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Lampiran 1. Data pengendara sepeda motor yang mengalami kecelakaan lalu lintas pada di Kota Medan periode Januari 2016 sampai Juni 2016.
Keterangan:
age : Umur pengendara saat terjadi kecelakaan.
sex : Jenis kelamin pengemudi. (1: laki-laki dan 2: perempuan) s_status : Kepemilikan SIM. (0: memiliki dan 1: tidak memiliki)
vehicle : Kendaraan yang terlibat kecelakaan dengan pengendara. (1: roda 2, 2: roda 3, 3: roda 4, 4: > roda 4)
helmet_use : Penggunaan Helm. (0: menggunakan dan 1: tidak menggunakan) durasi : Selang waktu dari pengendara mengalami kecelakaan sampai
kasus kecelakaan ditutup.
status : Kondisi pengemudi setelah terjadi kecelakaan. (0:hidup/tersensor dan 2: meninggal dunia)
kategori umur : Pengelompokkan umur. (1: 16-25 tahun, 2: 26-44 tahun, 3:45-65 tahun dan 4: >66 tahun)
ID age sex s_status vehicle helmet_use status durasi kategori.umur
1 25 1 0 3 0 1 2 1
ID age sex s_status vehicle helmet_use status durasi kategori.umur
ID age sex s_status vehicle helmet_use status durasi kategori.umur
ID age sex s_status vehicle helmet_use status durasi kategori.umur
ID age sex s_status vehicle helmet_use status durasi kategori.umur
ID age sex s_status vehicle helmet_use status durasi kategori.umur
ID age sex s_status vehicle helmet_use status durasi kategori.umur
ID age sex s_status vehicle helmet_use status durasi kategori.umur
ID age sex s_status vehicle helmet_use status durasi kategori.umur
ID age sex s_status vehicle helmet_use status durasi kategori.umur
ID age sex s_status vehicle helmet_use status durasi kategori.umur
394 59 1 1 1 0 0 30 3
395 66 1 1 3 0 0 37 4
396 20 2 1 1 1 0 28 1
397 32 2 0 3 0 0 2 2
398 60 1 1 4 0 0 38 3
399 16 1 1 1 0 0 41 1
400 54 1 0 3 0 0 20 3
401 48 1 0 3 0 0 7 3
402 37 1 0 3 0 1 23 2
403 51 1 1 1 0 0 49 3
404 19 1 1 1 1 0 48 1
405 19 1 1 4 1 1 16 1
406 15 1 1 3 1 1 22 1
407 23 1 1 4 1 1 1 1
408 37 1 1 1 0 0 23 2
409 23 2 0 3 0 0 24 1
410 15 1 1 1 1 0 27 1
411 45 1 1 3 1 1 2 3
412 32 1 0 3 1 1 1 2
413 24 1 1 3 0 0 51 1
414 22 1 0 3 0 0 16 1
415 34 2 1 1 0 0 22 2
Lampiran 2. Output R Model Cox dengan Metode Breslow
>##Estimasi Parameter##
> Laka<-read.csv(file.choose(), header = TRUE)
> View(Laka)
> library(survival)
> args(coxph)
function (formula, data, weights, subset, na.action, init, control, ties = c("efron", "breslow", "exact"), singular.ok = TRUE,
robust = FALSE, model = FALSE, x = FALSE, y = TRUE, tt, method = ties, ...)
