TF CEECS
Grupo 10
16/10/2022
Integrantes:
-Eisik, Magali
-Gomez, Sergio
-Querci, Marcia
-Valenti, Florencia
-Estudio de Sobrevida Global en pacientes pediĆ”tricos con diagnĆ³stico de Leucemia LinfoblĆ”stica Aguda en Argentina.
-Objetivo General: evaluar sobrevida global en pacientes pediatricos con LLA considerando la enfermedad residual minima como una de las variables explicativas mas importantes.
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.5 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.0.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rows## [1] "Sexo" "Down" "Blancos" "Blastos" "MO"
## [6] "SNC" "Ploidia" "Estirpe" "RTA_PRED" "CAT_ERM"
## [11] "Edad" "MLL" "TIEMPOSG" "SGSTATUS" "Edad_cat"
## [16] "Blancos_cat" "TEL"## [1] 17## [1] 2096DT::datatable(head(datos))Analisis exploratorio de datos
## Warning: NAs introducidos por coerciĆ³n## Stratified by SGSTATUS
## 0 1 p test
## n 1628 468
## Edad_cat = 2 (%) 713 (43.8) 258 ( 55.1) <0.001
## MO (mean (SD)) 86.76 (14.95) 86.89 (15.39) 0.870
## Blancos_cat = 2 (%) 490 (30.1) 227 ( 48.5) <0.001
## Blastos (mean (SD)) 45.98 (34.85) 55.24 (36.17) <0.001
## Sexo = 1 (%) 896 (55.0) 276 ( 59.0) 0.145
## Down = 1 (%) 21 ( 1.3) 23 ( 4.9) <0.001
## Ploidia (%) <0.001
## 1 277 (17.0) 63 ( 13.5)
## 2 849 (52.1) 53 ( 11.3)
## 4 427 (26.2) 351 ( 75.0)
## 5 75 ( 4.6) 1 ( 0.2)
## Down = 1 (%) 21 ( 1.3) 23 ( 4.9) <0.001
## MLL = 1 (%) 19 ( 1.2) 5 ( 1.1) 1.000
## TEL = 1 (%) 193 (14.8) 29 ( 7.4) <0.001
## CAT_ERM (%) <0.001
## 1 569 (41.4) 87 ( 23.6)
## 2 629 (45.8) 177 ( 48.0)
## 3 175 (12.7) 105 ( 28.5)
## SNC (%) 0.004
## 1 1561 (95.9) 431 ( 92.1)
## 2 15 ( 0.9) 9 ( 1.9)
## 3 52 ( 3.2) 28 ( 6.0)
## Estirpe = T (%) 156 ( 9.6) 82 ( 17.5) <0.001
## RTA_PRED = 1 (%) 1502 (92.3) 358 ( 77.2) <0.001
## SGSTATUS = 1 (%) 0 ( 0.0) 468 (100.0) <0.001##
## ### Summary of continuous variables ###
##
## SGSTATUS: 0
## n miss p.miss mean sd median p25 p75 min max skew kurt
## MO 1628 0 0.0 87 15 90 84 96 10 100 -2.19 5
## Blastos 1628 11 0.7 46 35 45 10 80 0 100 0.06 -2
## ------------------------------------------------------------
## SGSTATUS: 1
## n miss p.miss mean sd median p25 p75 min max skew kurt
## MO 468 0 0.0 87 15 90 84 97 14 100 -2.1 4
## Blastos 468 2 0.4 55 36 66 18 90 0 100 -0.3 -1
##
## p-values
## pNormal pNonNormal
## MO 8.704414e-01 3.045138e-01
## Blastos 5.956894e-07 1.283530e-07
##
## Standardize mean differences
## 1 vs 2
## MO 0.008487245
## Blastos 0.260606893
##
## =======================================================================================
##
## ### Summary of categorical variables ###
##
## SGSTATUS: 0
## var n miss p.miss level freq percent cum.percent
## Edad_cat 1628 0 0.0 1 915 56.2 56.2
## 2 713 43.8 100.0
##
## Blancos_cat 1628 0 0.0 1 1138 69.9 69.9
## 2 490 30.1 100.0
##
## Sexo 1628 0 0.0 0 732 45.0 45.0
## 1 896 55.0 100.0
##
## Down 1628 0 0.0 0 1607 98.7 98.7
## 1 21 1.3 100.0
##
## Ploidia 1628 0 0.0 1 277 17.0 17.0
## 2 849 52.1 69.2
## 4 427 26.2 95.4
## 5 75 4.6 100.0
##
## Down 1628 0 0.0 0 1607 98.7 98.7
## 1 21 1.3 100.0
##
## MLL 1628 8 0.5 0 1601 98.8 98.8
## 1 19 1.2 100.0
##
## TEL 1628 324 19.9 0 1111 85.2 85.2
## 1 193 14.8 100.0
##
## CAT_ERM 1628 255 15.7 1 569 41.4 41.4
## 2 629 45.8 87.3
## 3 175 12.7 100.0
##
## SNC 1628 0 0.0 1 1561 95.9 95.9
## 2 15 0.9 96.8
## 3 52 3.2 100.0
##
## Estirpe 1628 0 0.0 B 1472 90.4 90.4
## T 156 9.6 100.0
##
## RTA_PRED 1628 1 0.1 0 125 7.7 7.7
## 1 1502 92.3 100.0
##
## SGSTATUS 1628 0 0.0 0 1628 100.0 100.0
## 1 0 0.0 100.0
##
## ------------------------------------------------------------
## SGSTATUS: 1
## var n miss p.miss level freq percent cum.percent
## Edad_cat 468 0 0.0 1 210 44.9 44.9
## 2 258 55.1 100.0
##
## Blancos_cat 468 0 0.0 1 241 51.5 51.5
## 2 227 48.5 100.0
##
## Sexo 468 0 0.0 0 192 41.0 41.0
## 1 276 59.0 100.0
##
## Down 468 0 0.0 0 445 95.1 95.1
## 1 23 4.9 100.0
##
## Ploidia 468 0 0.0 1 63 13.5 13.5
## 2 53 11.3 24.8
## 4 351 75.0 99.8
## 5 1 0.2 100.0
##
## Down 468 0 0.0 0 445 95.1 95.1
## 1 23 4.9 100.0
##
## MLL 468 1 0.2 0 462 98.9 98.9
## 1 5 1.1 100.0
##
## TEL 468 77 16.5 0 362 92.6 92.6
## 1 29 7.4 100.0
##
## CAT_ERM 468 99 21.2 1 87 23.6 23.6
## 2 177 48.0 71.5
## 3 105 28.5 100.0
##
## SNC 468 0 0.0 1 431 92.1 92.1
## 2 9 1.9 94.0
## 3 28 6.0 100.0
##
## Estirpe 468 0 0.0 B 386 82.5 82.5
## T 82 17.5 100.0
##
## RTA_PRED 468 4 0.9 0 106 22.8 22.8
## 1 358 77.2 100.0
##
## SGSTATUS 468 0 0.0 0 0 0.0 0.0
## 1 468 100.0 100.0
##
##
## p-values
## pApprox pExact
## Edad_cat 1.866934e-05 1.581855e-05
## Blancos_cat 2.106591e-13 4.595884e-13
## Sexo 1.445013e-01 1.391732e-01
## Down 3.525496e-06 1.102234e-05
## Ploidia 1.706850e-85 1.906877e-89
## Down1 3.525496e-06 1.102234e-05
## MLL 1.000000e+00 1.000000e+00
## TEL 2.069250e-04 7.934739e-05
## CAT_ERM 7.721123e-16 3.250971e-15
## SNC 3.806641e-03 4.424535e-03
## Estirpe 2.755190e-06 4.764492e-06
## RTA_PRED 8.530406e-20 1.595150e-17
## SGSTATUS 0.000000e+00 0.000000e+00
##
## Standardize mean differences
## 1 vs 2
## Edad_cat 0.228125508
## Blancos_cat 0.383721247
## Sexo 0.079597601
## Down 0.210209547
## Ploidia 1.219003556
## Down 0.210209547
## MLL 0.009701837
## TEL 0.236609459
## CAT_ERM 0.481288932
## SNC 0.160304959
## Estirpe 0.233524495
## RTA_PRED 0.431279929
## SGSTATUS NaN| 1 vs 2 | |
|---|---|
