 1
## 25 747 0 1 0.000272 0.999 1
## 37 444 0 1 0.000272 0.999 1
## 49 230 0 1 0.000272 0.999 1
## 61 110 0 1 0.000272 0.999 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -0.0658 0.9363 0.7638 -0.09 0.93
##
## Likelihood ratio test=0.01 on 1 df, p=0.931
## n= 14291, number of events= 7
RMSD : S10.10 Stambha - Prishtha Stambha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 1 1 0.000126 1 1
## 2 4227 0 1 0.000126 1 1
## 3 3394 0 1 0.000126 1 1
## 4 2978 0 1 0.000126 1 1
## 7 2296 0 1 0.000126 1 1
## 13 1673 0 1 0.000126 1 1
## 25 974 0 1 0.000126 1 1
## 37 554 0 1 0.000126 1 1
## 49 277 0 1 0.000126 1 1
## 61 119 0 1 0.000126 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 1 1 0.000157 1 1
## 2 2964 0 1 0.000157 1 1
## 3 2388 0 1 0.000157 1 1
## 4 2124 0 1 0.000157 1 1
## 7 1688 0 1 0.000157 1 1
## 13 1228 0 1 0.000157 1 1
## 25 747 0 1 0.000157 1 1
## 37 444 0 1 0.000157 1 1
## 49 230 0 1 0.000157 1 1
## 61 110 0 1 0.000157 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 0.222 1.248 1.414 0.16 0.88
##
## Likelihood ratio test=0.02 on 1 df, p=0.875
## n= 14291, number of events= 2
RMSD : S10.12 Stambha - Sandhi Stambha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 1 1 0.000126 1 1
## 2 4226 0 1 0.000126 1 1
## 3 3393 0 1 0.000126 1 1
## 4 2977 0 1 0.000126 1 1
## 7 2295 0 1 0.000126 1 1
## 13 1672 0 1 0.000126 1 1
## 25 973 0 1 0.000126 1 1
## 37 554 0 1 0.000126 1 1
## 49 277 0 1 0.000126 1 1
## 61 119 0 1 0.000126 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 2 1 0.000222 0.999 1
## 2 2962 0 1 0.000222 0.999 1
## 3 2386 0 1 0.000222 0.999 1
## 4 2122 0 1 0.000222 0.999 1
## 7 1687 0 1 0.000222 0.999 1
## 13 1227 0 1 0.000222 0.999 1
## 25 747 0 1 0.000222 0.999 1
## 37 444 0 1 0.000222 0.999 1
## 49 230 0 1 0.000222 0.999 1
## 61 110 0 1 0.000222 0.999 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 0.915 2.497 1.225 0.75 0.45
##
## Likelihood ratio test=0.6 on 1 df, p=0.439
## n= 14291, number of events= 3
RMSD : S10.13 Stambha - Siraa Stambha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 1 1 0.000126 1 1
## 2 4226 0 1 0.000126 1 1
## 3 3394 0 1 0.000126 1 1
## 4 2978 0 1 0.000126 1 1
## 7 2296 0 1 0.000126 1 1
## 13 1673 0 1 0.000126 1 1
## 25 974 0 1 0.000126 1 1
## 37 554 0 1 0.000126 1 1
## 49 277 0 1 0.000126 1 1
## 61 119 0 1 0.000126 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 0 1 0 1 1
## 2 2964 0 1 0 1 1
## 3 2388 0 1 0 1 1
## 4 2124 0 1 0 1 1
## 7 1688 0 1 0 1 1
## 13 1228 0 1 0 1 1
## 25 747 0 1 0 1 1
## 37 444 0 1 0 1 1
## 49 230 0 1 0 1 1
## 61 110 0 1 0 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -1.90e+01 5.61e-09 1.49e+04 0 1
##
## Likelihood ratio test=1.18 on 1 df, p=0.278
## n= 14291, number of events= 1
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
RMSD : S10.14 Stambha - Uru Stambha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 2 1 0.000178 0.999 1