NULL
> cox<-coxph(Surv(durasi,status)~age+sex+s_status+vehicle+helmet_use, data = Laka, method = "breslow")
> summary(cox) Call:
coxph(formula = Surv(durasi, status) ~ age + sex + s_status + vehicle + helmet_use, data = Laka, method = "breslow")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) age 0.008605 1.008642 0.008489 1.014 0.3107 sex -0.783788 0.456673 0.412845 -1.899 0.0576 . s_status -1.226769 0.293239 0.304967 -4.023 5.76e-05 ***
vehicle 0.696992 2.007704 0.147539 4.724 2.31e-06 ***
helmet_use 1.552974 4.725504 0.321481 4.831 1.36e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95 age 1.0086 0.9914 0.9920 1.0256
s_status 0.2932 3.4102 0.1613 0.5331 vehicle 2.0077 0.4981 1.5035 2.6809 helmet_use 4.7255 0.2116 2.5165 8.8735
Concordance= 0.807 (se = 0.045 ) Rsquare= 0.157 (max possible= 0.765 )
Likelihood ratio test= 71.01 on 5 df, p=6.328e-14 Wald test = 63.68 on 5 df, p=2.104e-12 Score (logrank) test = 72.07 on 5 df, p=3.797e-14
> cox0<-coxph(Surv(durasi,status)~1,method = "breslow", data = Laka)
> summary(cox0)
Call: coxph(formula = Surv(durasi, status) ~ 1, data = Laka, method = "breslow")
Null model
log likelihood= -300.6643 n= 415
> cox$loglik
[1] -300.6643 -265.1615
> cox0$loglik [1] -300.6643
> 1-pchisq(-2*(cox0$loglik-cox$loglik[2]),5) [1] 6.328271e-14
>##Pemilihan Model Terbaik Model Cox##
> #variabel umur
> cox1<-coxph(Surv(durasi,status)~age, method = "breslow", data = Laka)
> cox1$loglik
[1] -300.6643 -299.1296
> 1-pchisq(-2*(cox0$loglik-cox1$loglik[2]),1) [1] 0.07978243
> #variabel jenis kelamin
> cox2<-coxph(Surv(durasi,status)~sex, method = "breslow", data = Laka)
> cox2$loglik
[1] -300.6643 -298.0404
> 1-pchisq(-2*(cox0$loglik-cox2$loglik[2]),1) [1] 0.02197421
> #variabel sim
> cox3<-coxph(Surv(durasi,status)~s_status, method = "breslow", data = Laka)
> cox3$loglik
[1] -300.6643 -295.2166
> 1-pchisq(-2*(cox0$loglik-cox3$loglik[2]),1) [1] 0.0009640563
> #variabel kendaraan
> cox4<-coxph(Surv(durasi,status)~vehicle, method = "breslow", data = Laka)
> cox4$loglik
[1] -300.6643 -284.8386
> 1-pchisq(-2*(cox0$loglik-cox4$loglik[2]),1) [1] 1.844809e-08
> #variabel helm
>cox5<-coxph(Surv(durasi,status)~helmet_use, method = "breslow", data = Laka)
> cox5$loglik
[1] -300.6643 -293.0001
> 1-pchisq(-2*(cox0$loglik-cox5$loglik[2]),1) [1] 9.035556e-05
> #variabel kendaraan+umur
> cox41<-coxph(Surv(durasi,status)~vehicle+age, method = "breslow", data = La
> cox41$loglik
[1] -300.6643 -284.6592
> 1-pchisq(-2*(cox4$loglik[2]-cox41$loglik[2]),1) [1] 0.5491771
> #variabel kendaraan+jenis kelamin
> cox42<-coxph(Surv(durasi,status)~vehicle+sex, method = "breslow", data = La ka)
> cox42$loglik
[1] -300.6643 -283.0219
> 1-pchisq(-2*(cox4$loglik[2]-cox42$loglik[2]),1) [1] 0.05663561
> #variabel kendaraan+sim
> cox43<-coxph(Surv(durasi,status)~vehicle+s_status, method = "breslow", data
= Laka)
> cox43$loglik
[1] -300.6643 -280.5321
> 1-pchisq(-2*(cox4$loglik[2]-cox43$loglik[2]),1) [1] 0.003337816
> #variabel kendaaran+helm
> cox45<-coxph(Surv(durasi,status)~vehicle+helmet_use, method = "breslow", da ta = Laka)
> cox45$loglik
[1] -300.6643 -275.4602
> 1-pchisq(-2*(cox4$loglik[2]-cox45$loglik[2]),1) [1] 1.48502e-05
> #variabel kendaraan+helm+umur
> cox451<-coxph(Surv(durasi,status)~vehicle+helmet_use+age, method = "breslo w", data = Laka)
> cox451$loglik
[1] -300.6643 -274.6917