| Edad_cat | 0.2281255 |
| Blancos_cat | 0.3837212 |
| Sexo | 0.0795976 |
| Down | 0.2102095 |
| Ploidia | 1.2190036 |
| Down | 0.2102095 |
| MLL | 0.0097018 |
| TEL | 0.2366095 |
| CAT_ERM | 0.4812889 |
| SNC | 0.1603050 |
| Estirpe | 0.2335245 |
| RTA_PRED | 0.4312799 |
| SGSTATUS | NaN |
| 1 Test de Wilcoxon | |
| 2 Test de Chi Cuadrado | |
| 3 Test de Fisher |
-TABLA1
## Warning in ModuleReturnVarsExist(vars, data): The data frame does not have: Rto
## Globulos Blancos (GB/L sangre) Dropped##
## Overall
## n 2096
## Sexo Masculino = 1 (%) 1172 (55.9)
## Presencia de Down = 1 (%) 44 ( 2.1)
## Blastos en Sangre Periferica (%) (mean (SD)) 48.05 (35.35)
## Blastos en MO (%) (mean (SD)) 86.79 (15.05)
## Compromiso SNC (%)
## 1 1992 (95.0)
## 2 24 ( 1.1)
## 3 80 ( 3.8)
## Ploidia (%)
## 1 340 (16.2)
## 2 902 (43.0)
## 4 778 (37.1)
## 5 76 ( 3.6)
## Estirpe T = T (%) 238 (11.4)
## Rta prednisona = 1 (%) 1860 (89.0)
## Edad (aƱos) (mean (SD)) 6.55 (4.47)
## Gen MLL = 1 (%) 24 ( 1.1)
## Fallecio = 1 (%) 468 (22.3)
## Categoria Rto GB = 2 (%) 971 (46.3)
## Gen TEL = 1 (%) 222 (13.1)##
## Overall
## n 2096
## Sexo Masculino = 1 (%) 1172 (55.9)
## Presencia de Down = 1 (%) 44 ( 2.1)
## Blastos en Sangre Periferica (%) (mean (SD)) 48.05 (35.35)
## Blastos en MO (%) (mean (SD)) 86.79 (15.05)
## Compromiso SNC (%)
## 1 1992 (95.0)
## 2 24 ( 1.1)
## 3 80 ( 3.8)
## Ploidia (%)
## 1 340 (16.2)
## 2 902 (43.0)
## 4 778 (37.1)
## 5 76 ( 3.6)
## Estirpe T = T (%) 238 (11.4)
## Rta prednisona = 1 (%) 1860 (89.0)
## Edad (aƱos) (mean (SD)) 6.55 (4.47)
## Gen MLL = 1 (%) 24 ( 1.1)
## Fallecio = 1 (%) 468 (22.3)
## Categoria Rto GB = 2 (%) 971 (46.3)
## Gen TEL = 1 (%) 222 (13.1)| Overall | |
|---|---|
| n | 2096 |
| Sexo Masculino = 1 (%) | 1172 (55.9) |
| Presencia de Down = 1 (%) | 44 ( 2.1) |
| Blastos en Sangre Periferica (%) (mean (SD)) | 48.05 (35.35) |
| Blastos en MO (%) (mean (SD)) | 86.79 (15.05) |
| Compromiso SNC (%) | |
| 1 | 1992 (95.0) |
| 2 | 24 ( 1.1) |
| 3 | 80 ( 3.8) |
| Ploidia (%) | |
| 1 | 340 (16.2) |
| 2 | 902 (43.0) |
| 4 | 778 (37.1) |
| 5 | 76 ( 3.6) |
| Estirpe T = T (%) | 238 (11.4) |
| Rta prednisona = 1 (%) | 1860 (89.0) |
| Edad (aƱos) (mean (SD)) | 6.55 (4.47) |
| Gen MLL = 1 (%) | 24 ( 1.1) |
| Fallecio = 1 (%) | 468 (22.3) |
| Categoria Rto GB = 2 (%) | 971 (46.3) |
| Gen TEL = 1 (%) | 222 (13.1) |
| 1 Test de Wilcoxon | |
| 2 Test de Chi Cuadrado | |
| 3 Test de Fisher |
##Analisis de supuestos y test
-Variables categoricas
## Warning: NAs introducidos por coerciĆ³n##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tabla1
## X-squared = 21.507, df = 1, p-value = 3.525e-06##
## Pearson's Chi-squared test
##
## data: tabla2
## X-squared = 395.9, df = 3, p-value < 2.2e-16
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tabla3
## X-squared = 82.923, df = 1, p-value < 2.2e-16##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tabla4
## X-squared = 2.1294, df = 1, p-value = 0.1445
##
## Pearson's Chi-squared test
##
## data: tabla5
## X-squared = 11.142, df = 2, p-value = 0.003807##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tabla6
## X-squared = 3.0278e-30, df = 1, p-value = 1
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tabla7
## X-squared = 13.767, df = 1, p-value = 0.0002069##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tabla8
## X-squared = 21.98, df = 1, p-value = 2.755e-06
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tabla9
## X-squared = 18.32, df = 1, p-value = 1.867e-05##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: tabla10
## X-squared = 53.903, df = 1, p-value = 2.107e-13##
## Pearson's Chi-squared test
##
## data: tabla11
## X-squared = 69.595, df = 2, p-value = 7.721e-16
-Variables continuas
##
## Shapiro-Wilk normality test
##
## data: datos$Edad
## W = 0.90726, p-value < 2.2e-16## Warning in as.data.frame.numeric(residuos, r_estandarizados, predichos):
## 'row.names' is not a character vector of length 2096 -- omitting it. Will be an
## error!## Warning in if (!optional) names(value) <- nm: la condiciĆ³n tiene longitud > 1 y
## sĆ³lo el primer elemento serĆ” usado##
## Shapiro-Wilk normality test
##
## data: residuos
## W = 0.91756, p-value < 2.2e-16
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
## Shapiro-Wilk normality test
##
## data: datos$Blastos
## W = 0.89253, p-value < 2.2e-16## Warning in as.data.frame.numeric(residuos, r_estandarizados1, predichos1):
## 'row.names' is not a character vector of length 2096 -- omitting it. Will be an
## error!
## Warning in as.data.frame.numeric(residuos, r_estandarizados1, predichos1): la
## condiciĆ³n tiene longitud > 1 y sĆ³lo el primer elemento serĆ” usado##
## Shapiro-Wilk normality test
##
## data: residuos1
## W = 0.90662, p-value < 2.2e-16
##
## Shapiro-Wilk normality test
##
## data: datos$Blancos
## W = 0.44578, p-value < 2.2e-16## Warning in as.data.frame.numeric(residuos2, r_estandarizados2, predichos2):
## 'row.names' is not a character vector of length 2096 -- omitting it. Will be an
## error!
## Warning in as.data.frame.numeric(residuos2, r_estandarizados2, predichos2): la
## condiciĆ³n tiene longitud > 1 y sĆ³lo el primer elemento serĆ” usado##
## Shapiro-Wilk normality test
##
## data: residuos2
## W = 0.5307, p-value < 2.2e-16
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##