## 2 4225 1 1 0.000296 0.999 1
## 3 3392 0 1 0.000296 0.999 1
## 4 2976 0 1 0.000296 0.999 1
## 7 2294 0 1 0.000296 0.999 1
## 13 1672 0 1 0.000296 0.999 1
## 25 973 0 1 0.000296 0.999 1
## 37 553 0 1 0.000296 0.999 1
## 49 276 0 1 0.000296 0.999 1
## 61 118 0 1 0.000296 0.999 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 0 1 0 1 1
## 2 2964 0 1 0 1 1
## 3 2388 0 1 0 1 1
## 4 2124 0 1 0 1 1
## 7 1688 0 1 0 1 1
## 13 1228 0 1 0 1 1
## 25 747 0 1 0 1 1
## 37 444 0 1 0 1 1
## 49 230 0 1 0 1 1
## 61 110 0 1 0 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -1.90e+01 5.80e-09 8.65e+03 0 1
##
## Likelihood ratio test=3.42 on 1 df, p=0.0645
## n= 14291, number of events= 3
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
RMSD : S10.4 Stambha - Greevaa Stambha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 17 0.998 0.000519 0.997 0.999
## 2 4219 0 0.998 0.000519 0.997 0.999
## 3 3386 0 0.998 0.000519 0.997 0.999
## 4 2972 0 0.998 0.000519 0.997 0.999
## 7 2290 1 0.997 0.000639 0.996 0.999
## 13 1669 0 0.997 0.000639 0.996 0.999
## 25 973 0 0.997 0.000639 0.996 0.999
## 37 553 0 0.997 0.000639 0.996 0.999
## 49 276 0 0.997 0.000639 0.996 0.999
## 61 118 0 0.997 0.000639 0.996 0.999
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 15 0.998 0.000609 0.996 0.999
## 2 2957 3 0.997 0.000843 0.995 0.998
## 3 2380 0 0.997 0.000843 0.995 0.998
## 4 2117 0 0.997 0.000843 0.995 0.998
## 7 1682 1 0.996 0.001009 0.994 0.998
## 13 1222 1 0.995 0.001213 0.993 0.998
## 25 741 0 0.995 0.001213 0.993 0.998
## 37 439 0 0.995 0.001213 0.993 0.998
## 49 228 0 0.995 0.001213 0.993 0.998
## 61 108 0 0.995 0.001213 0.993 0.998
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 0.346 1.413 0.325 1.06 0.29
##
## Likelihood ratio test=1.13 on 1 df, p=0.287
## n= 14291, number of events= 38
RMSD : S10.5 Stambha - Hanu Stambha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 1 1 0.000126 1 1
## 2 4226 0 1 0.000126 1 1
## 3 3394 0 1 0.000126 1 1
## 4 2978 0 1 0.000126 1 1
## 7 2296 0 1 0.000126 1 1
## 13 1673 0 1 0.000126 1 1
## 25 974 0 1 0.000126 1 1
## 37 554 0 1 0.000126 1 1
## 49 277 0 1 0.000126 1 1
## 61 119 0 1 0.000126 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 1 1.000 0.000157 1.000 1
## 2 2963 1 1.000 0.000372 0.999 1
## 3 2387 1 0.999 0.000560 0.998 1
## 4 2123 0 0.999 0.000560 0.998 1
## 7 1687 0 0.999 0.000560 0.998 1
## 13 1228 0 0.999 0.000560 0.998 1
## 25 747 0 0.999 0.000560 0.998 1
## 37 444 0 0.999 0.000560 0.998 1
## 49 230 0 0.999 0.000560 0.998 1
## 61 110 0 0.999 0.000560 0.998 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 1.39 4.00 1.16 1.2 0.23
##
## Likelihood ratio test=1.71 on 1 df, p=0.192
## n= 14291, number of events= 4
RMSD : S10.6 Stambha - Hridaya Stambha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 0 1 0 1 1
## 2 4227 0 1 0 1 1
## 3 3394 0 1 0 1 1
## 4 2978 0 1 0 1 1
## 7 2296 0 1 0 1 1
## 13 1673 0 1 0 1 1
## 25 974 0 1 0 1 1
## 37 554 0 1 0 1 1
## 49 277 0 1 0 1 1
## 61 119 0 1 0 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 1 1 0.000157 1 1
## 2 2964 0 1 0.000157 1 1
## 3 2388 0 1 0.000157 1 1
## 4 2124 0 1 0.000157 1 1
## 7 1688 0 1 0.000157 1 1
## 13 1228 0 1 0.000157 1 1
## 25 747 0 1 0.000157 1 1
## 37 444 0 1 0.000157 1 1
## 49 230 0 1 0.000157 1 1
## 61 110 0 1 0.000157 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 1.94e+01 2.79e+08 1.49e+04 0 1
##
## Likelihood ratio test=1.62 on 1 df, p=0.203
## n= 14291, number of events= 1
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