> 1-pchisq(-2*(cox45$loglik[2]-cox451$loglik[2]),1) [1] 0.2150675
> #variabel kendaraan+helm+jenis kelamin
> cox452<-coxph(Surv(durasi,status)~vehicle+helmet_use+sex, method = "breslo w", data = Laka)
> cox452$loglik
[1] -300.6643 -272.9844
> 1-pchisq(-2*(cox45$loglik[2]-cox452$loglik[2]),1) [1] 0.02606593
> #variabel kendaraan+helm+sim
> cox453<-coxph(Surv(durasi,status)~vehicle+helmet_use+s_status, method = "br eslow", data = Laka)
> cox453$loglik
[1] -300.6643 -268.2309
> 1-pchisq(-2*(cox45$loglik[2]-cox453$loglik[2]),1) [1] 0.000143264
> #variabel kendaraan+helm+sim+umur
> cox4531<-coxph(Surv(durasi,status)~vehicle+helmet_use+s_status+age,method
= "breslow", data = Laka)
> cox4531$loglik [1] -300.6643 -267.3105
> 1-pchisq(-2*(cox453$loglik[2]-cox4531$loglik[2]),1) [1] 0.1748688
> #variabel kendaraan+helm+sim+jenis kelamin
> cox4532<-coxph(Surv(durasi,status)~vehicle+helmet_use+s_status+sex,method
> cox4532$loglik [1] -300.6643 -265.6680
> 1-pchisq(-2*(cox453$loglik[2]-cox4532$loglik[2]),1) [1] 0.02357254
> #variabel kendaraan+helm+sim+jennis kelamin+umur
>cox45321<-coxph(Surv(durasi,status)~vehicle+helmet_use+s_status+sex+age, method = "breslow", data = Laka)
> cox45321$loglik [1] -300.6643 -265.1615
> 1-pchisq(-2*(cox4532$loglik[2]-cox45321$loglik[2]),1) [1] 0.314188
> #Estimasi Parameter Model Cox terbaik dengan seleksi forward#
> cox4532<-coxph(Surv(durasi,status)~vehicle+helmet_use+s_status+sex, metho d = "breslow", data = Laka)\
> summary(cox4532) Call:
coxph(formula = Surv(durasi, status) ~ vehicle + helmet_use + s_status + sex, data = Laka, method = "breslow")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) vehicle 0.7229 2.0604 0.1451 4.983 6.27e-07 ***
helmet_use 1.5218 4.5807 0.3201 4.754 1.99e-06 ***
s_status -1.2292 0.2925 0.3059 -4.018 5.87e-05 ***
sex -0.8406 0.4314 0.4095 -2.053 0.0401 * ---
Signif. codes:0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95 vehicle 2.0604 0.4853 1.5505 2.7381 helmet_use 4.5807 0.2183 2.4461 8.5781 s_status 0.2925 3.4185 0.1606 0.5328 sex 0.4314 2.3178 0.1933 0.9628
Concordance= 0.808 (se = 0.044 ) Rsquare= 0.155 (max possible= 0.765 )
Likelihood ratio test= 69.99 on 4 df, p=2.276e-14 Wald test = 62.46 on 4 df, p=8.833e-13 Score (logrank) test = 70.23 on 4 df, p=2.032e-14
>##Pengujian Parameter Model Cox secara Parsial##
> #variabel jenis kelamin
> cox2<-coxph(Surv(durasi,status)~sex, method = "breslow", data =Laka)
> summary(cox2) Call:
coxph(formula = Surv(durasi, status) ~ sex, data =Laka, method = "breslow")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) sex -0.8373 0.4329 0.4046 -2.07 0.0385 * ---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95 sex 0.4329 2.31 0.1959 0.9566
Concordance= 0.553 (se = 0.033 ) Rsquare= 0.013 (max possible= 0.765 )
Wald test = 4.28 on 1 df, p=0.0385 Score (logrank) test = 4.54 on 1 df, p=0.0332
> #variabel sim
> cox3<-coxph(Surv(durasi,status)~s_status, method = "breslow", data =Laka)
> summary(cox3) Call:
coxph(formula = Surv(durasi, status) ~ s_status, data =Laka, method = "breslow")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) s_status -0.9946 0.3699 0.2850 -3.49 0.000483 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95 s_status 0.3699 2.704 0.2116 0.6466
Concordance= 0.634 (se = 0.031 ) Rsquare= 0.026 (max possible= 0.765 )
