WAKA RESEARCH PROJECT ANALYSIS
WAKA OLIVIA
2024-07-02
STATISTICAL MODELLING OF STAFF SURVIVAL TIME IN SERVICE AT CHUKA UNIVERSITY
IMPORTING DATA
Mydata <- read.csv("C:/Users/user/Desktop/RESEARCH PROJECT WAKA ANALYSIS/staff_survival.data2.csv")
View(Mydata)
attach(Mydata)Display the first few rows of the dataset
head(Mydata)## Serial_No Gender Age_Bracket Marital_Status Terms_of_Employment Job_Group
## 1 1 Male 36-50 Single Permanent 10
## 2 2 Female 26-35 Single Contract 7
## 3 3 Male 36-50 Married Permanent 1
## 4 4 Female 36-50 Married Contract 11
## 5 5 Female 36-50 Married Contract 2
## 6 6 Male 51-75 Divorced/Separated Permanent 11
## Employment_Status Level_of_Education Academic_Designation
## 1 0 Degree Non-teaching staff
## 2 0 Diploma Non-teaching staff
## 3 0 Certificate Non-teaching staff
## 4 0 Ph.D Teaching staff
## 5 0 Certificate Non-teaching staff
## 6 1 Ph.D Teaching staff
## Employment_Start_Date Employment_End_Date Exit_Type time
## 1 12/2/2021 <NA> <NA> 759
## 2 5/15/2023 <NA> <NA> 230
## 3 5/31/2021 <NA> <NA> 944
## 4 2/23/2013 <NA> <NA> 3963
## 5 5/13/2016 <NA> <NA> 2788
## 6 7/6/2014 10/13/2021 Resignation 2656Display the last few rows of the dataset
tail(Mydata)## Serial_No Gender Age_Bracket Marital_Status Terms_of_Employment
## 595 595 Male 36-50 Married Contract
## 596 596 Female 36-50 Married Contract
## 597 597 Male 51-75 Married Permanent
## 598 598 Male 36-50 Divorced/Separated Permanent
## 599 599 Female 18-25 Married Contract
## 600 600 Male 36-50 Married Contract
## Job_Group Employment_Status Level_of_Education Academic_Designation
## 595 8 1 Diploma Non-teaching staff
## 596 3 0 Certificate Non-teaching staff
## 597 1 0 Certificate Non-teaching staff
## 598 13 0 Masters Teaching staff
## 599 12 0 Ph.D Teaching staff
## 600 12 0 Ph.D Teaching staff
## Employment_Start_Date Employment_End_Date Exit_Type time
## 595 10/28/2017 5/10/2020 Resignation 925
## 596 6/17/2021 <NA> <NA> 927
## 597 11/12/2021 <NA> <NA> 779
## 598 3/17/2022 <NA> <NA> 654
## 599 8/19/2019 <NA> <NA> 1595
## 600 3/19/2019 <NA> <NA> 1748Set up the script to remove scientific notation
Use the command below to ensure that the output values are not written in scientific notation.
options(scipen=999)Activate the necessary packages
library(survival)
library(ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ✔ purrr 1.0.2 ✔ tidyr 1.3.1
## ✔ readr 2.1.5## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(survminer)## Loading required package: ggpubr
##
## Attaching package: 'survminer'
##
## The following object is masked from 'package:survival':
##
## myelomalibrary(magrittr)##
## Attaching package: 'magrittr'
##
## The following object is masked from 'package:purrr':
##
## set_names
##
## The following object is masked from 'package:tidyr':
##
## extractlibrary(ggpubr)
library(stargazer)##
## Please cite as:
##
## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.3. https://CRAN.R-project.org/package=stargazerlibrary(ggfortify)
library(simsurv)Read variable name
names(Mydata)## [1] "Serial_No" "Gender" "Age_Bracket"
## [4] "Marital_Status" "Terms_of_Employment" "Job_Group"
## [7] "Employment_Status" "Level_of_Education" "Academic_Designation"
## [10] "Employment_Start_Date" "Employment_End_Date" "Exit_Type"
## [13] "time"####Exploratory and Non parametric statistics
hist (Mydata$time, xlab="survival time", ylab="No. of staff", main="Distribution of the survival
times of staff at Chuka University")
KM CURVES
#1.Survival distribution of Staff classified by gender
KM1<-survfit (Surv (time, Employment_Status) ~ Gender, data = Mydata)Summaries of the model
KM1## Call: survfit(formula = Surv(time, Employment_Status) ~ Gender, data = Mydata)
##
## n events median 0.95LCL 0.95UCL
## Gender=Female 234 62 NA 3183 NA
## Gender=Male 366 107 NA 3262 NAggsurvplot(KM1,
conf.int = TRUE, # Show confidence intervals
pval = TRUE, # Show p-value
risk.table = TRUE, # Show risk table
ggtheme = theme_minimal())
Model summary
Summary1<-summary(KM1)
Summary1## Call: survfit(formula = Surv(time, Employment_Status) ~ Gender, data = Mydata)
##
## Gender=Female
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 89 228 1 0.996 0.00438 0.987 1.000
## 117 225 1 0.991 0.00620 0.979 1.000
## 122 224 1 0.987 0.00759 0.972 1.000
## 123 223 1 0.982 0.00875 0.965 1.000
## 182 219 1 0.978 0.00979 0.959 0.997
## 238 216 1 0.973 0.01074 0.952 0.995
## 262 214 1 0.969 0.01162 0.946 0.992
## 283 210 1 0.964 0.01244 0.940 0.989
## 286 209 1 0.960 0.01321 0.934 0.986
## 293 208 1 0.955 0.01393 0.928 0.983
## 367 206 1 0.950 0.01461 0.922 0.979
## 397 205 1 0.946 0.01526 0.916 0.976
## 426 202 1 0.941 0.01589 0.910 0.973
## 470 197 1 0.936 0.01651 0.904 0.969
## 492 195 1 0.931 0.01711 0.898 0.966
## 512 194 1 0.927 0.01768 0.893 0.962
## 568 193 1 0.922 0.01823 0.887 0.958
## 704 188 1 0.917 0.01878 0.881 0.954
## 714 186 1 0.912 0.01932 0.875 0.951
## 772 182 1 0.907 0.01985 0.869 0.947
## 850 179 1 0.902 0.02037 0.863 0.943
## 939 175 1 0.897 0.02090 0.857 0.939
## 969 173 1 0.892 0.02141 0.851 0.935
## 1015 171 1 0.886 0.02191 0.844 0.930
## 1072 167 1 0.881 0.02241 0.838 0.926
## 1081 166 1 0.876 0.02290 0.832 0.922
## 1150 162 1 0.870 0.02339 0.826 0.917
## 1163 161 1 0.865 0.02386 0.819 0.913
## 1192 159 1 0.859 0.02432 0.813 0.908
## 1205 158 1 0.854 0.02477 0.807 0.904
## 1274 150 1 0.848 0.02525 0.800 0.899
## 1330 147 1 0.843 0.02573 0.794 0.895
## 1360 144 1 0.837 0.02621 0.787 0.890
## 1511 133 1 0.830 0.02675 0.780 0.885
## 1590 126 1 0.824 0.02734 0.772 0.879
## 1681 120 1 0.817 0.02796 0.764 0.874
## 1818 112 1 0.810 0.02865 0.755 0.868
## 1820 111 1 0.802 0.02930 0.747 0.862
## 1879 108 1 0.795 0.02996 0.738 0.856
## 1958 107 1 0.788 0.03059 0.730 0.850
## 1992 104 1 0.780 0.03122 0.721 0.844
## 2017 103 1 0.772 0.03182 0.712 0.837
## 2052 100 1 0.765 0.03242 0.704 0.831
## 2054 99 1 0.757 0.03300 0.695 0.824
## 2080 98 1 0.749 0.03356 0.686 0.818
## 2090 96 1 0.741 0.03410 0.677 0.811
## 2109 94 1 0.734 0.03464 0.669 0.805
## 2134 93 1 0.726 0.03516 0.660 0.798
## 2147 92 1 0.718 0.03565 0.651 0.791
## 2267 89 1 0.710 0.03615 0.642 0.784
## 2403 77 1 0.700 0.03683 0.632 0.777
## 2416 76 1 0.691 0.03748 0.622 0.769
## 2470 73 1 0.682 0.03815 0.611 0.761
## 2552 69 1 0.672 0.03885 0.600 0.753
## 2657 63 1 0.661 0.03967 0.588 0.744
## 2751 58 1 0.650 0.04059 0.575 0.734
## 2929 49 1 0.637 0.04188 0.560 0.724
## 3079 44 1 0.622 0.04335 0.543 0.713
## 3094 42 1 0.607 0.04478 0.526 0.702
## 3178 38 1 0.591 0.04636 0.507 0.690
## 3183 37 1 0.575 0.04779 0.489 0.677
## 3226 34 1 0.558 0.04929 0.470 0.664
##
## Gender=Male
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 31 365 1 0.997 0.00274 0.992 1.000