RMSD : S13.0 Sthaanabhedena Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 5 0.999 0.000282 0.999 1
## 2 4222 1 0.999 0.000368 0.998 1
## 3 3388 0 0.999 0.000368 0.998 1
## 4 2972 0 0.999 0.000368 0.998 1
## 7 2290 0 0.999 0.000368 0.998 1
## 13 1669 0 0.999 0.000368 0.998 1
## 25 971 0 0.999 0.000368 0.998 1
## 37 551 0 0.999 0.000368 0.998 1
## 49 276 0 0.999 0.000368 0.998 1
## 61 119 0 0.999 0.000368 0.998 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 7 0.999 0.000416 0.998 1
## 2 2961 1 0.999 0.000535 0.998 1
## 3 2385 0 0.999 0.000535 0.998 1
## 4 2121 0 0.999 0.000535 0.998 1
## 7 1686 1 0.998 0.000798 0.996 1
## 13 1226 0 0.998 0.000798 0.996 1
## 25 746 0 0.998 0.000798 0.996 1
## 37 443 0 0.998 0.000798 0.996 1
## 49 230 0 0.998 0.000798 0.996 1
## 61 110 0 0.998 0.000798 0.996 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 0.651 1.918 0.527 1.23 0.22
##
## Likelihood ratio test=1.57 on 1 df, p=0.211
## n= 14291, number of events= 15
RMSD : S13.1 Sthaanabhedena Graha - Anga Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 1 1 0.000126 1 1
## 2 4227 0 1 0.000126 1 1
## 3 3394 0 1 0.000126 1 1
## 4 2978 0 1 0.000126 1 1
## 7 2296 0 1 0.000126 1 1
## 13 1673 0 1 0.000126 1 1
## 25 974 0 1 0.000126 1 1
## 37 554 0 1 0.000126 1 1
## 49 277 0 1 0.000126 1 1
## 61 119 0 1 0.000126 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 2 1.000 0.000222 0.999 1
## 2 2963 0 1.000 0.000222 0.999 1
## 3 2387 0 1.000 0.000222 0.999 1
## 4 2123 1 0.999 0.000521 0.998 1
## 7 1687 0 0.999 0.000521 0.998 1
## 13 1227 0 0.999 0.000521 0.998 1
## 25 746 0 0.999 0.000521 0.998 1
## 37 443 0 0.999 0.000521 0.998 1
## 49 230 0 0.999 0.000521 0.998 1
## 61 110 0 0.999 0.000521 0.998 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 1.35 3.86 1.15 1.17 0.24
##
## Likelihood ratio test=1.61 on 1 df, p=0.204
## n= 14291, number of events= 4
RMSD : S13.11 Sthaanabhedena Graha - Katee Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 315 0.960 0.00219 0.956 0.965
## 2 4074 57 0.947 0.00279 0.941 0.952
## 3 3228 24 0.940 0.00312 0.934 0.946
## 4 2811 14 0.935 0.00334 0.929 0.942
## 7 2134 20 0.927 0.00377 0.920 0.935
## 13 1531 15 0.920 0.00421 0.911 0.928
## 25 871 15 0.908 0.00507 0.899 0.918
## 37 484 2 0.906 0.00539 0.895 0.916
## 49 239 5 0.891 0.00835 0.875 0.908
## 61 100 1 0.886 0.00975 0.867 0.906
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 387 0.939 0.00300 0.933 0.945
## 2 2814 41 0.925 0.00364 0.918 0.933
## 3 2229 14 0.920 0.00393 0.912 0.927
## 4 1965 10 0.915 0.00418 0.907 0.923
## 7 1538 25 0.901 0.00499 0.891 0.911
## 13 1099 13 0.892 0.00554 0.881 0.903
## 25 655 17 0.875 0.00674 0.862 0.889
## 37 381 5 0.866 0.00784 0.851 0.882
## 49 196 2 0.860 0.00885 0.843 0.878
## 61 89 0 0.860 0.00885 0.843 0.878
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 0.3585 1.4312 0.0639 5.61 2e-08