Likelihood ratio test= 10.9 on 1 df, p=0.0009641 Wald test = 12.18 on 1 df, p=0.0004829 Score (logrank) test = 13.18 on 1 df, p=0.0002834
> #variabel kendaraan
> cox4<-coxph(Surv(durasi,status)~vehicle, method = "breslow", data = Laka)
> summary(cox4) Call:
coxph(formula = Surv(durasi, status) ~ vehicle, data = Laka, method = "breslow")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) vehicle 0.7724 2.1650 0.1518 5.088 3.61e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95 vehicle 2.165 0.4619 1.608 2.915
Concordance= 0.708 (se = 0.041 ) Rsquare= 0.073 (max possible= 0.765 )
Likelihood ratio test= 31.65 on 1 df, p=1.845e-08 Wald test = 25.89 on 1 df, p=3.613e-07 Score (logrank) test = 29.74 on 1 df, p=4.937e-08
> #variabel helm
> cox5<-coxph(Surv(durasi,status)~helmet_use, method = "breslow", data =Laka)
> summary(cox5) Call:
coxph(formula = Surv(durasi, status) ~ helmet_use, data =Laka, method = "breslow")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) helmet_use 1.1041 3.0166 0.3014 3.663 0.000249 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
helmet_use 3.017 0.3315 1.671 5.446
Concordance= 0.649 (se = 0.038 ) Rsquare= 0.036 (max possible= 0.765 )
Likelihood ratio test= 15.33 on 1 df, p=9.036e-05 Wald test = 13.42 on 1 df, p=0.0002493 Score (logrank) test = 14.82 on 1 df, p=0.0001184
>##Uji Asumsi Proportional Hazard##
> cox4532<-coxph(Surv(durasi,status)~vehicle + helmet_use + s_status + sex, method = "breslow", data = Laka)
> sresids<-residuals(cox4532, type = "scaledsch")
> colnames(sresids)<-names(cox4532$coefficients)
> time<-as.numeric(rownames(sresids))
> plot(time, sresids[,1], xlab = "Time", ylab = "Scaled Schoenfeld Residual (Kendaraan)")
> lines(smooth.spline(time,sresids[,1]), col= "Red", lwd=2)
> plot(time, sresids[,2], xlab = "Time", ylab = "Scaled Schoenfeld Residual (Helm)")
> lines(smooth.spline(time,sresids[,2]), col= "Red", lwd=2)
> plot(time, sresids[,3], xlab = "Time", ylab = "Scaled Schoenfeld Residual (SIM)")
> lines(smooth.spline(time,sresids[,3]), col= "Red", lwd=2)
> plot(time, sresids[,4], xlab = "Time", ylab = "Scaled Schoenfeld Residual (Jenis Kelamin)")
> lines(smooth.spline(time,sresids[,4]), col= "Red", lwd=2)
Lampiran 3. Output R Model Cox dengan Metode Efron
>##Estimasi Parameter##
> cox<-coxph(Surv(durasi,status)~age+sex+s_status+vehicle+helmet_use, data = Laka, method = "efron")
> summary(cox) Call:
coxph(formula = Surv(durasi, status) ~ age + sex + s_status + vehicle + helmet_use, data = Laka, method = "efron")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) age 0.007723 1.007753 0.008494 0.909 0.3632 sex -0.809714 0.444986 0.413496 -1.958 0.0502 . s_status -1.301519 0.272118 0.304997 -4.267 1.98e-05 ***
vehicle 0.726737 2.068320 0.148996 4.878 1.07e-06 ***
helmet_use 1.603577 4.970781 0.322628 4.970 6.68e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95 age 1.0078 0.9923 0.9911 1.0247 sex 0.4450 2.2473 0.1979 1.0007 s_status 0.2721 3.6749 0.1497 0.4947 vehicle 2.0683 0.4835 1.5445 2.7698 helmet_use 4.9708 0.2012 2.6412 9.3550
Concordance= 0.807 (se = 0.045 ) Rsquare= 0.164 (max possible= 0.765 )
Likelihood ratio test= 74.29 on 5 df, p=1.31e-14 Wald test = 66.22 on 5 df, p=6.257e-13
> cox0<-coxph(Surv(durasi,status)~1,method = "efron", data = Laka)
> summary(cox0)
Call: coxph(formula = Surv(durasi, status) ~ 1, data = Laka, method = "efron")
Null model