## 41 364 1 0.995 0.00386 0.987 1.000
## 66 361 1 0.992 0.00473 0.983 1.000
## 70 360 1 0.989 0.00546 0.978 1.000
## 108 358 1 0.986 0.00611 0.974 0.998
## 115 356 1 0.983 0.00669 0.970 0.997
## 131 354 1 0.981 0.00722 0.967 0.995
## 203 350 1 0.978 0.00773 0.963 0.993
## 212 348 1 0.975 0.00820 0.959 0.991
## 217 347 1 0.972 0.00865 0.955 0.989
## 232 343 1 0.969 0.00907 0.952 0.987
## 255 341 2 0.964 0.00987 0.945 0.983
## 257 339 1 0.961 0.01024 0.941 0.981
## 274 337 1 0.958 0.01060 0.938 0.979
## 300 335 1 0.955 0.01095 0.934 0.977
## 304 334 1 0.952 0.01128 0.930 0.975
## 321 330 1 0.949 0.01161 0.927 0.972
## 351 326 1 0.947 0.01194 0.923 0.970
## 361 324 1 0.944 0.01225 0.920 0.968
## 371 322 1 0.941 0.01256 0.916 0.966
## 372 321 1 0.938 0.01286 0.913 0.963
## 376 319 1 0.935 0.01315 0.909 0.961
## 395 316 1 0.932 0.01344 0.906 0.959
## 399 315 1 0.929 0.01372 0.902 0.956
## 466 312 1 0.926 0.01399 0.899 0.954
## 482 310 1 0.923 0.01426 0.895 0.951
## 493 309 1 0.920 0.01452 0.892 0.949
## 527 308 1 0.917 0.01478 0.888 0.946
## 536 306 1 0.914 0.01503 0.885 0.944
## 540 305 1 0.911 0.01528 0.882 0.941
## 575 302 1 0.908 0.01552 0.878 0.939
## 582 300 1 0.905 0.01576 0.875 0.936
## 613 299 1 0.902 0.01600 0.871 0.934
## 674 292 1 0.899 0.01624 0.868 0.931
## 691 287 1 0.896 0.01648 0.864 0.929
## 700 286 1 0.893 0.01672 0.860 0.926
## 720 283 1 0.889 0.01696 0.857 0.923
## 737 279 1 0.886 0.01719 0.853 0.921
## 753 278 1 0.883 0.01742 0.850 0.918
## 798 272 1 0.880 0.01766 0.846 0.915
## 803 271 1 0.877 0.01789 0.842 0.912
## 821 270 1 0.873 0.01812 0.838 0.910
## 839 268 1 0.870 0.01834 0.835 0.907
## 857 265 1 0.867 0.01856 0.831 0.904
## 895 260 1 0.863 0.01879 0.827 0.901
## 914 259 1 0.860 0.01901 0.824 0.898
## 925 256 1 0.857 0.01923 0.820 0.895
## 935 254 1 0.853 0.01945 0.816 0.892
## 944 252 1 0.850 0.01966 0.812 0.889
## 998 244 1 0.846 0.01989 0.808 0.886
## 1113 235 1 0.843 0.02013 0.804 0.883
## 1120 234 1 0.839 0.02036 0.800 0.880
## 1161 230 1 0.836 0.02060 0.796 0.877
## 1165 228 1 0.832 0.02083 0.792 0.874
## 1166 227 1 0.828 0.02106 0.788 0.871
## 1184 224 1 0.825 0.02129 0.784 0.867
## 1198 222 1 0.821 0.02151 0.780 0.864
## 1201 221 1 0.817 0.02173 0.776 0.861
## 1225 215 1 0.813 0.02196 0.771 0.858
## 1264 213 1 0.810 0.02219 0.767 0.854
## 1271 212 1 0.806 0.02241 0.763 0.851
## 1308 209 1 0.802 0.02263 0.759 0.847
## 1353 205 1 0.798 0.02286 0.754 0.844
## 1356 204 1 0.794 0.02308 0.750 0.841
## 1393 200 1 0.790 0.02330 0.746 0.837
## 1411 199 1 0.786 0.02352 0.741 0.834
## 1429 198 1 0.782 0.02373 0.737 0.830
## 1444 195 1 0.778 0.02395 0.733 0.827
## 1490 192 1 0.774 0.02416 0.728 0.823
## 1591 186 1 0.770 0.02439 0.724 0.819
## 1662 179 1 0.766 0.02463 0.719 0.815
## 1689 177 1 0.761 0.02487 0.714 0.812
## 1697 176 1 0.757 0.02510 0.709 0.808
## 1699 175 1 0.753 0.02533 0.705 0.804
## 1723 173 1 0.748 0.02555 0.700 0.800
## 1725 172 1 0.744 0.02577 0.695 0.796
## 1811 166 1 0.739 0.02600 0.690 0.792
## 2054 154 1 0.735 0.02627 0.685 0.788
## 2186 143 1 0.730 0.02659 0.679 0.784
## 2267 138 1 0.724 0.02691 0.673 0.779
## 2317 134 2 0.713 0.02758 0.661 0.770
## 2358 129 1 0.708 0.02791 0.655 0.765
## 2434 121 1 0.702 0.02829 0.649 0.760
## 2453 120 1 0.696 0.02865 0.642 0.755
## 2460 119 1 0.690 0.02900 0.636 0.750
## 2462 118 1 0.684 0.02934 0.629 0.744
## 2489 117 1 0.679 0.02967 0.623 0.739
## 2523 116 1 0.673 0.02998 0.617 0.734
## 2581 110 1 0.667 0.03033 0.610 0.729
## 2595 109 1 0.661 0.03066 0.603 0.723
## 2653 105 1 0.654 0.03100 0.596 0.718
## 2656 104 1 0.648 0.03134 0.589 0.712
## 2759 100 1 0.641 0.03169 0.582 0.707
## 2850 94 1 0.635 0.03208 0.575 0.701
## 2893 89 1 0.628 0.03250 0.567 0.695
## 2918 85 1 0.620 0.03295 0.559 0.688
## 3001 76 1 0.612 0.03351 0.550 0.681
## 3007 75 1 0.604 0.03404 0.541 0.674
## 3029 73 1 0.596 0.03456 0.532 0.667
## 3069 70 1 0.587 0.03510 0.522 0.660
## 3136 64 1 0.578 0.03573 0.512 0.652
## 3262 52 1 0.567 0.03673 0.499 0.644
## 3271 51 1 0.556 0.03765 0.487 0.635
## 3344 45 1 0.543 0.03879 0.472 0.625
## 3566 30 1 0.525 0.04151 0.450 0.613###Log Rank test: Comparison of survival curves ## h0:There is no difference in survival experience across the Gender
log_rank_test <- survdiff(Surv(time, Employment_Status) ~ Gender, data = Mydata)log_rank_test## Call:
## survdiff(formula = Surv(time, Employment_Status) ~ Gender, data = Mydata)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## Gender=Female 234 62 66.5 0.311 0.514
## Gender=Male 366 107 102.5 0.202 0.514
##
## Chisq= 0.5 on 1 degrees of freedom, p= 0.5##PLOT THE CURVE
plot (KM1,ylab = "Survival Probability", xlab = "Time
(Days)", main= "survival probabilities as per the Gender", col=c (2, 4))
legend("topright",c("F","M"),col=c(2,4),lty=c(1,2),ncol=2,cex=0.6)
#1.Survival distribution of Staff classified by age
KM2<-survfit (Surv (time, Employment_Status) ~ Age_Bracket, data = Mydata)##Summary of the model
KM2## Call: survfit(formula = Surv(time, Employment_Status) ~ Age_Bracket,
## data = Mydata)
##
## n events median 0.95LCL 0.95UCL
## Age_Bracket=18-25 47 17 3007 2453 NA
## Age_Bracket=26-35 126 39 3344 3029 NA
## Age_Bracket=36-50 299 72 NA NA NA
## Age_Bracket=51-75 128 41 NA 2751 NA##Model Summary
Summary2<-summary(KM2)
Summary2## Call: survfit(formula = Surv(time, Employment_Status) ~ Age_Bracket,
## data = Mydata)
##
## Age_Bracket=18-25
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 41 47 1 0.979 0.0210 0.938 1.000
## 66 46 1 0.957 0.0294 0.901 1.000
## 262 44 1 0.936 0.0359 0.868 1.000
## 470 42 1 0.913 0.0414 0.836 0.998
## 493 41 1 0.891 0.0460 0.805 0.986
## 527 40 1 0.869 0.0500 0.776 0.972
## 772 38 1 0.846 0.0536 0.747 0.958
## 850 36 1 0.822 0.0570 0.718 0.942
## 857 35 1 0.799 0.0601 0.690 0.926
## 998 34 1 0.775 0.0627 0.662 0.909
## 1725 21 1 0.739 0.0698 0.614 0.889
## 2017 17 1 0.695 0.0780 0.558 0.866
## 2267 16 1 0.652 0.0844 0.506 0.840
## 2453 14 1 0.605 0.0903 0.452 0.811
## 2552 13 1 0.559 0.0946 0.401 0.778
## 3007 7 1 0.479 0.1097 0.306 0.750
## 3094 5 1 0.383 0.1226 0.205 0.717
##
## Age_Bracket=26-35
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 70 125 1 0.992 0.00797 0.977 1.000
## 89 124 1 0.984 0.01122 0.962 1.000
## 117 122 1 0.976 0.01373 0.949 1.000
## 122 121 1 0.968 0.01581 0.937 0.999
## 131 120 1 0.960 0.01761 0.926 0.995
## 203 119 1 0.952 0.01922 0.915 0.990
## 212 118 1 0.944 0.02068 0.904 0.985
## 255 115 1 0.935 0.02207 0.893 0.980
## 257 114 1 0.927 0.02335 0.883 0.974
## 274 113 1 0.919 0.02455 0.872 0.968
## 351 110 1 0.911 0.02571 0.862 0.963
## 361 108 1 0.902 0.02681 0.851 0.956
## 367 107 1 0.894 0.02786 0.841 0.950
## 371 106 1 0.885 0.02884 0.831 0.944
## 395 104 1 0.877 0.02980 0.820 0.937
## 613 99 1 0.868 0.03078 0.810 0.931
## 939 90 1 0.858 0.03192 0.798 0.923
## 1120 87 1 0.849 0.03304 0.786 0.916
## 1150 85 1 0.839 0.03413 0.774 0.908
## 1264 82 1 0.828 0.03521 0.762 0.900
## 1274 81 1 0.818 0.03623 0.750 0.892
## 1360 79 1 0.808 0.03722 0.738 0.884
## 1444 74 1 0.797 0.03828 0.725 0.875
## 1490 73 1 0.786 0.03929 0.713 0.867
## 1511 71 1 0.775 0.04026 0.700 0.858
## 1689 63 1 0.763 0.04146 0.685 0.848
## 1699 62 1 0.750 0.04257 0.671 0.838
## 1723 61 1 0.738 0.04362 0.657 0.829
## 1811 59 1 0.725 0.04463 0.643 0.818
## 1818 58 1 0.713 0.04558 0.629 0.808
## 2109 49 1 0.698 0.04692 0.612 0.797
## 2434 36 1 0.679 0.04946 0.589 0.783
## 2759 32 1 0.658 0.05227 0.563 0.769
## 2918 29 1 0.635 0.05517 0.536 0.753
## 3001 28 1 0.612 0.05767 0.509 0.737
## 3029 27 1 0.590 0.05983 0.483 0.719
## 3079 24 1 0.565 0.06218 0.456 0.701
## 3226 17 1 0.532 0.06682 0.416 0.680
## 3344 14 1 0.494 0.07204 0.371 0.657
##
## Age_Bracket=36-50
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 108 291 1 0.997 0.00343 0.990 1.000