##
## Likelihood ratio test=31.5 on 1 df, p=2.02e-08
## n= 14291, number of events= 982
RMSD : S13.13 Sthaanabhedena Graha - Manyaa Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 16 0.998 0.000504 0.997 0.999
## 2 4220 0 0.998 0.000504 0.997 0.999
## 3 3387 0 0.998 0.000504 0.997 0.999
## 4 2971 0 0.998 0.000504 0.997 0.999
## 7 2289 0 0.998 0.000504 0.997 0.999
## 13 1667 0 0.998 0.000504 0.997 0.999
## 25 971 0 0.998 0.000504 0.997 0.999
## 37 551 0 0.998 0.000504 0.997 0.999
## 49 274 0 0.998 0.000504 0.997 0.999
## 61 116 0 0.998 0.000504 0.997 0.999
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 7 0.999 0.000416 0.998 1
## 2 2962 0 0.999 0.000416 0.998 1
## 3 2386 0 0.999 0.000416 0.998 1
## 4 2122 0 0.999 0.000416 0.998 1
## 7 1686 0 0.999 0.000416 0.998 1
## 13 1226 0 0.999 0.000416 0.998 1
## 25 745 0 0.999 0.000416 0.998 1
## 37 442 0 0.999 0.000416 0.998 1
## 49 229 0 0.999 0.000416 0.998 1
## 61 110 0 0.999 0.000416 0.998 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -0.605 0.546 0.453 -1.34 0.18
##
## Likelihood ratio test=1.91 on 1 df, p=0.167
## n= 14291, number of events= 23
RMSD : S13.14 Sthaanabhedena Graha - Marma Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 2 1 0.000178 0.999 1
## 2 4226 0 1 0.000178 0.999 1
## 3 3393 0 1 0.000178 0.999 1
## 4 2977 0 1 0.000178 0.999 1
## 7 2295 0 1 0.000178 0.999 1
## 13 1672 0 1 0.000178 0.999 1
## 25 973 0 1 0.000178 0.999 1
## 37 554 0 1 0.000178 0.999 1
## 49 277 0 1 0.000178 0.999 1
## 61 119 0 1 0.000178 0.999 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 1 1 0.000157 1 1
## 2 2963 0 1 0.000157 1 1
## 3 2387 0 1 0.000157 1 1
## 4 2123 0 1 0.000157 1 1
## 7 1687 0 1 0.000157 1 1
## 13 1227 0 1 0.000157 1 1
## 25 746 0 1 0.000157 1 1
## 37 443 0 1 0.000157 1 1
## 49 229 0 1 0.000157 1 1
## 61 110 0 1 0.000157 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -0.471 0.624 1.225 -0.38 0.7
##
## Likelihood ratio test=0.15 on 1 df, p=0.694
## n= 14291, number of events= 3
RMSD : S13.17 Sthaanabhedena Graha - Paada Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 3 1 0.000218 0.999 1
## 2 4225 0 1 0.000218 0.999 1
## 3 3392 0 1 0.000218 0.999 1
## 4 2976 0 1 0.000218 0.999 1
## 7 2294 0 1 0.000218 0.999 1
## 13 1671 0 1 0.000218 0.999 1
## 25 972 0 1 0.000218 0.999 1
## 37 553 0 1 0.000218 0.999 1
## 49 276 0 1 0.000218 0.999 1
## 61 119 0 1 0.000218 0.999 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 0 1 0 1 1
## 2 2964 0 1 0 1 1
## 3 2388 0 1 0 1 1
## 4 2124 0 1 0 1 1
## 7 1688 0 1 0 1 1
## 13 1228 0 1 0 1 1
## 25 747 0 1 0 1 1
## 37 444 0 1 0 1 1
## 49 230 0 1 0 1 1
## 61 110 0 1 0 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -1.90e+01 5.60e-09 8.62e+03 0 1
##
## Likelihood ratio test=3.53 on 1 df, p=0.0603
## n= 14291, number of events= 3
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
RMSD : S13.18 Sthaanabhedena Graha - Paarshva Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 0 1 0 1 1