log likelihood= -300.1753 n= 415
> cox$loglik
[1] -300.1753 -263.0288
> cox0$loglik [1] -300.1753
> 1-pchisq(-2*(cox0$loglik-cox$loglik[2]),5) [1] 1.310063e-14
>##Pemilihan Model Terbaik##
>#variabel umur
> cox1<-coxph(Surv(durasi,status)~age, method = "efron", data = Laka)
> cox1$loglik
[1] -300.1753 -298.6371
> 1-pchisq(-2*(cox0$loglik-cox1$loglik[2]),1) [1] 0.07943437
> #variabel jenis kelamin
> cox2<-coxph(Surv(durasi,status)~sex, method = "efron", data = Laka)
> cox2$loglik
[1] -300.1753 -297.5231
> 1-pchisq(-2*(cox0$loglik-cox2$loglik[2]),1) [1] 0.02127197
> #variabel sim
> cox3<-coxph(Surv(durasi,status)~s_status, method = "efron", data = Laka)
> cox3$loglik
[1] -300.1753 -294.5366
> 1-pchisq(-2*(cox0$loglik-cox3$loglik[2]),1) [1] 0.0007845788
> #variabel kendaraan
> cox4<-coxph(Surv(durasi,status)~vehicle, method = "efron", data = Laka)
> cox4$loglik
[1] -300.1753 -283.8971
> 1-pchisq(-2*(cox0$loglik-cox4$loglik[2]),1) [1] 1.157854e-08
> #variabel helm
> cox5<-coxph(Surv(durasi,status)~helmet_use, method = "efron", data = Laka)
> cox5$loglik
[1] -300.1753 -292.3576
> 1-pchisq(-2*(cox0$loglik-cox5$loglik[2]),1) [1] 7.679904e-05
> #variabel kendaraan+umur
> cox41<-coxph(Surv(durasi,status)~vehicle+age, data = Laka, method = "efron")
> cox41$loglik
[1] -300.1753 -283.7367
> 1-pchisq(-2*(cox4$loglik[2]-cox41$loglik[2]),1) [1] 0.5710444
> #variabel kendaraan+jenis kelamin
> cox42<-coxph(Surv(durasi,status)~vehicle+sex, data = Laka, method = "efron")
> cox42$loglik
[1] -300.1753 -282.0462
> 1-pchisq(-2*(cox4$loglik[2]-cox42$loglik[2]),1)
> #variabel kendaraan+sim
> cox43<-coxph(Surv(durasi,status)~vehicle+s_status, data = Laka, method = "efr on")
> cox45<-coxph(Surv(durasi,status)~vehicle+helmet_use, data = Laka, method =
"efron")
> cox451<-coxph(Surv(durasi,status)~vehicle+helmet_use+age, data = Laka, met hod = "efron")
> cox452<-coxph(Surv(durasi,status)~vehicle+helmet_use+sex, data = Laka, met hod = "efron")
> cox452$loglik
[1] -300.1753 -271.6579
> 1-pchisq(-2*(cox45$loglik[2]-cox452$loglik[2]),1) [1] 0.02422016
> #variabel kendaraan+helm+sim
> cox453<-coxph(Surv(durasi,status)~vehicle+helmet_use+s_status, data = Laka, method = "efron")
> cox4531<-coxph(Surv(durasi,status)~vehicle+helmet_use+s_status+age, data = Laka, method = "efron")
> cox4532<-coxph(Surv(durasi,status)~vehicle+helmet_use+s_status+sex, data = Laka, method = "efron") data = Laka, method = "efron")
> cox45321$loglik [1] -300.1753 -263.0288
> 1-pchisq(-2*(cox4532$loglik[2]-cox45321$loglik[2]),1)
> #Estimasi Parameter Model Cox terbaik dengan seleksi forward#
> cox4532<-coxph(Surv(durasi,status)~sex+s_status+vehicle+helmet_use,data = Laka, method = "efron")
> summary(cox4532) Call:
coxph(formula = Surv(durasi, status) ~ sex + s_status + vehicle + helmet_use, data = Laka, method = "efron")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) sex -0.8645 0.4213 0.4096 -2.110 0.0348 * s_status -1.3089 0.2701 0.3059 -4.279 1.88e-05 ***
vehicle 0.7508 2.1187 0.1464 5.130 2.90e-07 ***
helmet_use 1.5793 4.8514 0.3218 4.908 9.22e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95 sex 0.4213 2.3738 0.1888 0.9402 s_status 0.2701 3.7022 0.1483 0.4920 vehicle 2.1187 0.4720 1.5903 2.8226 helmet_use 4.8514 0.2061 2.5820 9.1155
Concordance= 0.808 (se = 0.044 ) Rsquare= 0.162 (max possible= 0.765 )