## 115 288 1 0.993 0.00486 0.984 1.000
## 182 282 1 0.990 0.00598 0.978 1.000
## 217 279 1 0.986 0.00693 0.973 1.000
## 232 277 1 0.982 0.00777 0.967 0.998
## 238 276 1 0.979 0.00852 0.962 0.996
## 286 269 1 0.975 0.00923 0.957 0.994
## 300 268 1 0.972 0.00989 0.952 0.991
## 321 264 1 0.968 0.01051 0.948 0.989
## 372 261 1 0.964 0.01111 0.943 0.986
## 426 256 1 0.960 0.01169 0.938 0.984
## 466 255 1 0.957 0.01223 0.933 0.981
## 482 252 1 0.953 0.01276 0.928 0.978
## 492 250 1 0.949 0.01326 0.923 0.975
## 536 248 1 0.945 0.01375 0.919 0.973
## 568 246 1 0.941 0.01422 0.914 0.970
## 674 241 1 0.938 0.01469 0.909 0.967
## 691 237 1 0.934 0.01515 0.904 0.964
## 700 235 1 0.930 0.01560 0.900 0.961
## 704 234 1 0.926 0.01603 0.895 0.958
## 714 232 1 0.922 0.01645 0.890 0.954
## 737 229 1 0.918 0.01686 0.885 0.951
## 753 228 1 0.914 0.01726 0.880 0.948
## 798 224 1 0.910 0.01766 0.876 0.945
## 803 223 1 0.905 0.01805 0.871 0.942
## 914 217 1 0.901 0.01844 0.866 0.938
## 925 214 1 0.897 0.01883 0.861 0.935
## 935 212 1 0.893 0.01921 0.856 0.931
## 944 211 1 0.889 0.01958 0.851 0.928
## 969 207 1 0.884 0.01995 0.846 0.924
## 1015 202 1 0.880 0.02033 0.841 0.921
## 1072 196 1 0.875 0.02071 0.836 0.917
## 1161 191 1 0.871 0.02110 0.830 0.913
## 1163 190 1 0.866 0.02149 0.825 0.909
## 1165 189 1 0.862 0.02185 0.820 0.906
## 1166 188 1 0.857 0.02221 0.815 0.902
## 1184 187 1 0.853 0.02256 0.809 0.898
## 1192 185 1 0.848 0.02291 0.804 0.894
## 1198 184 1 0.843 0.02324 0.799 0.890
## 1271 173 1 0.838 0.02361 0.793 0.886
## 1308 169 1 0.833 0.02399 0.788 0.882
## 1330 167 1 0.828 0.02436 0.782 0.878
## 1393 162 1 0.823 0.02474 0.776 0.873
## 1411 161 1 0.818 0.02511 0.770 0.869
## 1429 159 1 0.813 0.02547 0.765 0.865
## 1681 147 1 0.808 0.02589 0.758 0.860
## 1820 138 1 0.802 0.02636 0.752 0.855
## 1992 132 1 0.796 0.02685 0.745 0.850
## 2054 130 1 0.790 0.02733 0.738 0.845
## 2080 127 1 0.783 0.02781 0.731 0.840
## 2090 125 1 0.777 0.02829 0.724 0.835
## 2134 123 1 0.771 0.02876 0.716 0.829
## 2147 122 1 0.764 0.02921 0.709 0.824
## 2186 119 1 0.758 0.02966 0.702 0.818
## 2267 116 1 0.751 0.03011 0.695 0.813
## 2317 115 2 0.738 0.03098 0.680 0.802
## 2358 109 1 0.732 0.03142 0.673 0.796
## 2403 105 1 0.725 0.03189 0.665 0.790
## 2460 104 1 0.718 0.03233 0.657 0.784
## 2470 103 1 0.711 0.03276 0.649 0.778
## 2489 102 1 0.704 0.03317 0.642 0.772
## 2581 95 1 0.696 0.03364 0.633 0.765
## 2595 92 1 0.689 0.03412 0.625 0.759
## 2657 88 1 0.681 0.03461 0.616 0.752
## 2850 75 1 0.672 0.03532 0.606 0.745
## 2893 71 1 0.662 0.03607 0.595 0.737
## 3069 58 1 0.651 0.03721 0.582 0.728
## 3178 52 1 0.638 0.03855 0.567 0.719
## 3262 47 1 0.625 0.04005 0.551 0.708
## 3271 45 1 0.611 0.04149 0.535 0.698
## 3566 25 1 0.587 0.04648 0.502 0.685
##
## Age_Bracket=51-75
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 31 128 1 0.992 0.00778 0.977 1.000
## 123 126 1 0.984 0.01101 0.963 1.000
## 255 123 1 0.976 0.01352 0.950 1.000
## 283 122 1 0.968 0.01560 0.938 0.999
## 293 121 1 0.960 0.01740 0.927 0.995
## 304 120 1 0.952 0.01901 0.916 0.990
## 376 118 1 0.944 0.02049 0.905 0.985
## 397 117 1 0.936 0.02184 0.894 0.980
## 399 116 1 0.928 0.02310 0.884 0.974
## 512 112 1 0.920 0.02433 0.873 0.969
## 540 111 1 0.912 0.02549 0.863 0.963
## 575 110 1 0.903 0.02657 0.853 0.957
## 582 108 1 0.895 0.02761 0.842 0.951
## 720 103 1 0.886 0.02867 0.832 0.944
## 821 99 1 0.877 0.02975 0.821 0.938
## 839 98 1 0.868 0.03076 0.810 0.931
## 895 96 1 0.859 0.03174 0.799 0.924
## 1081 90 1 0.850 0.03279 0.788 0.916
## 1113 89 1 0.840 0.03379 0.776 0.909
## 1201 85 1 0.830 0.03480 0.765 0.901
## 1205 84 1 0.820 0.03577 0.753 0.894
## 1225 83 1 0.810 0.03668 0.742 0.886
## 1353 80 1 0.800 0.03759 0.730 0.878
## 1356 79 1 0.790 0.03846 0.718 0.869
## 1590 71 1 0.779 0.03949 0.705 0.860
## 1591 70 1 0.768 0.04047 0.693 0.852
## 1662 67 1 0.757 0.04145 0.679 0.842
## 1697 66 1 0.745 0.04238 0.666 0.833
## 1879 61 1 0.733 0.04341 0.652 0.823
## 1958 60 1 0.721 0.04437 0.639 0.813
## 2052 56 1 0.708 0.04541 0.624 0.803
## 2054 55 1 0.695 0.04637 0.610 0.792
## 2416 44 1 0.679 0.04793 0.591 0.780
## 2462 41 1 0.663 0.04954 0.572 0.767
## 2523 39 1 0.646 0.05110 0.553 0.754
## 2653 37 1 0.628 0.05261 0.533 0.740
## 2656 36 1 0.611 0.05397 0.514 0.726
## 2751 34 1 0.593 0.05529 0.494 0.712
## 2929 30 1 0.573 0.05686 0.472 0.696
## 3136 25 1 0.550 0.05903 0.446 0.679
## 3183 23 1 0.526 0.06111 0.419 0.661log_rank_test2 <- survdiff(Surv(time, Employment_Status) ~ Age_Bracket, data = Mydata)
log_rank_test2## Call:
## survdiff(formula = Surv(time, Employment_Status) ~ Age_Bracket,
## data = Mydata)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## Age_Bracket=18-25 47 17 12.3 1.759 1.900
## Age_Bracket=26-35 126 39 34.8 0.513 0.647
## Age_Bracket=36-50 299 72 84.6 1.870 3.749
## Age_Bracket=51-75 128 41 37.3 0.366 0.470
##
## Chisq= 4.5 on 3 degrees of freedom, p= 0.2##Plot of the model
plot (KM2,ylab = "Survival Probability", xlab = "Time
(Days)", main= "survival probabilities as per the Age", col=c (2, 4,6,8))
legend("topright",c("18-25","26-35","36-50","51-75"),col=c(2,4,6,8),lty=c(1,2),ncol=4,cex=0.6)
#3.Survival distribution of Staff classified by terms of employment
KM3<-survfit (Surv (time, Employment_Status) ~ Terms_of_Employment, data = Mydata)##summaries of the model
KM3## Call: survfit(formula = Surv(time, Employment_Status) ~ Terms_of_Employment,
## data = Mydata)
##
## n events median 0.95LCL 0.95UCL
## Terms_of_Employment=Contract 236 65 NA NA NA
## Terms_of_Employment=Permanent 364 104 NA 3178 NA###Model Summary
Summary3<-summary(KM3)
Summary3## Call: survfit(formula = Surv(time, Employment_Status) ~ Terms_of_Employment,
## data = Mydata)
##
## Terms_of_Employment=Contract
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 41 233 1 0.996 0.00428 0.987 1.000
## 89 231 1 0.991 0.00606 0.980 1.000
## 108 230 1 0.987 0.00741 0.973 1.000
## 115 227 1 0.983 0.00856 0.966 1.000
## 131 225 1 0.978 0.00957 0.960 0.997
## 182 224 1 0.974 0.01047 0.954 0.995
## 212 222 1 0.970 0.01131 0.948 0.992
## 238 219 1 0.965 0.01209 0.942 0.989
## 255 217 1 0.961 0.01283 0.936 0.986
## 262 216 1 0.956 0.01352 0.930 0.983
## 274 215 1 0.952 0.01417 0.924 0.980
## 293 213 1 0.947 0.01479 0.919 0.977
## 300 212 1 0.943 0.01538 0.913 0.974
## 304 211 1 0.938 0.01594 0.908 0.970
## 351 207 1 0.934 0.01650 0.902 0.967
## 376 206 1 0.929 0.01703 0.897 0.963
## 399 205 1 0.925 0.01754 0.891 0.960
## 527 201 1 0.920 0.01805 0.886 0.956
## 536 200 1 0.916 0.01853 0.880 0.953
## 575 198 1 0.911 0.01901 0.875 0.949
## 582 197 1 0.906 0.01947 0.869 0.945
## 674 192 1 0.902 0.01993 0.863 0.942
## 720 187 1 0.897 0.02040 0.858 0.938
## 772 181 1 0.892 0.02088 0.852 0.934
## 798 179 1 0.887 0.02135 0.846 0.930
## 821 177 1 0.882 0.02181 0.840 0.926
## 857 174 1 0.877 0.02226 0.834 0.922
## 895 171 1 0.872 0.02272 0.828 0.917
## 925 169 1 0.867 0.02316 0.822 0.913
## 935 166 1 0.861 0.02360 0.816 0.909
## 998 161 1 0.856 0.02405 0.810 0.904
## 1113 157 1 0.851 0.02451 0.804 0.900
## 1161 153 1 0.845 0.02497 0.797 0.895
## 1192 152 1 0.839 0.02542 0.791 0.891
## 1198 151 1 0.834 0.02585 0.785 0.886
## 1201 150 1 0.828 0.02627 0.778 0.881
## 1225 147 1 0.823 0.02669 0.772 0.877
## 1264 146 1 0.817 0.02709 0.766 0.872
## 1330 143 1 0.811 0.02750 0.759 0.867
## 1360 138 1 0.805 0.02792 0.753 0.862
## 1393 134 1 0.799 0.02835 0.746 0.857
## 1444 131 1 0.793 0.02879 0.739 0.852
## 1511 128 1 0.787 0.02922 0.732 0.847
## 1591 121 1 0.781 0.02969 0.725 0.841
## 1697 112 1 0.774 0.03024 0.717 0.835
## 1699 111 1 0.767 0.03076 0.709 0.829
## 1723 110 1 0.760 0.03126 0.701 0.823
## 1725 109 1 0.753 0.03174 0.693 0.818
## 1958 105 1 0.746 0.03223 0.685 0.811
## 2054 103 1 0.738 0.03272 0.677 0.805
## 2080 101 1 0.731 0.03321 0.669 0.799
## 2090 99 1 0.724 0.03368 0.661 0.793
## 2186 93 1 0.716 0.03421 0.652 0.786
## 2267 91 1 0.708 0.03472 0.643 0.779
## 2317 86 1 0.700 0.03528 0.634 0.772
## 2358 83 1 0.691 0.03585 0.625 0.765
## 2403 78 1 0.682 0.03647 0.615 0.758
## 2460 74 1 0.673 0.03713 0.604 0.750
## 2462 73 1 0.664 0.03774 0.594 0.742
## 2581 66 1 0.654 0.03849 0.583 0.734
## 2653 64 1 0.644 0.03922 0.571 0.725
## 2751 60 1 0.633 0.04001 0.559 0.716
## 2918 51 1 0.621 0.04110 0.545 0.707
## 3007 46 1 0.607 0.04237 0.529 0.696
## 3271 39 1 0.592 0.04405 0.511 0.684
##
## Terms_of_Employment=Permanent
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 31 363 1 0.997 0.00275 0.992 1.000