## 2 4227 0 1 0 1 1
## 3 3394 0 1 0 1 1
## 4 2978 0 1 0 1 1
## 7 2296 0 1 0 1 1
## 13 1673 0 1 0 1 1
## 25 974 0 1 0 1 1
## 37 554 0 1 0 1 1
## 49 277 0 1 0 1 1
## 61 119 0 1 0 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 2 1.000 0.000222 0.999 1
## 2 2963 1 0.999 0.000404 0.999 1
## 3 2386 0 0.999 0.000404 0.999 1
## 4 2122 0 0.999 0.000404 0.999 1
## 7 1686 0 0.999 0.000404 0.999 1
## 13 1227 0 0.999 0.000404 0.999 1
## 25 746 0 0.999 0.000404 0.999 1
## 37 444 0 0.999 0.000404 0.999 1
## 49 230 0 0.999 0.000404 0.999 1
## 61 110 0 0.999 0.000404 0.999 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 1.95e+01 2.95e+08 8.67e+03 0 1
##
## Likelihood ratio test=5.01 on 1 df, p=0.0251
## n= 14291, number of events= 3
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
RMSD : S13.19 Sthaanabhedena Graha - Prishtha Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 28 0.996 0.000666 0.995 0.998
## 2 4209 7 0.995 0.000913 0.993 0.997
## 3 3374 5 0.993 0.001125 0.991 0.996
## 4 2956 2 0.993 0.001220 0.990 0.995
## 7 2270 4 0.991 0.001445 0.988 0.994
## 13 1653 1 0.991 0.001527 0.988 0.994
## 25 962 2 0.989 0.001845 0.986 0.993
## 37 547 1 0.988 0.002296 0.983 0.992
## 49 272 0 0.988 0.002296 0.983 0.992
## 61 116 0 0.988 0.002296 0.983 0.992
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 56 0.991 0.00117 0.989 0.993
## 2 2936 4 0.990 0.00135 0.987 0.992
## 3 2361 1 0.989 0.00141 0.987 0.992
## 4 2098 1 0.989 0.00149 0.986 0.992
## 7 1662 4 0.987 0.00184 0.983 0.990
## 13 1206 2 0.985 0.00209 0.981 0.989
## 25 731 1 0.984 0.00249 0.979 0.989
## 37 433 2 0.981 0.00333 0.974 0.987
## 49 223 0 0.981 0.00333 0.974 0.987
## 61 105 0 0.981 0.00333 0.974 0.987
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 0.609 1.838 0.185 3.3 0.00098
##
## Likelihood ratio test=11.1 on 1 df, p=0.000873
## n= 14291, number of events= 121
RMSD : S13.20 Sthaanabhedena Graha - Shiro Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 0 1 0 1 1
## 2 4227 0 1 0 1 1
## 3 3394 0 1 0 1 1
## 4 2978 0 1 0 1 1
## 7 2296 0 1 0 1 1
## 13 1673 0 1 0 1 1
## 25 974 0 1 0 1 1
## 37 554 0 1 0 1 1
## 49 277 0 1 0 1 1
## 61 119 0 1 0 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 1 1 0.000157 1 1
## 2 2963 0 1 0.000157 1 1
## 3 2387 0 1 0.000157 1 1
## 4 2123 0 1 0.000157 1 1
## 7 1687 0 1 0.000157 1 1
## 13 1227 0 1 0.000157 1 1
## 25 746 0 1 0.000157 1 1
## 37 443 0 1 0.000157 1 1
## 49 229 0 1 0.000157 1 1
## 61 109 0 1 0.000157 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 1.94e+01 2.79e+08 1.49e+04 0 1
##
## Likelihood ratio test=1.62 on 1 df, p=0.203
## n= 14291, number of events= 1
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
RMSD : S13.22 Sthaanabhedena Graha - Uro Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 0 1 0 1 1