## Shapiro-Wilk normality test
##
## data: datos$MO
## W = 0.74845, p-value < 2.2e-16## Warning in as.data.frame.numeric(residuos3, r_estandarizados3, predichos3):
## 'row.names' is not a character vector of length 2096 -- omitting it. Will be an
## error!
## Warning in as.data.frame.numeric(residuos3, r_estandarizados3, predichos3): la
## condiciĆ³n tiene longitud > 1 y sĆ³lo el primer elemento serĆ” usado##
## Shapiro-Wilk normality test
##
## data: residuos3
## W = 0.74877, p-value < 2.2e-16
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## some## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 23.892 1.096e-06 ***
## 2094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 0.9532 0.329
## 2081## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 96.876 < 2.2e-16 ***
## 2094
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Levene's Test for Homogeneity of Variance (center = median)
## Df F value Pr(>F)
## group 1 0.4469 0.5039
## 2094#Test para variables continuas
#Blastos: no cumple normalidad ni homocedasticidad. Test de la mediana.
#MO: no cumple normalidad y cumple homocedasticidad. Test de Wilcoxon/Mann Whitney
library(coin)
#Test de la mediana
median_test(Blastos~SGSTATUS,datos) ##
## Asymptotic Two-Sample Brown-Mood Median Test
##
## data: Blastos by SGSTATUS (0, 1)
## Z = -4.0017, p-value = 6.29e-05
## alternative hypothesis: true mu is not equal to 0# Da pv < 0.05
#Test de Wilcoxon/Mann Whitney
wilcox.test(datos$MO~datos$SGSTATUS,paired=FALSE)##
## Wilcoxon rank sum test with continuity correction
##
## data: datos$MO by datos$SGSTATUS
## W = 369200, p-value = 0.3045
## alternative hypothesis: true location shift is not equal to 0#da pv > 0.05Visualizacion de datos
##
## Attaching package: 'networkD3'## The following object is masked from 'package:DT':
##
## JS## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo##
## Attaching package: 'highcharter'## The following object is masked from 'package:networkD3':
##
## JS
## Warning: Removed 13 rows containing non-finite values (stat_boxplot).## Warning: Removed 13 rows containing missing values (geom_point).
## Warning: Removed 13 rows containing non-finite values (stat_density).
-Sankey plot para variables categoricas
#Sankey plot
#CATEGORIAS DE ENFERMEDAD RESIDUAL Y SNC
data1<- basesankey%>%dplyr::select(SNC,CAT_ERM, evolucion)
hchart(data_to_sankey(data1), "sankey", name = "Sobrevida segun ERM y SNC")##CATEGORIAS DE ENFERMEDAD RESIDUAL Y SNC
data2<- basesankey%>%dplyr::select(Estirpe,CAT_ERM, evolucion)
hchart(data_to_sankey(data2), "sankey", name = "Sobrevida segun ERM y Estirpe")#CATEGORIAS DE ENFERMEDAD residual minima
data2<- basesankey%>%dplyr::select(CAT_ERM, evolucion)
hchart(data_to_sankey(data2), "sankey", name = "Sobrevida segun ERM ")sankeyNetworkOutput("TF-ceecs.html", width = "500px", height = "1000px")#ESTIRPE, Rta pred Y CAT ERM
data3<- basesankey%>%dplyr::select(Estirpe, RTA_PRED,CAT_ERM, Estirpe)
hchart(data_to_sankey(data3), "sankey", name = "Estirpe, Respuesta a Prednisona y ERM ")Analisis e imputacion de datos faltantes
##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbind## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.5.1
## Current Matrix version is 1.4.0
## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package## Loading required package: colorspace## Loading required package: grid## VIM is ready to use.## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues##
## Attaching package: 'VIM'## The following object is masked from 'package:datasets':
##
## sleep
## Sexo Down Blancos Blastos MO SNC
## 0 0 0 13 0 0
## Ploidia Estirpe RTA_PRED CAT_ERM Edad MLL
## 0 0 5 354 0 9
## TIEMPOSG SGSTATUS Edad_cat Blancos_cat TEL
## 0 0 0 0 401## [1] 782## [1] 34850## [1] 0.02194656## [1] 2.194656## [1] 0.9780534## [1] 97.80534## [1] 0.1688931## [1] 83.11069## [1] 0.1913168## [1] 80.86832## [1] 0.004293893## [1] 99.57061## [1] 0.002385496## [1] 99.76145## [1] 0.00620229## [1] 99.37977## # A tibble: 17 x 3
## variable n_miss pct_miss
## <chr> <int> <dbl>
## 1 TEL 401 19.1
## 2 CAT_ERM 354 16.9
## 3 Blastos 13 0.620
## 4 MLL 9 0.429
## 5 RTA_PRED 5 0.239
## 6 Sexo 0 0
## 7 Down 0 0
## 8 Blancos 0 0
## 9 MO 0 0
## 10 SNC 0 0
## 11 Ploidia 0 0
## 12 Estirpe 0 0
## 13 Edad 0 0
## 14 TIEMPOSG 0 0
## 15 SGSTATUS 0 0
## 16 Edad_cat 0 0
## 17 Blancos_cat 0 0## # A tibble: 2,096 x 3
## case n_miss pct_miss
## <int> <int> <dbl>
## 1 498 3 17.6
## 2 555 3 17.6
## 3 1350 3 17.6
## 4 9 2 11.8
## 5 313 2 11.8
## 6 376 2 11.8
## 7 408 2 11.8
## 8 411 2 11.8
## 9 412 2 11.8
## 10 419 2 11.8
## # ... with 2,086 more rows
## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.
## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.
## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.
## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.
## Warning: It is deprecated to specify `guide = FALSE` to remove a guide. Please
## use `guide = "none"` instead.
## Sexo Down Blancos MO SNC Ploidia Estirpe Edad TIEMPOSG SGSTATUS Edad_cat
## 1472 1 1 1 1 1 1 1 1 1 1 1
## 249 1 1 1 1 1 1 1 1 1 1 1
## 200 1 1 1 1 1 1 1 1 1 1 1
## 148 1 1 1 1 1 1 1 1 1 1 1
## 12 1 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 1 1
## 8 1 1 1 1 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1 1 1 1 1
## 3 1 1 1 1 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1 1 1 1 1 1
## 0 0 0 0 0 0 0 0 0 0 0
## Blancos_cat RTA_PRED MLL Blastos CAT_ERM TEL
## 1472 1 1 1 1 1 1 0
## 249 1 1 1 1 1 0 1
## 200 1 1 1 1 0 1 1
## 148 1 1 1 1 0 0 2
## 12 1 1 1 0 1 1 1
## 1 1 1 1 0 1 0 2
## 8 1 1 0 1 1 1 1
## 1 1 1 0 1 0 0 3
## 3 1 0 1 1 0 1 2
## 2 1 0 1 1 0 0 3
## 0 5 9 13 354 401 782## Warning in plot.aggr(res, ...): not enough horizontal space to display
## frequencies
##
## Variables sorted by number of missings:
## Variable Count
## TEL 0.191316794
## CAT_ERM 0.168893130
## Blastos 0.006202290
## MLL 0.004293893
## RTA_PRED 0.002385496
## Sexo 0.000000000
## Down 0.000000000
## Blancos 0.000000000
## MO 0.000000000
## SNC 0.000000000
## Ploidia 0.000000000
## Estirpe 0.000000000
## Edad 0.000000000
## TIEMPOSG 0.000000000
## SGSTATUS 0.000000000
## Edad_cat 0.000000000
## Blancos_cat 0.000000000
##
## Variables sorted by number of missings:
## Variable Count
## TEL 0.191316794
## CAT_ERM 0.168893130
## Blastos 0.006202290
## MLL 0.004293893
## RTA_PRED 0.002385496
## Sexo 0.000000000
## Down 0.000000000
## Blancos 0.000000000
## MO 0.000000000
## SNC 0.000000000
## Ploidia 0.000000000
## Estirpe 0.000000000
## Edad 0.000000000
## TIEMPOSG 0.000000000
## SGSTATUS 0.000000000
## Edad_cat 0.000000000
## Blancos_cat 0.000000000## # A tibble: 2,096 x 17
## Sexo_NA Down_NA Blancos_NA Blastos_NA MO_NA SNC_NA Ploidia_NA Estirpe_NA
## <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct>
## 1 !NA !NA !NA !NA !NA !NA !NA !NA
## 2 !NA !NA !NA !NA !NA !NA !NA !NA
## 3 !NA !NA !NA !NA !NA !NA !NA !NA
## 4 !NA !NA !NA !NA !NA !NA !NA !NA
## 5 !NA !NA !NA !NA !NA !NA !NA !NA
## 6 !NA !NA !NA !NA !NA !NA !NA !NA
## 7 !NA !NA !NA !NA !NA !NA !NA !NA
## 8 !NA !NA !NA !NA !NA !NA !NA !NA
## 9 !NA !NA !NA !NA !NA !NA !NA !NA
## 10 !NA !NA !NA !NA !NA !NA !NA !NA
## # ... with 2,086 more rows, and 9 more variables: RTA_PRED_NA <fct>,
## # CAT_ERM_NA <fct>, Edad_NA <fct>, MLL_NA <fct>, TIEMPOSG_NA <fct>,
## # SGSTATUS_NA <fct>, Edad_cat_NA <fct>, Blancos_cat_NA <fct>, TEL_NA <fct>## Rows: 2,096
## Columns: 34
## $ Sexo <fct> 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1~
## $ Down <fct> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ Blancos <dbl> 3, 36, 10, 8, 12, 37, 203, 3, 1, 3, 13, 30, 52, 130, 6,~
## $ Blastos <dbl> 2, 48, 95, 98, 22, 62, 93, 21, 0, 8, 27, 90, 84, 100, 0~
## $ MO <dbl> 90, 50, 98, 98, 80, 95, 80, 85, 87, 54, 95, 92, 99, 100~
## $ SNC <fct> 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 3, 1, 1, 1, 1, 1, 3, 1~
## $ Ploidia <fct> 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2~
## $ Estirpe <fct> B, T, B, B, B, T, T, B, B, B, T, T, T, T, T, T, T, T, T~
## $ RTA_PRED <fct> 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
## $ CAT_ERM <fct> NA, 3, 3, 1, 1, 1, 3, 1, NA, NA, NA, 1, 2, 2, 2, NA, 1,~
## $ Edad <dbl> 2, 14, 4, 3, 1, 15, 13, 6, 8, 12, 5, 10, 3, 6, 14, 10, ~
## $ MLL <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
## $ TIEMPOSG <dbl> 81, 16, 67, 131, 28, 69, 37, 86, 67, 36, 61, 23, 62, 26~
## $ SGSTATUS <fct> 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0~
## $ Edad_cat <fct> 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 1, 2~
## $ Blancos_cat <fct> 1, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2~
## $ TEL <fct> 0, NA, 0, 0, NA, 0, 0, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ Sexo_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ Down_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ Blancos_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ Blastos_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ MO_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ SNC_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ Ploidia_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ Estirpe_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ RTA_PRED_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ CAT_ERM_NA <fct> NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, NA, NA, NA, !NA,~
## $ Edad_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ MLL_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ TIEMPOSG_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ SGSTATUS_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ Edad_cat_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ Blancos_cat_NA <fct> !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, !NA, ~
## $ TEL_NA <fct> !NA, NA, !NA, !NA, NA, !NA, !NA, NA, NA, !NA, !NA, !NA,~## [1] 0.2977099
## Warning: Use with(imp, glm(yourmodel).## term estimate std.error statistic df p.value
## 1 (Intercept) -1.3007824567 0.439605193 -2.9589788 1798.0207 3.126940e-03
## 2 Sexo1 0.0528324772 0.125672434 0.4203983 2070.3249 6.742381e-01
## 3 Down1 1.3294313553 0.370595340 3.5872857 2028.2466 3.420539e-04
## 4 MLL1 0.2933404553 0.591795702 0.4956786 2067.1251 6.201739e-01
## 5 RTA_PRED1 -0.6300229939 0.195031252 -3.2303694 1734.5375 1.259454e-03
## 6 EstirpeT -0.1338678892 0.211047902 -0.6343010 2064.1655 5.259547e-01
## 7 Edad 0.0241103446 0.013660319 1.7649913 1999.8605 7.771782e-02
## 8 TEL1 -0.5762202082 0.219948876 -2.6197916 1038.0977 8.926733e-03
## 9 Blancos 0.0032928612 0.000776925 4.2383258 2035.2579 2.352554e-05
## 10 MO -0.0052278182 0.004291177 -1.2182715 2047.3730 2.232612e-01
## 11 SNC2 0.1325226406 0.527806214 0.2510820 1994.8570 8.017766e-01
## 12 SNC3 0.4430398638 0.306363535 1.4461247 2071.0550 1.482936e-01
## 13 Ploidia2 -1.2226491648 0.208346585 -5.8683427 2072.1236 5.112339e-09
## 14 Ploidia4 1.3205602248 0.167215416 7.8973593 2070.0768 4.440892e-15
## 15 Ploidia5 -2.8068669981 1.035198470 -2.7114289 2071.1581 6.754730e-03
## 16 Blastos 0.0003344771 0.002084880 0.1604299 1938.6126 8.725591e-01
## 17 CAT_ERM2 0.4590747846 0.156960393 2.9247811 348.2371 3.672853e-03
## 18 CAT_ERM3 0.9860947469 0.205451455 4.7996484 285.7899 2.565611e-06## Warning: Use with(imp, glm(yourmodel).