## 66 359 1 0.994 0.00390 0.987 1.000
## 70 358 1 0.992 0.00478 0.982 1.000
## 117 354 1 0.989 0.00553 0.978 1.000
## 122 353 1 0.986 0.00618 0.974 0.998
## 123 352 1 0.983 0.00677 0.970 0.997
## 203 345 1 0.980 0.00732 0.966 0.995
## 217 343 1 0.978 0.00784 0.962 0.993
## 232 340 1 0.975 0.00833 0.959 0.991
## 255 338 1 0.972 0.00879 0.955 0.989
## 257 337 1 0.969 0.00922 0.951 0.987
## 283 333 1 0.966 0.00964 0.947 0.985
## 286 331 1 0.963 0.01005 0.944 0.983
## 321 328 1 0.960 0.01044 0.940 0.981
## 361 324 1 0.957 0.01082 0.936 0.979
## 367 322 1 0.954 0.01118 0.933 0.976
## 371 321 1 0.951 0.01154 0.929 0.974
## 372 320 1 0.948 0.01188 0.925 0.972
## 395 316 1 0.945 0.01221 0.922 0.970
## 397 315 1 0.942 0.01254 0.918 0.967
## 426 312 1 0.939 0.01286 0.914 0.965
## 466 307 1 0.936 0.01317 0.911 0.962
## 470 305 1 0.933 0.01348 0.907 0.960
## 482 304 1 0.930 0.01378 0.903 0.957
## 492 303 1 0.927 0.01408 0.900 0.955
## 493 302 1 0.924 0.01436 0.896 0.953
## 512 301 1 0.921 0.01464 0.893 0.950
## 540 299 1 0.918 0.01491 0.889 0.947
## 568 297 1 0.915 0.01517 0.885 0.945
## 613 294 1 0.912 0.01544 0.882 0.942
## 691 286 1 0.908 0.01571 0.878 0.940
## 700 285 1 0.905 0.01598 0.874 0.937
## 704 284 1 0.902 0.01623 0.871 0.934
## 714 283 1 0.899 0.01649 0.867 0.932
## 737 280 1 0.896 0.01674 0.863 0.929
## 753 279 1 0.892 0.01698 0.860 0.926
## 803 274 1 0.889 0.01723 0.856 0.924
## 839 271 1 0.886 0.01748 0.852 0.921
## 850 270 1 0.883 0.01772 0.849 0.918
## 914 267 1 0.879 0.01796 0.845 0.915
## 939 262 1 0.876 0.01820 0.841 0.912
## 944 261 1 0.873 0.01844 0.837 0.909
## 969 258 1 0.869 0.01867 0.833 0.907
## 1015 251 1 0.866 0.01892 0.829 0.904
## 1072 246 1 0.862 0.01916 0.825 0.901
## 1081 245 1 0.859 0.01941 0.821 0.898
## 1120 241 1 0.855 0.01965 0.817 0.895
## 1150 239 1 0.852 0.01989 0.813 0.891
## 1163 238 1 0.848 0.02013 0.809 0.888
## 1165 236 1 0.844 0.02036 0.805 0.885
## 1166 235 1 0.841 0.02059 0.801 0.882
## 1184 231 1 0.837 0.02082 0.797 0.879
## 1205 228 1 0.833 0.02105 0.793 0.876
## 1271 217 1 0.830 0.02130 0.789 0.872
## 1274 216 1 0.826 0.02154 0.785 0.869
## 1308 213 1 0.822 0.02179 0.780 0.866
## 1353 211 1 0.818 0.02203 0.776 0.862
## 1356 210 1 0.814 0.02227 0.772 0.859
## 1411 207 1 0.810 0.02250 0.767 0.856
## 1429 205 1 0.806 0.02274 0.763 0.852
## 1490 198 1 0.802 0.02299 0.758 0.849
## 1590 191 1 0.798 0.02325 0.754 0.845
## 1662 187 1 0.794 0.02351 0.749 0.841
## 1681 185 1 0.789 0.02377 0.744 0.837
## 1689 184 1 0.785 0.02403 0.739 0.834
## 1811 173 1 0.781 0.02431 0.734 0.830
## 1818 171 1 0.776 0.02459 0.729 0.826
## 1820 170 1 0.771 0.02487 0.724 0.822
## 1879 166 1 0.767 0.02515 0.719 0.818
## 1992 159 1 0.762 0.02545 0.714 0.814
## 2017 156 1 0.757 0.02575 0.708 0.809
## 2052 151 1 0.752 0.02606 0.703 0.805
## 2054 150 1 0.747 0.02637 0.697 0.801
## 2109 147 1 0.742 0.02667 0.692 0.796
## 2134 145 1 0.737 0.02698 0.686 0.792
## 2147 144 1 0.732 0.02727 0.680 0.787
## 2267 136 1 0.726 0.02760 0.674 0.783
## 2317 131 1 0.721 0.02794 0.668 0.778
## 2416 123 1 0.715 0.02832 0.662 0.773
## 2434 121 1 0.709 0.02869 0.655 0.768
## 2453 120 1 0.703 0.02906 0.648 0.762
## 2470 119 1 0.697 0.02941 0.642 0.757
## 2489 118 1 0.691 0.02975 0.635 0.752
## 2523 116 1 0.685 0.03008 0.629 0.747
## 2552 112 1 0.679 0.03043 0.622 0.742
## 2595 107 1 0.673 0.03080 0.615 0.736
## 2656 104 1 0.666 0.03117 0.608 0.730
## 2657 103 1 0.660 0.03154 0.601 0.725
## 2759 98 1 0.653 0.03192 0.594 0.719
## 2850 90 1 0.646 0.03238 0.586 0.713
## 2893 87 1 0.639 0.03285 0.577 0.706
## 2929 83 1 0.631 0.03335 0.569 0.700
## 3001 77 1 0.623 0.03390 0.560 0.693
## 3029 76 1 0.614 0.03443 0.551 0.686
## 3069 72 1 0.606 0.03500 0.541 0.679
## 3079 71 1 0.597 0.03553 0.532 0.671
## 3094 68 1 0.589 0.03608 0.522 0.664
## 3136 62 1 0.579 0.03672 0.511 0.656
## 3178 56 1 0.569 0.03749 0.500 0.647
## 3183 54 1 0.558 0.03825 0.488 0.638
## 3226 50 1 0.547 0.03908 0.476 0.629
## 3262 45 1 0.535 0.04006 0.462 0.619
## 3344 38 1 0.521 0.04140 0.446 0.609
## 3566 25 1 0.500 0.04468 0.420 0.596log_rank_test3 <- survdiff(Surv(time, Employment_Status) ~ Terms_of_Employment, data = Mydata)
log_rank_test3## Call:
## survdiff(formula = Surv(time, Employment_Status) ~ Terms_of_Employment,
## data = Mydata)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## Terms_of_Employment=Contract 236 65 66.5 0.0348 0.0575
## Terms_of_Employment=Permanent 364 104 102.5 0.0226 0.0575
##
## Chisq= 0.1 on 1 degrees of freedom, p= 0.8Plot the model
plot (KM3,ylab = "Survival Probability", xlab = "Time
(Days)", main= "survival probabilities as per the Terms of employment", col=c (2, 4))
legend("topright",c("permanent","contract"),col=c(2,4),lty=c(1,2),ncol=2,cex=0.6)
names(Mydata)## [1] "Serial_No" "Gender" "Age_Bracket"
## [4] "Marital_Status" "Terms_of_Employment" "Job_Group"
## [7] "Employment_Status" "Level_of_Education" "Academic_Designation"
## [10] "Employment_Start_Date" "Employment_End_Date" "Exit_Type"
## [13] "time"###4.Survival distribution of Staff classified by Academic Designation
KM4<-survfit (Surv (time, Employment_Status) ~ Academic_Designation, data = Mydata)##Summaries of the model
KM4## Call: survfit(formula = Surv(time, Employment_Status) ~ Academic_Designation,
## data = Mydata)
##
## n events median 0.95LCL 0.95UCL
## Academic_Designation=Non-teaching staff 381 102 NA 3262 NA
## Academic_Designation=Teaching staff 219 67 3566 3029 NAggsurvplot(KM4,
conf.int = TRUE, # Show confidence intervals
pval = TRUE, # Show p-value
risk.table = TRUE, # Show risk table
ggtheme = theme_minimal())
#####Model Summary
Summary4<-summary(KM4)
Summary4## Call: survfit(formula = Surv(time, Employment_Status) ~ Academic_Designation,
## data = Mydata)
##
## Academic_Designation=Non-teaching staff
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 31 381 1 0.997 0.00262 0.992 1.000
## 66 377 1 0.995 0.00372 0.987 1.000
## 122 371 1 0.992 0.00457 0.983 1.000
## 123 370 1 0.989 0.00529 0.979 1.000
## 203 363 1 0.987 0.00593 0.975 0.998
## 212 362 1 0.984 0.00651 0.971 0.997
## 217 361 1 0.981 0.00704 0.967 0.995
## 255 355 2 0.976 0.00802 0.960 0.991
## 262 352 1 0.973 0.00846 0.956 0.990
## 286 348 1 0.970 0.00888 0.953 0.988
## 293 347 1 0.967 0.00929 0.949 0.986
## 300 346 1 0.965 0.00967 0.946 0.984
## 351 342 1 0.962 0.01005 0.942 0.982
## 367 339 1 0.959 0.01041 0.939 0.979
## 372 338 1 0.956 0.01076 0.935 0.977
## 376 336 1 0.953 0.01110 0.932 0.975
## 395 335 1 0.950 0.01142 0.928 0.973
## 397 334 1 0.947 0.01174 0.925 0.971
## 399 333 1 0.945 0.01204 0.921 0.969
## 466 329 1 0.942 0.01234 0.918 0.966
## 482 326 1 0.939 0.01264 0.914 0.964
## 492 325 1 0.936 0.01293 0.911 0.962
## 493 324 1 0.933 0.01320 0.908 0.959
## 512 323 1 0.930 0.01348 0.904 0.957
## 536 321 1 0.927 0.01374 0.901 0.955
## 568 319 1 0.924 0.01400 0.897 0.952
## 613 315 1 0.921 0.01426 0.894 0.950
## 674 311 1 0.919 0.01452 0.890 0.947
## 700 309 1 0.916 0.01478 0.887 0.945
## 704 308 1 0.913 0.01502 0.884 0.942
## 714 307 1 0.910 0.01527 0.880 0.940
## 720 306 1 0.907 0.01550 0.877 0.938
## 737 301 1 0.904 0.01574 0.873 0.935
## 753 300 1 0.901 0.01597 0.870 0.932
## 772 296 1 0.898 0.01621 0.866 0.930
## 803 293 1 0.894 0.01644 0.863 0.927
## 821 290 1 0.891 0.01667 0.859 0.925
## 895 285 1 0.888 0.01690 0.856 0.922
## 925 283 1 0.885 0.01713 0.852 0.919
## 935 280 1 0.882 0.01736 0.849 0.917
## 944 278 1 0.879 0.01758 0.845 0.914
## 969 273 1 0.876 0.01781 