## 2 4227 0 1 0 1 1
## 3 3394 0 1 0 1 1
## 4 2978 0 1 0 1 1
## 7 2296 0 1 0 1 1
## 13 1673 0 1 0 1 1
## 25 974 0 1 0 1 1
## 37 554 0 1 0 1 1
## 49 277 0 1 0 1 1
## 61 119 0 1 0 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 1 1 0.000157 1 1
## 2 2963 0 1 0.000157 1 1
## 3 2387 0 1 0.000157 1 1
## 4 2123 0 1 0.000157 1 1
## 7 1688 0 1 0.000157 1 1
## 13 1228 0 1 0.000157 1 1
## 25 747 0 1 0.000157 1 1
## 37 444 0 1 0.000157 1 1
## 49 230 0 1 0.000157 1 1
## 61 110 0 1 0.000157 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 1.94e+01 2.79e+08 1.49e+04 0 1
##
## Likelihood ratio test=1.62 on 1 df, p=0.203
## n= 14291, number of events= 1
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
RMSD : S13.23 Sthaanabhedena Graha - Vaak Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 1 1.000 0.000126 1.000 1
## 2 4227 0 1.000 0.000126 1.000 1
## 3 3394 0 1.000 0.000126 1.000 1
## 4 2978 0 1.000 0.000126 1.000 1
## 7 2296 0 1.000 0.000126 1.000 1
## 13 1672 2 0.999 0.000741 0.997 1
## 25 974 0 0.999 0.000741 0.997 1
## 37 554 0 0.999 0.000741 0.997 1
## 49 277 0 0.999 0.000741 0.997 1
## 61 119 0 0.999 0.000741 0.997 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 5 0.999 0.000352 0.999 1.000
## 2 2963 0 0.999 0.000352 0.999 1.000
## 3 2387 0 0.999 0.000352 0.999 1.000
## 4 2123 0 0.999 0.000352 0.999 1.000
## 7 1686 2 0.998 0.000837 0.996 1.000
## 13 1227 1 0.997 0.001098 0.995 1.000
## 25 746 1 0.996 0.001729 0.993 0.999
## 37 443 0 0.996 0.001729 0.993 0.999
## 49 229 0 0.996 0.001729 0.993 0.999
## 61 110 0 0.996 0.001729 0.993 0.999
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 1.367 3.925 0.667 2.05 0.04
##
## Likelihood ratio test=4.97 on 1 df, p=0.0259
## n= 14291, number of events= 12
RMSD : S13.3 Sthaanabhedena Graha - Gala Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 3 1 0.000218 0.999 1
## 2 4227 0 1 0.000218 0.999 1
## 3 3394 0 1 0.000218 0.999 1
## 4 2978 0 1 0.000218 0.999 1
## 7 2296 0 1 0.000218 0.999 1
## 13 1673 0 1 0.000218 0.999 1
## 25 974 0 1 0.000218 0.999 1
## 37 554 0 1 0.000218 0.999 1
## 49 277 0 1 0.000218 0.999 1
## 61 119 0 1 0.000218 0.999 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 1 1 0.000157 1 1
## 2 2964 0 1 0.000157 1 1
## 3 2388 0 1 0.000157 1 1
## 4 2124 0 1 0.000157 1 1
## 7 1688 0 1 0.000157 1 1
## 13 1228 0 1 0.000157 1 1
## 25 747 0 1 0.000157 1 1
## 37 444 0 1 0.000157 1 1
## 49 230 0 1 0.000157 1 1
## 61 110 0 1 0.000157 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -0.877 0.416 1.155 -0.76 0.45
##
## Likelihood ratio test=0.65 on 1 df, p=0.419
## n= 14291, number of events= 4
RMSD : S13.5 Sthaanabhedena Graha - Hanu Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 1 1 0.000126 1 1
## 2 4227 0 1 0.000126 1 1
## 3 3394 0 1 0.000126 1 1
## 4 2978 0 1 0.000126 1 1
## 7 2296 0 1 0.000126 1 1
## 13 1673 0 1 0.000126 1 1
## 25 974 0 1 0.000126 1 1
## 37 554 0 1 0.000126 1 1
## 49 277 0 1 0.000126 1 1
## 61 119 0 1 0.000126 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 0 1 0 1 1
## 2 2964 0 1 0 1 1
## 3 2388 0 1 0 1 1
## 4 2124 0 1 0 1 1
## 7 1688 0 1 0 1 1
## 13 1228 0 1 0 1 1
## 25 747 0 1 0 1 1
## 37 444 0 1 0 1 1
## 49 230 0 1 0 1 1
## 61 110 0 1 0 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -1.90e+01 5.61e-09 1.49e+04 0 1
##
## Likelihood ratio test=1.18 on 1 df, p=0.278
## n= 14291, number of events= 1
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