Likelihood ratio test= 73.48 on 4 df, p=4.219e-15 Wald test = 65.1 on 4 df, p=2.452e-13 Score (logrank) test = 73.31 on 4 df, p=4.552e-15
>##Pengujian Parameter Model Cox secara Parsial##
> #variabel jenis kelamin
> cox2<-coxph(Surv(durasi,status)~sex, data = Laka, method = "efron")
> summary(cox2) Call:
coxph(formula = Surv(durasi, status) ~ sex, data = Laka, method = "efron")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) sex -0.8414 0.4311 0.4046 -2.08 0.0376 * ---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95 sex 0.4311 2.32 0.1951 0.9527
Concordance= 0.553 (se = 0.033 ) Rsquare= 0.013 (max possible= 0.765 ) Likelihood ratio test= 5.3 on 1 df, p=0.02127 Wald test = 4.33 on 1 df, p=0.03756 Score (logrank) test = 4.58 on 1 df, p=0.0323
> #variabel sim
> cox3<-coxph(Surv(durasi,status)~s_status, data = Laka, method = "efron")
> summary(cox3) Call:
coxph(formula = Surv(durasi, status) ~ s_status, data = Laka, method = "efron")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) s_status -1.0124 0.3633 0.2847 -3.556 0.000377 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95 s_status 0.3633 2.752 0.2079 0.6348
Concordance= 0.634 (se = 0.031 ) Rsquare= 0.027 (max possible= 0.765 )
Likelihood ratio test= 11.28 on 1 df, p=0.0007846 Wald test = 12.64 on 1 df, p=0.0003767 Score (logrank) test = 13.72 on 1 df, p=0.000212
> #variabel kendaraan
> cox4<-coxph(Surv(durasi,status)~vehicle, data = Laka, method = "efron")
> summary(cox4) Call:
coxph(formula = Surv(durasi, status) ~ vehicle, data = Laka, method = "efron")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) vehicle 0.7871 2.1970 0.1529 5.148 2.63e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95 vehicle 2.197 0.4552 1.628 2.965
Concordance= 0.708 (se = 0.041 ) Rsquare= 0.075 (max possible= 0.765 )
Likelihood ratio test= 32.56 on 1 df, p=1.158e-08 Wald test = 26.5 on 1 df, p=2.629e-07 Score (logrank) test = 30.57 on 1 df, p=3.215e-08
> #variabel helm
> cox5<-coxph(Surv(durasi,status)~helmet_use, data = Laka, method = "efron")
> summary(cox5) Call:
coxph(formula = Surv(durasi, status) ~ helmet_use, data = Laka, method = "efron
")
n= 415, number of events= 57
coef exp(coef) se(coef) z Pr(>|z|) helmet_use 1.1147 3.0488 0.3014 3.698 0.000217 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95 helmet_use 3.049 0.328 1.689 5.504
Concordance= 0.649 (se = 0.038 ) Rsquare= 0.037 (max possible= 0.765 )
Likelihood ratio test= 15.64 on 1 df, p=7.68e-05 Wald test = 13.68 on 1 df, p=0.0002172 Score (logrank) test = 15.13 on 1 df, p=0.0001003
>##Uji Asumsi Proportional Hazard##
> cox4532<-coxph(Surv(durasi,status)~ vehicle + helmet_use + s_status + sex, method = "efron", data = Laka)
> colnames(sresids)<-names(cox4532$coefficients)
> time<-as.numeric(rownames(sresids))
> plot(time, sresids[,1], xlab = "Time", ylab = "Scaled Schoenfeld Residual (Kendaraan)")
> lines(smooth.spline(time,sresids[,1]), col= "Red", lwd=2)
> plot(time, sresids[,2], xlab = "Time", ylab = "Scaled Schoenfeld Residual (Helm)")
> lines(smooth.spline(time,sresids[,2]), col= "Red", lwd=2)
> plot(time, sresids[,3], xlab = "Time", ylab = "Scaled Schoenfeld Residual (SIM)")
> lines(smooth.spline(time,sresids[,3]), col= "Red", lwd=2)
> plot(time, sresids[,4], xlab = "Time", ylab = "Scaled Schoenfeld Residual (Jenis Kelamin)")
> lines(smooth.spline(time,sresids[,4]), col= "Red", lwd=2)