0.841 0.911
## 998 268 1 0.872 0.01804 0.838 0.908
## 1015 265 1 0.869 0.01827 0.834 0.906
## 1072 260 1 0.866 0.01851 0.830 0.903
## 1113 258 1 0.862 0.01874 0.826 0.900
## 1120 255 1 0.859 0.01896 0.823 0.897
## 1150 252 1 0.856 0.01919 0.819 0.894
## 1161 250 1 0.852 0.01942 0.815 0.891
## 1163 249 1 0.849 0.01964 0.811 0.888
## 1165 247 1 0.845 0.01986 0.807 0.885
## 1166 246 1 0.842 0.02007 0.803 0.882
## 1184 243 1 0.838 0.02029 0.800 0.879
## 1192 241 1 0.835 0.02050 0.796 0.876
## 1201 240 1 0.831 0.02071 0.792 0.873
## 1205 238 1 0.828 0.02091 0.788 0.870
## 1271 230 1 0.824 0.02113 0.784 0.867
## 1274 229 1 0.821 0.02134 0.780 0.864
## 1308 226 1 0.817 0.02155 0.776 0.860
## 1353 222 1 0.813 0.02177 0.772 0.857
## 1360 219 1 0.810 0.02198 0.768 0.854
## 1411 216 1 0.806 0.02220 0.764 0.851
## 1429 214 1 0.802 0.02241 0.759 0.847
## 1511 207 1 0.798 0.02264 0.755 0.844
## 1590 196 1 0.794 0.02288 0.751 0.840
## 1591 195 1 0.790 0.02313 0.746 0.837
## 1697 190 1 0.786 0.02338 0.741 0.833
## 1699 189 1 0.782 0.02362 0.737 0.830
## 1725 187 1 0.778 0.02386 0.732 0.826
## 1811 181 1 0.773 0.02411 0.728 0.822
## 1818 179 1 0.769 0.02436 0.723 0.818
## 1879 174 1 0.765 0.02462 0.718 0.814
## 1958 172 1 0.760 0.02487 0.713 0.811
## 1992 167 1 0.756 0.02514 0.708 0.807
## 2017 165 1 0.751 0.02540 0.703 0.802
## 2054 162 2 0.742 0.02592 0.693 0.794
## 2090 156 1 0.737 0.02618 0.687 0.790
## 2109 152 1 0.732 0.02646 0.682 0.786
## 2134 151 1 0.727 0.02672 0.677 0.782
## 2186 145 1 0.722 0.02701 0.671 0.777
## 2267 140 1 0.717 0.02730 0.666 0.773
## 2317 133 2 0.706 0.02793 0.654 0.763
## 2403 123 1 0.701 0.02829 0.647 0.758
## 2416 121 1 0.695 0.02864 0.641 0.753
## 2453 117 1 0.689 0.02901 0.634 0.748
## 2489 115 1 0.683 0.02937 0.628 0.743
## 2581 107 1 0.677 0.02978 0.621 0.737
## 2657 102 1 0.670 0.03022 0.613 0.732
## 2850 90 1 0.662 0.03078 0.605 0.726
## 2918 84 1 0.655 0.03141 0.596 0.719
## 3001 77 1 0.646 0.03213 0.586 0.712
## 3007 76 1 0.638 0.03282 0.576 0.705
## 3079 71 1 0.629 0.03356 0.566 0.698
## 3094 69 1 0.619 0.03429 0.556 0.690
## 3136 64 1 0.610 0.03509 0.545 0.683
## 3178 59 1 0.599 0.03599 0.533 0.674
## 3183 58 1 0.589 0.03682 0.521 0.666
## 3226 54 1 0.578 0.03772 0.509 0.657
## 3262 51 1 0.567 0.03865 0.496 0.648
##
## Academic_Designation=Teaching staff
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 41 215 1 0.995 0.00464 0.986 1.000
## 70 213 1 0.991 0.00656 0.978 1.000
## 89 212 1 0.986 0.00802 0.970 1.000
## 108 210 1 0.981 0.00926 0.963 1.000
## 115 209 1 0.977 0.01034 0.957 0.997
## 117 208 1 0.972 0.01130 0.950 0.994
## 131 207 1 0.967 0.01218 0.944 0.991
## 182 206 1 0.963 0.01300 0.937 0.988
## 232 203 1 0.958 0.01377 0.931 0.985
## 238 202 1 0.953 0.01450 0.925 0.982
## 257 200 1 0.948 0.01519 0.919 0.979
## 274 199 1 0.944 0.01584 0.913 0.975
## 283 197 1 0.939 0.01647 0.907 0.972
## 304 196 1 0.934 0.01707 0.901 0.968
## 321 193 1 0.929 0.01765 0.895 0.964
## 361 190 1 0.924 0.01822 0.889 0.961
## 371 189 1 0.919 0.01877 0.883 0.957
## 426 184 1 0.914 0.01932 0.877 0.953
## 470 181 1 0.909 0.01987 0.871 0.949
## 527 179 1 0.904 0.02039 0.865 0.945
## 540 178 1 0.899 0.02090 0.859 0.941
## 575 177 1 0.894 0.02139 0.853 0.937
## 582 176 1 0.889 0.02187 0.847 0.933
## 691 166 1 0.884 0.02238 0.841 0.929
## 798 160 1 0.878 0.02291 0.834 0.924
## 839 158 1 0.873 0.02343 0.828 0.920
## 850 156 1 0.867 0.02394 0.821 0.915
## 857 155 1 0.861 0.02443 0.815 0.911
## 914 153 1 0.856 0.02491 0.808 0.906
## 939 149 1 0.850 0.02540 0.802 0.901
## 1081 143 1 0.844 0.02590 0.795 0.896
## 1198 140 1 0.838 0.02641 0.788 0.891
## 1225 135 1 0.832 0.02694 0.781 0.886
## 1264 133 1 0.826 0.02745 0.773 0.881
## 1330 129 1 0.819 0.02797 0.766 0.876
## 1356 128 1 0.813 0.02848 0.759 0.870
## 1393 125 1 0.806 0.02898 0.751 0.865
## 1444 122 1 0.800 0.02949 0.744 0.860
## 1490 119 1 0.793 0.03000 0.736 0.854
## 1662 110 1 0.786 0.03058 0.728 0.848
## 1681 107 1 0.778 0.03116 0.720 0.842
## 1689 106 1 0.771 0.03172 0.711 0.836
## 1723 105 1 0.764 0.03226 0.703 0.830
## 1820 98 1 0.756 0.03286 0.694 0.823
## 2052 92 1 0.748 0.03351 0.685 0.816
## 2080 91 1 0.739 0.03413 0.675 0.809
## 2147 89 1 0.731 0.03475 0.666 0.802
## 2267 87 1 0.723 0.03535 0.657 0.795
## 2358 82 1 0.714 0.03600 0.647 0.788
## 2434 78 1 0.705 0.03668 0.636 0.780
## 2460 77 1 0.696 0.03733 0.626 0.773
## 2462 76 1 0.686 0.03795 0.616 0.765
## 2470 75 1 0.677 0.03853 0.606 0.757
## 2523 74 1 0.668 0.03908 0.596 0.749
## 2552 72 1 0.659 0.03962 0.586 0.741
## 2595 67 1 0.649 0.04023 0.575 0.733
## 2653 66 1 0.639 0.04081 0.564 0.724
## 2656 65 1 0.629 0.04135 0.553 0.716
## 2751 61 1 0.619 0.04194 0.542 0.707
## 2759 60 1 0.609 0.04249 0.531 0.698
## 2893 54 1 0.597 0.04317 0.519 0.688
## 2929 51 1 0.586 0.04389 0.506 0.678
## 3029 45 1 0.573 0.04480 0.491 0.668
## 3069 43 1 0.559 0.04569 0.477 0.657
## 3271 32 1 0.542 0.04749 0.456 0.643
## 3344 28 1 0.523 0.04958 0.434 0.629
## 3566 17 1 0.492 0.05538 0.394 0.613log_rank_test4 <- survdiff(Surv(time, Employment_Status) ~ Academic_Designation, data = Mydata)
log_rank_test4## Call:
## survdiff(formula = Surv(time, Employment_Status) ~ Academic_Designation,
## data = Mydata)
##
## N Observed Expected (O-E)^2/E
## Academic_Designation=Non-teaching staff 381 102 107.2 0.252
## Academic_Designation=Teaching staff 219 67 61.8 0.438
## (O-E)^2/V
## Academic_Designation=Non-teaching staff 0.691
## Academic_Designation=Teaching staff 0.691
##
## Chisq= 0.7 on 1 degrees of freedom, p= 0.4###Plotting the model
plot (KM4,ylab = "Survival Probability", xlab = "Time
(Days)", main= "survival probabilities as per the Academic_Designation", col=c (2, 4))
legend("topright",c("Teaching","Non-teaching"),col=c(2,4),lty=c(1,2),ncol=2,cex=0.6)
###5.Survival distribution of Staff classified by Level_of_Education
KM5<-survfit (Surv (time, Employment_Status) ~Level_of_Education , data = Mydata)##Summaries of the model
KM5## Call: survfit(formula = Surv(time, Employment_Status) ~ Level_of_Education,
## data = Mydata)
##
## n events median 0.95LCL 0.95UCL
## Level_of_Education=Certificate 175 44 NA 3262 NA
## Level_of_Education=Degree 41 13 3226 2581 NA
## Level_of_Education=Diploma 165 45 NA 3178 NA
## Level_of_Education=Masters 78 21 NA 2470 NA
## Level_of_Education=Ph.D 141 46 3566 3029 NA#####Model Summary
Summary5<-summary(KM5)
Summary5## Call: survfit(formula = Surv(time, Employment_Status) ~ Level_of_Education,
## data = Mydata)
##
## Level_of_Education=Certificate
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 122 170 1 0.994 0.00587 0.983 1.000
## 123 169 1 0.988 0.00827 0.972 1.000
## 212 166 1 0.982 0.01014 0.963 1.000
## 217 165 1 0.976 0.01170 0.954 1.000
## 255 161 1 0.970 0.01310 0.945 0.996
## 286 157 1 0.964 0.01440 0.936 0.993
## 293 156 1 0.958 0.01558 0.928 0.989
## 300 155 1 0.952 0.01666 0.920 0.985
## 351 153 1 0.946 0.01767 0.911 0.981
## 367 152 1 0.939 0.01862 0.903 0.976
## 376 151 1 0.933 0.01951 0.896 0.972
## 399 150 1 0.927 0.02035 0.888 0.968
## 466 148 1 0.921 0.02115 0.880 0.963
## 492 145 1 0.914 0.02194 0.872 0.958
## 493 144 1 0.908 0.02268 0.864 0.953
## 512 143 1 0.902 0.02340 0.857 0.949
## 568 141 1 0.895 0.02409 0.849 0.944
## 674 136 1 0.889 0.02479 0.841 0.939
## 704 134 1 0.882 0.02548 0.833 0.933
## 714 133 1 0.875 0.02614 0.826 0.928
## 998 119 1 0.868 0.02693 0.817 0.922
## 1072 115 1 0.860 0.02774 0.808 0.917
## 1113 114 1 0.853 0.02850 0.799 0.911
## 1120 112 1 0.845 0.02925 0.790 0.905
## 1163 109 1 0.837 0.02999 0.781 0.898
## 1205 105 1 0.830 0.03075 0.771 0.892
## 1308 100 1 0.821 0.03154 0.762 0.885
## 1429 94 1 0.812 0.03239 0.751 0.878
## 1697 85 1 0.803 0.03339 0.740 0.871
## 1699 84 1 0.793 0.03433 0.729 0.864
## 1725 83 1 0.784 0.03522 0.718 0.856
## 1811 80 1 0.774 0.03612 0.706 0.848
## 1818 79 1 0.764 0.03697 0.695 0.840
## 1958 77 1 0.754 0.03780 0.684 0.832
## 1992 75 1 0.744 0.03861 0.672 0.824
## 2267 68 1 0.733 0.03956 0.660 0.815
## 2317 64 1 0.722 0.04057 0.647 0.806
## 2416 57 1 0.709 0.04179 0.632 0.796
## 2489 55 1 0.696 0.04297 0.617 0.786
## 3001 39 1 0.678 0.04543 0.595 0.774
## 3007 38 1 0.661 0.04761 0.574 0.761
## 3094 34 1 0.641 0.05002 0.550 0.747
## 3136 30 1 0.620 0.05272 0.525 0.732
## 3262 24 1 0.594 0.05649 0.493 0.716
##
## Level_of_Education=Degree
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 262 41 1 0.976 0.0241 0.930 1.000