RMSD : S13.6 Sthaanabhedena Graha - Hrid Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 0 1 0 1 1
## 2 4227 0 1 0 1 1
## 3 3394 0 1 0 1 1
## 4 2978 0 1 0 1 1
## 7 2296 0 1 0 1 1
## 13 1673 0 1 0 1 1
## 25 974 0 1 0 1 1
## 37 554 0 1 0 1 1
## 49 277 0 1 0 1 1
## 61 119 0 1 0 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 1 1 0.000157 1 1
## 2 2963 0 1 0.000157 1 1
## 3 2387 0 1 0.000157 1 1
## 4 2123 0 1 0.000157 1 1
## 7 1687 0 1 0.000157 1 1
## 13 1228 0 1 0.000157 1 1
## 25 747 0 1 0.000157 1 1
## 37 444 0 1 0.000157 1 1
## 49 230 0 1 0.000157 1 1
## 61 110 0 1 0.000157 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 1.94e+01 2.79e+08 1.49e+04 0 1
##
## Likelihood ratio test=1.62 on 1 df, p=0.203
## n= 14291, number of events= 1
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
RMSD : S13.7 Sthaanabhedena Graha - Jaanugraha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 4 0.999 0.000252 0.999 1
## 2 4223 0 0.999 0.000252 0.999 1
## 3 3391 0 0.999 0.000252 0.999 1
## 4 2975 0 0.999 0.000252 0.999 1
## 7 2293 0 0.999 0.000252 0.999 1
## 13 1671 0 0.999 0.000252 0.999 1
## 25 972 0 0.999 0.000252 0.999 1
## 37 553 0 0.999 0.000252 0.999 1
## 49 277 0 0.999 0.000252 0.999 1
## 61 119 0 0.999 0.000252 0.999 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 1 1 0.000157 1 1
## 2 2963 0 1 0.000157 1 1
## 3 2388 0 1 0.000157 1 1
## 4 2124 0 1 0.000157 1 1
## 7 1688 0 1 0.000157 1 1
## 13 1228 0 1 0.000157 1 1
## 25 747 0 1 0.000157 1 1
## 37 444 0 1 0.000157 1 1
## 49 230 0 1 0.000157 1 1
## 61 110 0 1 0.000157 1 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM -1.165 0.312 1.118 -1.04 0.3
##
## Likelihood ratio test=1.32 on 1 df, p=0.25
## n= 14291, number of events= 5
RMSD : S13.8 Sthaanabhedena Graha - Janghaa Graha
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, :
## Median survival not reached.
## Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
## Loglik converged before variable 1 ; beta may be infinite.
Kaplan Meier table
## Call: survfit(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## patient_gender=F
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 7935 0 1 0 1 1
## 2 4227 0 1 0 1 1
## 3 3394 0 1 0 1 1
## 4 2978 0 1 0 1 1
## 7 2296 0 1 0 1 1
## 13 1673 0 1 0 1 1
## 25 974 0 1 0 1 1
## 37 554 0 1 0 1 1
## 49 277 0 1 0 1 1
## 61 119 0 1 0 1 1
##
## patient_gender=M
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 1 6356 2 1 0.000222 0.999 1
## 2 2963 0 1 0.000222 0.999 1
## 3 2387 0 1 0.000222 0.999 1
## 4 2123 0 1 0.000222 0.999 1
## 7 1687 0 1 0.000222 0.999 1
## 13 1227 0 1 0.000222 0.999 1
## 25 747 0 1 0.000222 0.999 1
## 37 444 0 1 0.000222 0.999 1
## 49 230 0 1 0.000222 0.999 1
## 61 110 0 1 0.000222 0.999 1
Survival plot
Hazard ratio plot
## Call:
## coxph(formula = Surv(disdur, status) ~ patient_gender, data = tmp)
##
## coef exp(coef) se(coef) z p
## patient_genderM 1.94e+01 2.79e+08 1.06e+04 0 1
##
## Likelihood ratio test=3.24 on 1 df, p=0.0718
## n= 14291, number of events= 2
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_text).