## term estimate std.error statistic df p.value
## 1 (Intercept) -1.26208331 0.437958070 -2.8817446 1694.6959 4.004599e-03
## 2 Sexo1 0.04899378 0.124543894 0.3933857 2072.7918 6.940751e-01
## 3 Down1 1.39049740 0.373925620 3.7186470 1995.9577 2.058186e-04
## 4 MLL1 0.24426628 0.583121942 0.4188940 2067.9673 6.753371e-01
## 5 RTA_PRED1 -0.73714835 0.186589926 -3.9506332 1693.6616 8.114415e-05
## 6 EstirpeT 0.30192226 0.187722543 1.6083431 2064.7640 1.079129e-01
## 7 TEL1 -0.65544778 0.217400163 -3.0149369 1039.0232 2.632755e-03
## 8 MO -0.00166119 0.004050626 -0.4101069 2046.7945 6.817705e-01
## 9 SNC2 0.28734335 0.508397001 0.5651948 1955.0885 5.720061e-01
## 10 SNC3 0.61996426 0.291573851 2.1262684 2073.5829 3.359874e-02
## 11 CAT_ERM2 0.47708443 0.155414077 3.0697633 355.6627 2.306936e-03
## 12 CAT_ERM3 1.07755125 0.204907401 5.2587230 238.6481 3.222466e-07
## 13 Ploidia2 -1.22634393 0.205553505 -5.9660570 2074.9933 2.850354e-09
## 14 Ploidia4 1.32664881 0.165069215 8.0369244 2073.1694 1.554312e-15
## 15 Ploidia5 -2.65753854 1.020358390 -2.6045148 2076.2030 9.266080e-03## Class: mipo m = 10
## term m estimate ubar b t dfcom
## 1 (Intercept) 10 -1.26208331 1.862100e-01 5.088400e-03 1.918073e-01 2081
## 2 Sexo1 10 0.04899378 1.547961e-02 2.870093e-05 1.551118e-02 2081
## 3 Down1 10 1.39049740 1.382252e-01 1.450147e-03 1.398204e-01 2081
## 4 MLL1 10 0.24426628 3.389761e-01 9.591450e-04 3.400312e-01 2081
## 5 RTA_PRED1 10 -0.73714835 3.379803e-02 9.252474e-04 3.481580e-02 2081
## 6 EstirpeT 10 0.30192226 3.510911e-02 1.187691e-04 3.523975e-02 2081
## 7 TEL1 10 -0.65544778 4.425906e-02 2.730699e-03 4.726283e-02 2081
## 8 MO 10 -0.00166119 1.630325e-05 9.483332e-08 1.640757e-05 2081
## 9 SNC2 10 0.28734335 2.547164e-01 3.410089e-03 2.584675e-01 2081
## 10 SNC3 10 0.61996426 8.485942e-02 1.417211e-04 8.501531e-02 2081
## 11 CAT_ERM2 10 0.47708443 2.071579e-02 3.125222e-03 2.415354e-02 2081
## 12 CAT_ERM3 10 1.07755125 3.442558e-02 6.874061e-03 4.198704e-02 2081
## 13 Ploidia2 10 -1.22634393 4.219111e-02 5.557140e-05 4.225224e-02 2081
## 14 Ploidia4 10 1.32664881 2.719498e-02 4.806337e-05 2.724785e-02 2081
## 15 Ploidia5 10 -2.65753854 1.040008e+00 1.020910e-03 1.041131e+00 2081
## df riv lambda fmi
## 1 1694.6959 0.030058746 0.029181584 0.030325273
## 2 2072.7918 0.002039523 0.002035372 0.002996899
## 3 1995.9577 0.011540313 0.011408654 0.012397760
## 4 2067.9673 0.003112489 0.003102831 0.004065567
## 5 1693.6616 0.030113359 0.029233053 0.030377379
## 6 2064.7640 0.003721142 0.003707346 0.004670989
## 7 1039.0232 0.067867886 0.063554572 0.065351932
## 8 2046.7945 0.006398517 0.006357836 0.007327341
## 9 1955.0885 0.014726565 0.014512841 0.015519422
## 10 2073.5829 0.001837076 0.001833707 0.002795062
## 11 355.6627 0.165947974 0.142328799 0.147111405
## 12 238.6481 0.219646794 0.180090494 0.186876474
## 13 2074.9933 0.001448849 0.001446753 0.002407827
## 14 2073.1694 0.001944098 0.001940326 0.002901770
## 15 2076.2030 0.001079800 0.001078635 0.002039505## Class: mipo m = 10
## term m estimate ubar b t dfcom
## 1 (Intercept) 10 -1.26208331 1.862100e-01 5.088400e-03 1.918073e-01 2081
## 2 Sexo1 10 0.04899378 1.547961e-02 2.870093e-05 1.551118e-02 2081
## 3 Down1 10 1.39049740 1.382252e-01 1.450147e-03 1.398204e-01 2081
## 4 MLL1 10 0.24426628 3.389761e-01 9.591450e-04 3.400312e-01 2081
## 5 RTA_PRED1 10 -0.73714835 3.379803e-02 9.252474e-04 3.481580e-02 2081
## 6 EstirpeT 10 0.30192226 3.510911e-02 1.187691e-04 3.523975e-02 2081
## 7 TEL1 10 -0.65544778 4.425906e-02 2.730699e-03 4.726283e-02 2081
## 8 MO 10 -0.00166119 1.630325e-05 9.483332e-08 1.640757e-05 2081
## 9 SNC2 10 0.28734335 2.547164e-01 3.410089e-03 2.584675e-01 2081
## 10 SNC3 10 0.61996426 8.485942e-02 1.417211e-04 8.501531e-02 2081
## 11 CAT_ERM2 10 0.47708443 2.071579e-02 3.125222e-03 2.415354e-02 2081
## 12 CAT_ERM3 10 1.07755125 3.442558e-02 6.874061e-03 4.198704e-02 2081
## 13 Ploidia2 10 -1.22634393 4.219111e-02 5.557140e-05 4.225224e-02 2081
## 14 Ploidia4 10 1.32664881 2.719498e-02 4.806337e-05 2.724785e-02 2081
## 15 Ploidia5 10 -2.65753854 1.040008e+00 1.020910e-03 1.041131e+00 2081
## df riv lambda fmi
## 1 1694.6959 0.030058746 0.029181584 0.030325273
## 2 2072.7918 0.002039523 0.002035372 0.002996899
## 3 1995.9577 0.011540313 0.011408654 0.012397760
## 4 2067.9673 0.003112489 0.003102831 0.004065567
## 5 1693.6616 0.030113359 0.029233053 0.030377379
## 6 2064.7640 0.003721142 0.003707346 0.004670989
## 7 1039.0232 0.067867886 0.063554572 0.065351932
## 8 2046.7945 0.006398517 0.006357836 0.007327341
## 9 1955.0885 0.014726565 0.014512841 0.015519422
## 10 2073.5829 0.001837076 0.001833707 0.002795062
## 11 355.6627 0.165947974 0.142328799 0.147111405
## 12 238.6481 0.219646794 0.180090494 0.186876474
## 13 2074.9933 0.001448849 0.001446753 0.002407827
## 14 2073.1694 0.001944098 0.001940326 0.002901770
## 15 2076.2030 0.001079800 0.001078635 0.002039505Random forest para seleccion de variables
## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:ggplot2':
##
## margin## List of 18
## $ call : language randomForest(formula = TIEMPOSG ~ CAT_ERM + Sexo + Down + RTA_PRED + Estirpe + Edad + TEL + Blancos + SNC + | __truncated__ ...