## 395 38 1 0.950 0.0345 0.885 1.000
## 737 36 1 0.924 0.0425 0.844 1.000
## 753 35 1 0.897 0.0488 0.806 0.998
## 935 32 1 0.869 0.0547 0.768 0.983
## 944 30 1 0.840 0.0601 0.730 0.967
## 1165 26 1 0.808 0.0659 0.689 0.948
## 1184 25 1 0.776 0.0707 0.649 0.927
## 2090 18 1 0.732 0.0788 0.593 0.904
## 2317 16 1 0.687 0.0862 0.537 0.878
## 2581 13 1 0.634 0.0944 0.473 0.849
## 3079 10 1 0.570 0.1041 0.399 0.816
## 3226 7 1 0.489 0.1168 0.306 0.781
##
## Level_of_Education=Diploma
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 31 165 1 0.994 0.00604 0.982 1.000
## 66 164 1 0.988 0.00852 0.971 1.000
## 203 156 1 0.982 0.01056 0.961 1.000
## 255 153 1 0.975 0.01229 0.951 1.000
## 372 148 1 0.969 0.01386 0.942 0.996
## 397 147 1 0.962 0.01525 0.933 0.992
## 482 144 1 0.955 0.01654 0.923 0.988
## 536 142 1 0.949 0.01774 0.914 0.984
## 613 140 1 0.942 0.01886 0.906 0.979
## 700 138 1 0.935 0.01992 0.897 0.975
## 720 137 1 0.928 0.02091 0.888 0.970
## 772 133 1 0.921 0.02189 0.879 0.965
## 803 131 1 0.914 0.02282 0.870 0.960
## 821 129 1 0.907 0.02372 0.862 0.955
## 895 126 1 0.900 0.02460 0.853 0.949
## 925 125 1 0.893 0.02544 0.844 0.944
## 969 122 1 0.885 0.02626 0.835 0.938
## 1015 119 1 0.878 0.02707 0.826 0.933
## 1150 117 1 0.870 0.02786 0.817 0.927
## 1161 115 1 0.863 0.02863 0.808 0.921
## 1166 114 1 0.855 0.02936 0.800 0.915
## 1192 111 1 0.848 0.03009 0.791 0.909
## 1201 110 1 0.840 0.03079 0.782 0.902
## 1271 107 1 0.832 0.03148 0.773 0.896
## 1274 106 1 0.824 0.03215 0.763 0.890
## 1353 102 1 0.816 0.03283 0.754 0.883
## 1360 100 1 0.808 0.03350 0.745 0.876
## 1411 98 1 0.800 0.03416 0.735 0.869
## 1511 93 1 0.791 0.03486 0.726 0.862
## 1590 89 1 0.782 0.03558 0.715 0.855
## 1591 88 1 0.773 0.03627 0.705 0.848
## 1879 78 1 0.763 0.03714 0.694 0.840
## 2017 73 1 0.753 0.03807 0.682 0.831
## 2054 70 2 0.731 0.03991 0.657 0.814
## 2109 65 1 0.720 0.04085 0.644 0.805
## 2134 64 1 0.709 0.04173 0.632 0.796
## 2186 59 1 0.697 0.04272 0.618 0.786
## 2403 51 1 0.683 0.04401 0.602 0.775
## 2453 48 1 0.669 0.04534 0.586 0.764
## 2657 42 1 0.653 0.04697 0.567 0.752
## 2850 35 1 0.634 0.04920 0.545 0.739
## 2918 31 1 0.614 0.05169 0.521 0.724
## 3178 24 1 0.588 0.05551 0.489 0.708
## 3183 23 1 0.563 0.05869 0.459 0.690
##
## Level_of_Education=Masters
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 89 76 1 0.987 0.0131 0.962 1.000
## 131 74 1 0.974 0.0185 0.938 1.000
## 232 71 1 0.960 0.0227 0.916 1.000
## 283 68 1 0.946 0.0264 0.895 0.999
## 371 67 1 0.932 0.0296 0.875 0.991
## 470 60 1 0.916 0.0329 0.854 0.983
## 691 52 1 0.898 0.0367 0.829 0.973
## 839 50 1 0.880 0.0401 0.805 0.963
## 914 49 1 0.862 0.0431 0.782 0.951
## 1081 43 1 0.842 0.0466 0.756 0.939
## 1198 42 1 0.822 0.0496 0.731 0.926
## 1225 41 1 0.802 0.0523 0.706 0.912
## 1490 37 1 0.781 0.0552 0.680 0.897
## 1689 34 1 0.758 0.0581 0.652 0.881
## 2052 31 1 0.733 0.0612 0.623 0.863
## 2358 28 1 0.707 0.0644 0.592 0.845
## 2434 26 1 0.680 0.0674 0.560 0.826
## 2460 25 1 0.653 0.0700 0.529 0.805
## 2470 24 1 0.625 0.0721 0.499 0.784
## 2751 19 1 0.593 0.0755 0.462 0.761
## 2929 15 1 0.553 0.0801 0.416 0.735
##
## Level_of_Education=Ph.D
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 41 139 1 0.993 0.00717 0.979 1.000
## 70 137 1 0.986 0.01014 0.966 1.000
## 108 136 1 0.978 0.01239 0.954 1.000
## 115 135 1 0.971 0.01426 0.944 0.999
## 117 134 1 0.964 0.01589 0.933 0.995
## 182 133 1 0.957 0.01734 0.923 0.991
## 238 132 1 0.949 0.01866 0.913 0.987
## 257 131 1 0.942 0.01988 0.904 0.982
## 274 130 1 0.935 0.02100 0.895 0.977
## 304 129 1 0.928 0.02206 0.885 0.972
## 321 126 1 0.920 0.02308 0.876 0.967
## 361 123 1 0.913 0.02407 0.867 0.961
## 426 122 1 0.905 0.02501 0.858 0.956
## 527 120 1 0.898 0.02591 0.848 0.950
## 540 119 1 0.890 0.02677 0.839 0.944
## 575 118 1 0.883 0.02759 0.830 0.938
## 582 117 1 0.875 0.02836 0.821 0.932
## 798 110 1 0.867 0.02920 0.812 0.926
## 850 107 1 0.859 0.03003 0.802 0.920
## 857 106 1 0.851 0.03082 0.793 0.914
## 939 103 1 0.843 0.03161 0.783 0.907
## 1264 93 1 0.834 0.03254 0.772 0.900
## 1330 91 1 0.824 0.03345 0.761 0.893
## 1356 90 1 0.815 0.03431 0.751 0.885
## 1393 88 1 0.806 0.03515 0.740 0.878
## 1444 85 1 0.797 0.03599 0.729 0.870
## 1662 76 1 0.786 0.03701 0.717 0.862
## 1681 73 1 0.775 0.03804 0.704 0.854
## 1723 72 1 0.765 0.03901 0.692 0.845
## 1820 65 1 0.753 0.04014 0.678 0.836
## 2080 61 1 0.740 0.04133 0.664 0.826
## 2147 59 1 0.728 0.04250 0.649 0.816
## 2267 57 1 0.715 0.04363 0.635 0.806
## 2462 52 1 0.701 0.04490 0.619 0.795
## 2523 51 1 0.688 0.04608 0.603 0.784
## 2552 49 1 0.674 0.04723 0.587 0.773
## 2595 46 1 0.659 0.04842 0.571 0.761
## 2653 45 1 0.644 0.04951 0.554 0.749
## 2656 44 1 0.630 0.05050 0.538 0.737
## 2759 42 1 0.615 0.05148 0.522 0.724
## 2893 39 1 0.599 0.05251 0.504 0.711
## 3029 33 1 0.581 0.05397 0.484 0.697
## 3069 31 1 0.562 0.05538 0.463 0.682
## 3271 23 1 0.538 0.05811 0.435 0.664
## 3344 20 1 0.511 0.06111 0.404 0.646
## 3566 12 1 0.468 0.06927 0.350 0.626log_rank_test5 <- survdiff(Surv(time, Employment_Status) ~ Level_of_Education, data = Mydata)
log_rank_test5## Call:
## survdiff(formula = Surv(time, Employment_Status) ~ Level_of_Education,
## data = Mydata)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## Level_of_Education=Certificate 175 44 48.7 0.4445 0.6245
## Level_of_Education=Degree 41 13 12.2 0.0582 0.0628
## Level_of_Education=Diploma 165 45 46.4 0.0418 0.0577
## Level_of_Education=Masters 78 21 19.8 0.0686 0.0778
## Level_of_Education=Ph.D 141 46 42.0 0.3879 0.5166
##
## Chisq= 1 on 4 degrees of freedom, p= 0.9###Plotting the model
plot (KM5,ylab = "Survival Probability", xlab = "Time
(Days)", main= "survival probabilities as per theLevel of Education", col=c (2, 4,6,8,10))
legend("topright",c("Certificate","Diploma","Degree","Masters","Ph.D"),col=c(2,4,6,8,10),lty=c(1,2),ncol=5,cex=0.6)
###6.Survival distribution of Staff classified by Marital Status
KM6<-survfit (Surv (time, Employment_Status) ~ Marital_Status, data = Mydata)##Summaries of the model
KM6## Call: survfit(formula = Surv(time, Employment_Status) ~ Marital_Status,
## data = Mydata)
##
## n events median 0.95LCL 0.95UCL
## Marital_Status=Divorced/Separated 33 7 NA 3079 NA
## Marital_Status=Married 472 139 NA 3183 NA
## Marital_Status=Single 95 23 NA NA NA#####Model Summary
Summary6<-summary(KM6)
Summary6## Call: survfit(formula = Surv(time, Employment_Status) ~ Marital_Status,
## data = Mydata)
##
## Marital_Status=Divorced/Separated
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 714 29 1 0.966 0.0339 0.901 1.000
## 1811 23 1 0.924 0.0523 0.827 1.000
## 2147 20 1 0.877 0.0670 0.755 1.000
## 2434 17 1 0.826 0.0806 0.682 1.000
## 2523 16 1 0.774 0.0906 0.616 0.974
## 2656 14 1 0.719 0.0995 0.548 0.943
## 3079 10 1 0.647 0.1126 0.460 0.910
##
## Marital_Status=Married
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 31 470 1 0.998 0.00213 0.994 1.000