## $ type : chr "regression"
## $ predicted : Named num [1:2096] 59.6 30 48.2 62 67.8 ...
## ..- attr(*, "names")= chr [1:2096] "1" "2" "3" "4" ...
## $ mse : num [1:500] 1646 1444 1345 1349 1323 ...
## $ rsq : num [1:500] -0.457 -0.278 -0.19 -0.195 -0.171 ...
## $ oob.times : int [1:2096] 184 198 178 193 190 169 178 172 196 203 ...
## $ importance : num [1:11, 1:2] 16.959 0.932 -2.253 33.495 5.026 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:11] "CAT_ERM" "Sexo" "Down" "RTA_PRED" ...
## .. ..$ : chr [1:2] "%IncMSE" "IncNodePurity"
## $ importanceSD : Named num [1:11] 2.369 1.842 0.569 1.766 1.359 ...
## ..- attr(*, "names")= chr [1:11] "CAT_ERM" "Sexo" "Down" "RTA_PRED" ...
## $ localImportance: NULL
## $ proximity : NULL
## $ ntree : num 500
## $ mtry : num 3
## $ forest :List of 11
## ..$ ndbigtree : int [1:500] 1013 691 871 893 977 847 1017 1041 919 841 ...
## ..$ nodestatus : int [1:1125, 1:500] -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 ...
## ..$ leftDaughter : int [1:1125, 1:500] 2 4 6 8 10 12 14 16 18 20 ...
## ..$ rightDaughter: int [1:1125, 1:500] 3 5 7 9 11 13 15 17 19 21 ...
## ..$ nodepred : num [1:1125, 1:500] 51.4 52.9 36 38.9 54.1 ...
## ..$ bestvar : int [1:1125, 1:500] 8 4 6 8 8 10 11 1 1 11 ...
## ..$ xbestsplit : num [1:1125, 1:500] 122.5 1 11.5 110.5 1.5 ...
## ..$ ncat : Named int [1:11] 3 2 2 2 2 1 2 1 3 4 ...
## .. ..- attr(*, "names")= chr [1:11] "CAT_ERM" "Sexo" "Down" "RTA_PRED" ...
## ..$ nrnodes : int 1125
## ..$ ntree : num 500
## ..$ xlevels :List of 11
## .. ..$ CAT_ERM : chr [1:3] "1" "2" "3"
## .. ..$ Sexo : chr [1:2] "0" "1"
## .. ..$ Down : chr [1:2] "0" "1"
## .. ..$ RTA_PRED: chr [1:2] "0" "1"
## .. ..$ Estirpe : chr [1:2] "B" "T"
## .. ..$ Edad : num 0
## .. ..$ TEL : chr [1:2] "0" "1"
## .. ..$ Blancos : num 0
## .. ..$ SNC : chr [1:3] "1" "2" "3"
## .. ..$ Ploidia : chr [1:4] "1" "2" "4" "5"
## .. ..$ Blastos : num 0
## $ coefs : NULL
## $ y : Named num [1:2096] 81 16 67 131 28 69 37 86 67 36 ...
## ..- attr(*, "names")= chr [1:2096] "1" "2" "3" "4" ...
## $ test : NULL
## $ inbag : NULL
## $ terms :Classes 'terms', 'formula' language TIEMPOSG ~ CAT_ERM + Sexo + Down + RTA_PRED + Estirpe + Edad + TEL + Blancos + SNC + RTA_PRED + Ploidia + Blastos
## .. ..- attr(*, "variables")= language list(TIEMPOSG, CAT_ERM, Sexo, Down, RTA_PRED, Estirpe, Edad, TEL, Blancos, SNC, Ploidia, Blastos)
## .. ..- attr(*, "factors")= int [1:12, 1:11] 0 1 0 0 0 0 0 0 0 0 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:12] "TIEMPOSG" "CAT_ERM" "Sexo" "Down" ...
## .. .. .. ..$ : chr [1:11] "CAT_ERM" "Sexo" "Down" "RTA_PRED" ...
## .. ..- attr(*, "term.labels")= chr [1:11] "CAT_ERM" "Sexo" "Down" "RTA_PRED" ...
## .. ..- attr(*, "order")= int [1:11] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..- attr(*, "intercept")= num 0
## .. ..- attr(*, "response")= int 1
## .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
## .. ..- attr(*, "predvars")= language list(TIEMPOSG, CAT_ERM, Sexo, Down, RTA_PRED, Estirpe, Edad, TEL, Blancos, SNC, Ploidia, Blastos)
## .. ..- attr(*, "dataClasses")= Named chr [1:12] "numeric" "factor" "factor" "factor" ...
## .. .. ..- attr(*, "names")= chr [1:12] "TIEMPOSG" "CAT_ERM" "Sexo" "Down" ...
## - attr(*, "class")= chr [1:2] "randomForest.formula" "randomForest"## %IncMSE IncNodePurity
## CAT_ERM 16.9591729 81738.82
## Sexo 0.9315931 48819.71
## Down -2.2526670 15959.19
## RTA_PRED 33.4947526 50318.57
## Estirpe 5.0262561 27585.99
## Edad 4.0521685 243419.65
## TEL 2.8139730 30138.20
## Blancos 62.6043195 330429.83
## SNC 2.7122820 32780.21
## Ploidia 255.7426601 337653.91
## Blastos 40.2086143 312194.30## Warning in base::cbind(...): number of rows of result is not a multiple of
## vector length (arg 2)## 'data.frame': 17 obs. of 2 variables:
## $ w: chr "Sexo" "Down" "Blancos" "Blastos" ...
## $ v: chr "16.9591728930578" "0.931593088517574" "-2.25266700890893" "33.4947525579218" ...
-Regresion de COX con base de datos con imputacion
#models<-with(datos_imputados,coxph(Surv(TIEMPOSG,SGSTATUS)~ Down+RTA_PRED+Estirpe+Edad_cat+TEL+Blancos_cat+SNC+RTA_PRED+Ploidia+Blastos+CAT_ERM+MO+MLL))
#summary(pool(models))
#Comentario: no nos dio la regresion de cox con la base de datos imputada, trabajamos con la base sin imputar.-Regresion de COX con base de datos sin imputacion
library(survival)
library(ggplot2)
library(KMsurv)
library(ggfortify)
library ( survminer)## Loading required package: ggpubr##
## Attaching package: 'survminer'## The following object is masked from 'package:survival':
##
## myelomalibrary(survMisc)##
## Attaching package: 'survMisc'## The following object is masked from 'package:ggplot2':
##
## autoplotlibrary(base)
library(flexsurv)
library(coin)
library(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, last## The following object is masked from 'package:purrr':
##
## transposelibrary(Hmisc)## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following object is masked from 'package:arsenal':
##
## %nin%## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, units#Graficos
#CATEGORIAS DE ENFERMEDAD RESIDUAL
ckm<-survfit(Surv(TIEMPOSG, SGSTATUS) ~ CAT_ERM,data=datos, conf.type="log-log")
ggsurvplot(fit = ckm, data =datos, conf.int = T, title = "LLA",
xlab = "Tiempo", ylab = "Probabilidad de sobrevida", legend.title = "CAT_ERM",
legend.labs = c("Estandar", "Intermedio", "Elevado"), risk.table = "percentage", ncensor.plot = F, surv.median.line = "hv")## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, : Median
## survival not reached.
print(ckm, print.rmean=TRUE)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ CAT_ERM, data = datos,
## conf.type = "log-log")
##
## 354 observations deleted due to missingness
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## CAT_ERM=1 656 87 115.6 1.76 NA NA NA
## CAT_ERM=2 806 177 102.8 2.05 NA NA NA
## CAT_ERM=3 280 105 83.4 3.73 NA 67 NA
## * restricted mean with upper limit = 133#SEXO
ckm_sexo<-survfit(Surv(TIEMPOSG, SGSTATUS) ~ Sexo,data=datos, conf.type="log-log")
ggsurvplot(fit = ckm_sexo, data =datos, conf.int = T, title = "LLA",
xlab = "Tiempo", ylab = "Probabilidad de sobrevida", legend.title = "Sexo",
legend.labs = c("Femenino","Masculino"), risk.table = "percentage", ncensor.plot = F, surv.median.line = "hv")## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, : Median
## survival not reached.
print(ckm_sexo, print.rmean=TRUE)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ Sexo, data = datos,
## conf.type = "log-log")
##
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## Sexo=0 924 192 105 1.76 NA NA NA
## Sexo=1 1172 276 102 1.65 NA NA NA
## * restricted mean with upper limit = 133#Down
ckm_down<-survfit(Surv(TIEMPOSG, SGSTATUS) ~ Down,data=datos, conf.type="log-log")
ggsurvplot(fit = ckm_down, data =datos, conf.int = T, title = "LLA",
xlab = "Tiempo", ylab = "Probabilidad de sobrevida", legend.title = "Down",
legend.labs = c("No","Si"), risk.table = "percentage", ncensor.plot = F, surv.median.line = "hv")
print(ckm_down, print.rmean=TRUE)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down, data = datos,
## conf.type = "log-log")
##
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## Down=0 2052 445 104.2 1.21 NA NA NA
## Down=1 44 23 66.8 9.31 46 23 NA
## * restricted mean with upper limit = 133#SNC
ckm_snc<-survfit(Surv(TIEMPOSG, SGSTATUS) ~ SNC,data=datos, conf.type="log-log")
ggsurvplot(fit = ckm_snc, data =datos, conf.int = T, title = "LLA",
xlab = "Tiempo", ylab = "Probabilidad de sobrevida", legend.title = "SNC",
legend.labs = c("1","2","3"), risk.table = "percentage", ncensor.plot = F, surv.median.line = "hv")## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, : Median
## survival not reached.
print(ckm_snc, print.rmean=TRUE)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ SNC, data = datos,
## conf.type = "log-log")
##
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## SNC=1 1992 431 104.2 1.23 NA NA NA
## SNC=2 24 9 87.9 11.97 NA 12 NA
## SNC=3 80 28 87.1 6.88 NA 47 NA
## * restricted mean with upper limit = 133#Ploidia
ckm_ploidia<-survfit(Surv(TIEMPOSG, SGSTATUS) ~ Ploidia,data=datos, conf.type="log-log")
ggsurvplot(fit = ckm_ploidia, data =datos, conf.int = T, title = "LLA",
xlab = "Tiempo", ylab = "Probabilidad de sobrevida", legend.title = "Ploidia",
legend.labs = c("1","2","4","5"), risk.table = "percentage", ncensor.plot = F, surv.median.line = "hv")
print(ckm_ploidia, print.rmean=TRUE)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ Ploidia, data = datos,
## conf.type = "log-log")
##
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## Ploidia=1 340 63 106.4 3.07 NA NA NA
## Ploidia=2 902 53 124.7 1.31 NA NA NA
## Ploidia=4 778 351 73.7 2.26 65 52 97
## Ploidia=5 76 1 131.7 1.28 NA NA NA
## * restricted mean with upper limit = 133#Estirpe
ckm_estirpe<-survfit(Surv(TIEMPOSG, SGSTATUS) ~ Estirpe,data=datos, conf.type="log-log")
ggsurvplot(fit = ckm_estirpe, data =datos, conf.int = T, title = "LLA",
xlab = "Tiempo", ylab = "Probabilidad de sobrevida", legend.title = "Estirpe",
legend.labs = c("B","T"), risk.table = "percentage", ncensor.plot = F, surv.median.line = "hv")## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, : Median