## 41 469 1 0.996 0.00300 0.990 1.000
## 70 465 1 0.994 0.00368 0.986 1.000
## 89 463 1 0.991 0.00425 0.983 1.000
## 108 461 1 0.989 0.00476 0.980 0.999
## 115 458 1 0.987 0.00521 0.977 0.997
## 117 457 1 0.985 0.00563 0.974 0.996
## 122 455 1 0.983 0.00602 0.971 0.995
## 123 454 1 0.981 0.00639 0.968 0.993
## 131 452 1 0.978 0.00673 0.965 0.992
## 182 447 1 0.976 0.00706 0.963 0.990
## 203 446 1 0.974 0.00738 0.960 0.989
## 212 443 1 0.972 0.00768 0.957 0.987
## 217 442 1 0.970 0.00797 0.954 0.985
## 232 438 1 0.967 0.00826 0.951 0.984
## 238 437 1 0.965 0.00853 0.949 0.982
## 255 434 2 0.961 0.00905 0.943 0.979
## 257 432 1 0.959 0.00930 0.941 0.977
## 262 430 1 0.956 0.00954 0.938 0.975
## 274 429 1 0.954 0.00978 0.935 0.974
## 283 426 1 0.952 0.01001 0.933 0.972
## 286 424 1 0.950 0.01023 0.930 0.970
## 293 423 1 0.947 0.01045 0.927 0.968
## 300 422 1 0.945 0.01066 0.925 0.966
## 321 417 1 0.943 0.01088 0.922 0.964
## 351 413 1 0.941 0.01109 0.919 0.963
## 361 411 1 0.938 0.01129 0.916 0.961
## 367 409 1 0.936 0.01150 0.914 0.959
## 371 408 1 0.934 0.01170 0.911 0.957
## 372 407 1 0.931 0.01189 0.908 0.955
## 395 404 1 0.929 0.01208 0.906 0.953
## 399 403 1 0.927 0.01227 0.903 0.951
## 426 399 1 0.925 0.01246 0.900 0.949
## 466 395 1 0.922 0.01264 0.898 0.947
## 470 392 1 0.920 0.01283 0.895 0.945
## 482 391 1 0.917 0.01301 0.892 0.943
## 492 389 1 0.915 0.01319 0.890 0.941
## 493 388 1 0.913 0.01336 0.887 0.939
## 527 387 1 0.910 0.01354 0.884 0.937
## 536 386 1 0.908 0.01370 0.882 0.935
## 540 385 1 0.906 0.01387 0.879 0.933
## 568 383 1 0.903 0.01403 0.876 0.931
## 582 379 1 0.901 0.01420 0.874 0.929
## 613 378 1 0.899 0.01436 0.871 0.927
## 674 372 1 0.896 0.01452 0.868 0.925
## 691 367 1 0.894 0.01469 0.865 0.923
## 700 365 1 0.891 0.01485 0.863 0.921
## 720 362 1 0.889 0.01501 0.860 0.919
## 737 357 1 0.886 0.01517 0.857 0.917
## 753 356 1 0.884 0.01533 0.854 0.914
## 772 353 1 0.881 0.01549 0.851 0.912
## 798 349 1 0.879 0.01565 0.849 0.910
## 803 348 1 0.876 0.01581 0.846 0.908
## 821 345 1 0.874 0.01597 0.843 0.906
## 839 344 1 0.871 0.01612 0.840 0.903
## 850 343 1 0.869 0.01627 0.837 0.901
## 857 341 1 0.866 0.01642 0.834 0.899
## 914 336 1 0.864 0.01658 0.832 0.897
## 925 333 1 0.861 0.01673 0.829 0.894
## 939 328 1 0.858 0.01688 0.826 0.892
## 944 327 1 0.856 0.01703 0.823 0.890
## 998 322 1 0.853 0.01719 0.820 0.887
## 1015 317 1 0.850 0.01734 0.817 0.885
## 1072 309 1 0.848 0.01750 0.814 0.883
## 1081 308 1 0.845 0.01766 0.811 0.880
## 1113 305 1 0.842 0.01782 0.808 0.878
## 1150 300 1 0.839 0.01798 0.805 0.875
## 1165 297 1 0.836 0.01814 0.802 0.873
## 1166 296 1 0.834 0.01830 0.798 0.870
## 1184 293 1 0.831 0.01845 0.795 0.868
## 1192 291 1 0.828 0.01861 0.792 0.865
## 1198 290 1 0.825 0.01876 0.789 0.863
## 1201 289 1 0.822 0.01891 0.786 0.860
## 1205 287 1 0.819 0.01906 0.783 0.858
## 1225 279 1 0.816 0.01922 0.780 0.855
## 1264 274 1 0.813 0.01938 0.776 0.852
## 1271 272 1 0.810 0.01954 0.773 0.850
## 1274 271 1 0.807 0.01969 0.770 0.847
## 1308 267 1 0.804 0.01985 0.766 0.844
## 1330 266 1 0.801 0.02001 0.763 0.842
## 1353 262 1 0.798 0.02016 0.760 0.839
## 1411 258 1 0.795 0.02032 0.756 0.836
## 1429 255 1 0.792 0.02048 0.753 0.833
## 1444 252 1 0.789 0.02064 0.750 0.830
## 1490 247 1 0.786 0.02080 0.746 0.828
## 1511 244 1 0.783 0.02096 0.743 0.825
## 1590 235 1 0.779 0.02113 0.739 0.822
## 1662 225 1 0.776 0.02132 0.735 0.819
## 1681 223 1 0.772 0.02151 0.731 0.816
## 1689 222 1 0.769 0.02169 0.727 0.812
## 1697 221 1 0.765 0.02187 0.724 0.809
## 1699 220 1 0.762 0.02205 0.720 0.806
## 1723 218 1 0.758 0.02222 0.716 0.803
## 1725 217 1 0.755 0.02239 0.712 0.800
## 1818 207 1 0.751 0.02258 0.708 0.797
## 1820 206 1 0.748 0.02276 0.704 0.794
## 1879 202 1 0.744 0.02295 0.700 0.790
## 1958 200 1 0.740 0.02313 0.696 0.787
## 1992 195 1 0.736 0.02332 0.692 0.783
## 2017 192 1 0.732 0.02351 0.688 0.780
## 2054 187 2 0.725 0.02391 0.679 0.773
## 2080 183 1 0.721 0.02410 0.675 0.770
## 2090 181 1 0.717 0.02429 0.671 0.766
## 2134 176 1 0.713 0.02450 0.666 0.762
## 2186 171 1 0.708 0.02470 0.662 0.759
## 2267 168 2 0.700 0.02512 0.653 0.751
## 2317 160 2 0.691 0.02556 0.643 0.743
## 2358 155 1 0.687 0.02578 0.638 0.739
## 2403 147 1 0.682 0.02602 0.633 0.735
## 2416 145 1 0.677 0.02626 0.628 0.731
## 2453 142 1 0.673 0.02651 0.623 0.727
## 2460 141 1 0.668 0.02675 0.617 0.722
## 2462 140 1 0.663 0.02698 0.612 0.718
## 2470 138 1 0.658 0.02721 0.607 0.714
## 2489 137 1 0.654 0.02743 0.602 0.710
## 2552 133 1 0.649 0.02766 0.597 0.705
## 2581 126 1 0.643 0.02792 0.591 0.701
## 2595 124 1 0.638 0.02817 0.585 0.696
## 2751 117 1 0.633 0.02845 0.579 0.691
## 2759 116 1 0.627 0.02872 0.574 0.686
## 2893 103 1 0.621 0.02908 0.567 0.681
## 2918 99 1 0.615 0.02946 0.560 0.676
## 2929 97 1 0.609 0.02983 0.553 0.670
## 3007 89 1 0.602 0.03027 0.545 0.664
## 3029 88 1 0.595 0.03069 0.538 0.658
## 3069 82 1 0.588 0.03116 0.530 0.652
## 3094 80 1 0.580 0.03162 0.522 0.646
## 3136 75 1 0.573 0.03214 0.513 0.639
## 3178 71 1 0.565 0.03268 0.504 0.632
## 3183 69 1 0.556 0.03321 0.495 0.625
## 3226 64 1 0.548 0.03381 0.485 0.618
## 3262 59 1 0.538 0.03449 0.475 0.610
## 3271 57 1 0.529 0.03516 0.464 0.603
## 3344 51 1 0.519 0.03596 0.453 0.594
## 3566 34 1 0.503 0.03800 0.434 0.584
##
## Marital_Status=Single
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 66 93 1 0.989 0.0107 0.969 1.000
## 304 89 1 0.978 0.0153 0.949 1.000
## 376 88 1 0.967 0.0187 0.931 1.000
## 397 87 1 0.956 0.0216 0.915 0.999
## 512 85 1 0.945 0.0241 0.899 0.993
## 575 83 1 0.933 0.0263 0.883 0.986
## 704 80 1 0.922 0.0285 0.867 0.979
## 895 76 1 0.909 0.0306 0.852 0.971
## 935 74 1 0.897 0.0325 0.836 0.963
## 969 73 1 0.885 0.0343 0.820 0.955
## 1120 69 1 0.872 0.0361 0.804 0.946
## 1161 68 1 0.859 0.0378 0.788 0.937
## 1163 67 1 0.846 0.0394 0.773 0.927
## 1356 64 1 0.833 0.0409 0.757 0.917
## 1360 62 1 0.820 0.0424 0.741 0.907
## 1393 59 1 0.806 0.0439 0.724 0.897
## 1591 54 1 0.791 0.0456 0.707 0.885
## 2052 47 1 0.774 0.0476 0.686 0.873
## 2109 45 1 0.757 0.0495 0.666 0.861
## 2653 32 1 0.733 0.0533 0.636 0.846
## 2657 31 1 0.710 0.0566 0.607 0.830
## 2850 27 1 0.683 0.0603 0.575 0.812
## 3001 24 1 0.655 0.0642 0.540 0.794log_rank_test6 <- survdiff(Surv(time, Employment_Status) ~ Marital_Status, data = Mydata)
log_rank_test6## Call:
## survdiff(formula = Surv(time, Employment_Status) ~ Marital_Status,
## data = Mydata)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## Marital_Status=Divorced/Separated 33 7 11.5 1.770 1.90
## Marital_Status=Married 472 139 127.9 0.965 3.98
## Marital_Status=Single 95 23 29.6 1.471 1.79
##
## Chisq= 4.2 on 2 degrees of freedom, p= 0.1###Plotting the model
plot (KM6,ylab = "Survival Probability", xlab = "Time
(Days)", main= "survival probabilities as per the Marital Status", col=c (2, 4, 6))
legend("topright",c("Single","Married","Separated"),col=c(2,4,6),lty=c(1,2),ncol=3,cex=0.6)
cox.model1<-coxph(Surv(time,Employment_Status)~Gender, data = Mydata)
cox.model1## Call:
## coxph(formula = Surv(time, Employment_Status) ~ Gender, data = Mydata)
##
## coef exp(coef) se(coef) z p
## GenderMale 0.1144 1.1212 0.1597 0.716 0.474
##
## Likelihood ratio test=0.52 on 1 df, p=0.4716
## n= 600, number of events= 169SEMI-PARAMETRIC TESTS
COX PROPORTIONAL MODEL
cox.model<-coxph(Surv(time,Employment_Status)~Gender+Level_of_Education+Age_Bracket+Terms_of_Employment+Job_Group, data = Mydata)###Summary of the model
summary(cox.model)## Call:
## coxph(formula = Surv(time, Employment_Status) ~ Gender + Level_of_Education +
## Age_Bracket + Terms_of_Employment + Job_Group, data = Mydata)
##
## n= 600, number of events= 169
##
## coef exp(coef) se(coef) z Pr(>|z|)
## GenderMale 0.10388 1.10947 0.16185 0.642 0.521
## Level_of_EducationDegree -0.28165 0.75453 0.51675 -0.545 0.586
## Level_of_EducationDiploma -0.21772 0.80435 0.34576 -0.630 0.529
## Level_of_EducationMasters -0.48349 0.61663 0.67041 -0.721 0.471
## Level_of_EducationPh.D -0.43495 0.64730 0.60973 -0.713 0.476
## Age_Bracket26-35 -0.18727 0.82922 0.29579 -0.633 0.527
## Age_Bracket36-50 -0.46556 0.62779 0.27541 -1.690 0.091 .
## Age_Bracket51-75 -0.24779 0.78052 0.29203 -0.849 0.396
## Terms_of_EmploymentPermanent 0.06051 1.06238 0.15991 0.378 0.705
## Job_Group 0.06596 1.06819 0.06105 1.080 0.280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## GenderMale 1.1095 0.9013 0.8079 1.524
## Level_of_EducationDegree 0.7545 1.3253 0.2740 2.077
## Level_of_EducationDiploma 0.8043 1.2432 0.4084 1.584
## Level_of_EducationMasters 0.6166 1.6217 0.1657 2.294
## Level_of_EducationPh.D 0.6473 1.5449 0.1959 2.138
## Age_Bracket26-35 0.8292 1.2060 0.4644 1.481
## Age_Bracket36-50 0.6278 1.5929 0.3659 1.077
## Age_Bracket51-75 0.7805 1.2812 0.4404 1.383
## Terms_of_EmploymentPermanent 1.0624 0.9413 0.7765 1.453
## Job_Group 1.0682 0.9362 0.9477 1.204
##
## Concordance= 0.558 (se = 0.025 )
## Likelihood ratio test= 7.24 on 10 df, p=0.7
## Wald test = 7.32 on 10 df, p=0.7
## Score (logrank) test = 7.37 on 10 df, p=0.7cox.zph(cox.model)## chisq df p
## Gender 0.624 1 0.43
## Level_of_Education 1.273 4 0.87
## Age_Bracket 0.346 3 0.95
## Terms_of_Employment 2.117 1 0.15
## Job_Group 0.101 1 0.75
## GLOBAL 5.046 10 0.89cox.model<-cox.model<-coxph(Surv(time,Employment_Status)~Gender+Level_of_Education+Age_Bracket+Terms_of_Employment+Job_Group+Exit_Type, data = Mydata)
#cox.model2<-cox.model2<-coxph(Surv(time,Employment_Status)~Gender+Level_of_Education+Age_Bracket+Terms_of_Employment, data = Mydata)
#cox.model3<-cox.model3<-coxph(Surv(time,Employment_Status)~Gender+Level_of_Education+Age_Bracket+Job_Group, data = Mydata)
cox.model4<-cox.model4<-coxph(Surv(time,Employment_Status)~Gender+Level_of_Education+Terms_of_Employment+Job_Group, data = Mydata)
#cox.model5<-cox.model5<-coxph(Surv(time,Employment_Status)~Gender+Age_Bracket+Terms_of_Employment+Job_Group, data = Mydata)
#cox.model6<-cox.model6<-coxph(Surv(time,Employment_Status)~Level_of_Education+Age_Bracket+Terms_of_Employment+Job_Group, data = Mydata)summary(cox.model)## Call:
## coxph(formula = Surv(time, Employment_Status) ~ Gender + Level_of_Education +
## Age_Bracket + Terms_of_Employment + Job_Group + Exit_Type,
## data = Mydata)
##
## n= 169, number of events= 169
## (431 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## GenderMale 0.04471 1.04572 0.18173 0.246 0.8057
## Level_of_EducationDegree -0.77191 0.46213 0.56572 -1.364 0.1724
## Level_of_EducationDiploma -0.48020 0.61866 0.38701 -1.241 0.2147
## Level_of_EducationMasters -0.78743 0.45501 0.71740 -1.098 0.2724
## Level_of_EducationPh.D -1.13154 0.32254 0.64509 -1.754 0.0794 .
## Age_Bracket26-35 0.17716 1.19382 0.31362 0.565 0.5721
## Age_Bracket36-50 0.12434 1.13240 0.29299 0.424 0.6713
## Age_Bracket51-75 0.10832 1.11441 0.31444 0.344 0.7305
## Terms_of_EmploymentPermanent -0.28938 0.74872 0.17179 -1.685 0.0921 .
## Job_Group 0.09134 1.09564 0.06488 1.408 0.1592
## Exit_TypeDismissal -0.28005 0.75575 0.37775 -0.741 0.4585
## Exit_TypeResignation 0.11838 1.12568 0.34637 0.342 0.7325
## Exit_TypeRetirement 0.17352 1.18949 0.53140 0.327 0.7440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## GenderMale 1.0457 0.9563 0.73237 1.493
## Level_of_EducationDegree 0.4621 2.1639 0.15248 1.401
## Level_of_EducationDiploma 0.6187 1.6164 0.28975 1.321
## Level_of_EducationMasters 0.4550 2.1977 0.11153 1.856
## Level_of_EducationPh.D 0.3225 3.1004 0.09109 1.142
## Age_Bracket26-35 1.1938 0.8376 0.64564 2.207
## Age_Bracket36-50 1.1324 0.8831 0.63769 2.011
## Age_Bracket51-75 1.1144 0.8973 0.60172 2.064
## Terms_of_EmploymentPermanent 0.7487 1.3356 0.53468 1.048
## Job_Group 1.0956 0.9127 0.96481 1.244
## Exit_TypeDismissal 0.7557 1.3232 0.36044 1.585
## Exit_TypeResignation 1.1257 0.8884 0.57093 2.219
## Exit_TypeRetirement 1.1895 0.8407 0.41979 3.370
##
## Concordance= 0.557 (se = 0.026 )
## Likelihood ratio test= 12.27 on 13 df, p=0.5
## Wald test = 11.99 on 13 df, p=0.5
## Score (logrank) test = 12.09 on 13 df, p=0.5library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectstepwise_model <- stepAIC(cox.model, direction = "both")## Start: AIC=1416.61
## Surv(time, Employment_Status) ~ Gender + Level_of_Education +
## Age_Bracket + Terms_of_Employment + Job_Group + Exit_Type
##
## Df AIC
## - Age_Bracket 3 1410.9
## - Level_of_Education 4 1413.5
## - Gender 1 1414.7
## - Job_Group 1 1416.6
## <none> 1416.6
## - Terms_of_Employment 1 1417.4
## - Exit_Type 3 1967.6
##
## Step: AIC=1410.94
## Surv(time, Employment_Status) ~ Gender + Level_of_Education +
## Terms_of_Employment + Job_Group + Exit_Type
##
## Df AIC
## - Level_of_Education 4 1407.6
## - Gender 1 1409.0
## - Job_Group 1 1410.7
## <none> 1410.9
## - Terms_of_Employment 1 1411.6
## + Age_Bracket 3 1416.6
## - Exit_Type 3 1965.5
##
## Step: AIC=1407.56
## Surv(time, Employment_Status) ~ Gender + Terms_of_Employment +
## Job_Group + Exit_Type
##
## Df AIC
## - Gender 1 1405.6
## - Job_Group 1 1405.6
## <none> 1407.6
## - Terms_of_Employment 1 1409.2
## + Level_of_Education 4 1410.9
## + Age_Bracket 3 1413.5
## - Exit_Type 3 1958.5
##
## Step: AIC=1405.56
## Surv(time, Employment_Status) ~ Terms_of_Employment + Job_Group +
## Exit_Type
##
## Df AIC
## - Job_Group 1 1403.6
## <none> 1405.6
## - Terms_of_Employment 1 1407.2
## + Gender 1 1407.6
## + Level_of_Education 4 1409.0
## + Age_Bracket 3 1411.5
## - Exit_Type 3 1957.0
##
## Step: AIC=1403.63
## Surv(time, Employment_Status) ~ Terms_of_Employment + Exit_Type
##
## Df AIC
## <none> 1403.6
## - Terms_of_Employment 1 1405.3
## + Job_Group 1 1405.6
## + Gender 1 1405.6
## + Level_of_Education 4 1408.7
## + Age_Bracket 3 1409.6
## - Exit_Type 3 1956.8summary(stepwise_model)## Call:
## coxph(formula = Surv(time, Employment_Status) ~ Terms_of_Employment +
## Exit_Type, data = Mydata)
##
## n= 169, number of events= 169
## (431 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Terms_of_EmploymentPermanent -0.31802 0.72759 0.16402 -1.939 0.0525 .
## Exit_TypeDismissal -0.26707 0.76562 0.34662 -0.771 0.4410
## Exit_TypeResignation 0.09533 1.10002 0.32313 0.295 0.7680
## Exit_TypeRetirement 0.12718 1.13562 0.47103 0.270 0.7872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Terms_of_EmploymentPermanent 0.7276 1.3744 0.5276 1.003
## Exit_TypeDismissal 0.7656 1.3061 0.3881 1.510
## Exit_TypeResignation 1.1000 0.9091 0.5839 2.072
## Exit_TypeRetirement 1.1356 0.8806 0.4511 2.859
##
## Concordance= 0.548 (se = 0.025 )
## Likelihood ratio test= 7.25 on 4 df, p=0.1
## Wald test = 7.14 on 4 df, p=0.1
## Score (logrank) test = 7.19 on 4 df, p=0.1weibfull.aft<-survreg (Surv (time, Employment_Status) ~ Gender+Level_of_Education+Age_Bracket+Terms_of_Employment+Job_Group+Exit_Type, data = Mydata, dist='weibull', control=survreg.control (maxiter=70))
summary (weibfull.aft)##
## Call:
## survreg(formula = Surv(time, Employment_Status) ~ Gender + Level_of_Education +
## Age_Bracket + Terms_of_Employment + Job_Group + Exit_Type,
## data = Mydata, dist = "weibull", control = survreg.control(maxiter = 70))
## Value Std. Error z p
## (Intercept) 7.3471 0.3616 20.32 < 0.0000000000000002
## GenderMale -0.0685 0.1286 -0.53 0.59
## Level_of_EducationDegree 0.3988 0.4056 0.98 0.33
## Level_of_EducationDiploma 0.2470 0.2753 0.90 0.37
## Level_of_EducationMasters 0.4188 0.5165 0.81 0.42
## Level_of_EducationPh.D 0.5322 0.4571 1.16 0.24
## Age_Bracket26-35 -0.1298 0.2265 -0.57 0.57
## Age_Bracket36-50 -0.0701 0.2117 -0.33 0.74
## Age_Bracket51-75 -0.0710 0.2281 -0.31 0.76
## Terms_of_EmploymentPermanent 0.1426 0.1225 1.16 0.24
## Job_Group -0.0434 0.0465 -0.93 0.35
## Exit_TypeDismissal 0.2168 0.2692 0.81 0.42
## Exit_TypeResignation 0.0209 0.2469 0.08 0.93
## Exit_TypeRetirement 0.0305 0.3831 0.08 0.94
## Log(scale) -0.3160 0.0636 -4.97 0.00000067
##
## Scale= 0.729
##
## Weibull distribution
## Loglik(model)= -1376.4 Loglik(intercept only)= -1379.5
## Chisq= 6.19 on 13 degrees of freedom, p= 0.94
## Number of Newton-Raphson Iterations: 7
## n=169 (431 observations deleted due to missingness)