## survival not reached.
print(ckm_estirpe, print.rmean=TRUE)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ Estirpe, data = datos,
## conf.type = "log-log")
##
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## Estirpe=B 1858 386 105.3 1.26 NA NA NA
## Estirpe=T 238 82 88.9 3.90 NA NA NA
## * restricted mean with upper limit = 133#RTA_pred
ckm_pred<-survfit(Surv(TIEMPOSG, SGSTATUS) ~ RTA_PRED,data=datos, conf.type="log-log")
ggsurvplot(fit = ckm_pred, data =datos, conf.int = T, title = "LLA",
xlab = "Tiempo", ylab = "Probabilidad de sobrevida", legend.title = "Rta Prednisona",
legend.labs = c("No","Si"), risk.table = "percentage", ncensor.plot = F, surv.median.line = "hv")
print(ckm_pred, print.rmean=TRUE)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ RTA_PRED, data = datos,
## conf.type = "log-log")
##
## 5 observations deleted due to missingness
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## RTA_PRED=0 231 106 71.9 4.36 49 30 NA
## RTA_PRED=1 1860 358 107.4 1.22 NA NA NA
## * restricted mean with upper limit = 133#MLL
ckm_mll<-survfit(Surv(TIEMPOSG, SGSTATUS) ~ MLL,data=datos, conf.type="log-log")
ggsurvplot(fit = ckm_mll, data =datos, conf.int = T, title = "LLA",
xlab = "Tiempo", ylab = "Probabilidad de sobrevida", legend.title = "MLL",
legend.labs = c("Ausente","Presente"), risk.table = "percentage", ncensor.plot = F, surv.median.line = "hv")## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, : Median
## survival not reached.
print(ckm_mll, print.rmean=TRUE)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ MLL, data = datos,
## conf.type = "log-log")
##
## 9 observations deleted due to missingness
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## MLL=0 2063 462 103 1.22 NA NA NA
## MLL=1 24 5 106 10.69 NA 55 NA
## * restricted mean with upper limit = 133#TEL
ckm_tel<-survfit(Surv(TIEMPOSG, SGSTATUS) ~ TEL,data=datos, conf.type="log-log")
ggsurvplot(fit = ckm_tel, data =datos, conf.int = T, title = "LLA",
xlab = "Tiempo", ylab = "Probabilidad de sobrevida", legend.title = "TEL",
legend.labs = c("Ausente","Presente"), risk.table = "percentage", ncensor.plot = F, surv.median.line = "hv")## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, : Median
## survival not reached.
print(ckm_tel, print.rmean=TRUE)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ TEL, data = datos,
## conf.type = "log-log")
##
## 401 observations deleted due to missingness
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## TEL=0 1473 362 100 1.51 NA NA NA
## TEL=1 222 29 116 2.90 NA NA NA
## * restricted mean with upper limit = 133#Edad cat
ckm_edad<-survfit(Surv(TIEMPOSG, SGSTATUS) ~ Edad_cat,data=datos, conf.type="log-log")
ggsurvplot(fit = ckm_edad, data =datos, conf.int = T, title = "LLA",
xlab = "Tiempo", ylab = "Probabilidad de sobrevida", legend.title = "Edad",
legend.labs = c("Menor a 6","Mayor a 6"), risk.table = "percentage", ncensor.plot = F, surv.median.line = "hv")## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, : Median
## survival not reached.
print(ckm_edad, print.rmean=TRUE)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ Edad_cat, data = datos,
## conf.type = "log-log")
##
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## Edad_cat=1 1125 210 108.5 1.52 NA NA NA
## Edad_cat=2 971 258 97.4 1.93 NA NA NA
## * restricted mean with upper limit = 133#Blancos cat
ckm_blancos<-survfit(Surv(TIEMPOSG, SGSTATUS) ~ Blancos_cat,data=datos, conf.type="log-log")
ggsurvplot(fit = ckm_blancos, data =datos, conf.int = T, title = "LLA",
xlab = "Tiempo", ylab = "Probabilidad de sobrevida", legend.title = "GB",
legend.labs = c("Menor a 20.000","Mayor a 20.000"), risk.table = "percentage", ncensor.plot = F, surv.median.line = "hv")## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, : Median
## survival not reached.
print(ckm_blancos, print.rmean=TRUE)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ Blancos_cat, data = datos,
## conf.type = "log-log")
##
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## Blancos_cat=1 1379 241 109.7 1.37 NA NA NA
## Blancos_cat=2 717 227 91.4 2.27 NA NA NA
## * restricted mean with upper limit = 133kmaids<-survfit(Surv(TIEMPOSG, SGSTATUS)~CAT_ERM,data=datos, type="kaplan-meier",conf.type = "log-log", conf.int = 0.95)
print(kmaids, print.rmean=T)## Call: survfit(formula = Surv(TIEMPOSG, SGSTATUS) ~ CAT_ERM, data = datos,
## type = "kaplan-meier", conf.type = "log-log", conf.int = 0.95)
##
## 354 observations deleted due to missingness
## n events rmean* se(rmean) median 0.95LCL 0.95UCL
## CAT_ERM=1 656 87 115.6 1.76 NA NA NA
## CAT_ERM=2 806 177 102.8 2.05 NA NA NA
## CAT_ERM=3 280 105 83.4 3.73 NA 67 NA
## * restricted mean with upper limit = 133#Modelos univariados
#Categorias de ERM
modelo_erm<- coxph( Surv(TIEMPOSG,SGSTATUS) ~ I(CAT_ERM=="2")+I(CAT_ERM=="3"), data=datos)
summary(modelo_erm)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ I(CAT_ERM == "2") +
## I(CAT_ERM == "3"), data = datos)
##
## n= 1742, number of events= 369
## (354 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## I(CAT_ERM == "2")TRUE 0.5966 1.8160 0.1310 4.555 5.25e-06 ***
## I(CAT_ERM == "3")TRUE 1.2912 3.6371 0.1452 8.893 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## I(CAT_ERM == "2")TRUE 1.816 0.5507 1.405 2.348
## I(CAT_ERM == "3")TRUE 3.637 0.2749 2.736 4.834
##
## Concordance= 0.628 (se = 0.014 )
## Likelihood ratio test= 77.78 on 2 df, p=<2e-16
## Wald test = 80.19 on 2 df, p=<2e-16
## Score (logrank) test = 87.6 on 2 df, p=<2e-16#Down
modelo_down<- coxph( Surv(TIEMPOSG,SGSTATUS) ~ Down, data=datos)
summary(modelo_down)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down, data = datos)
##
## n= 2096, number of events= 468
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down1 1.0702 2.9159 0.2139 5.004 5.63e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down1 2.916 0.3429 1.917 4.434
##
## Concordance= 0.516 (se = 0.005 )
## Likelihood ratio test= 18.5 on 1 df, p=2e-05
## Wald test = 25.04 on 1 df, p=6e-07
## Score (logrank) test = 27.52 on 1 df, p=2e-07#Modelo con SNC
modelo_snc<- coxph( Surv(TIEMPOSG, SGSTATUS)~SNC, data=datos)
summary(modelo_snc)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ SNC, data = datos)
##
## n= 2096, number of events= 468
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SNC2 0.5656 1.7606 0.3368 1.679 0.09311 .
## SNC3 0.5882 1.8008 0.1951 3.015 0.00257 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SNC2 1.761 0.5680 0.9098 3.407
## SNC3 1.801 0.5553 1.2286 2.640
##
## Concordance= 0.519 (se = 0.006 )
## Likelihood ratio test= 9.86 on 2 df, p=0.007
## Wald test = 11.57 on 2 df, p=0.003
## Score (logrank) test = 11.9 on 2 df, p=0.003#Modelo con Globulos blancos
modelo_blancos<- coxph( Surv(TIEMPOSG, SGSTATUS)~Blancos, data=datos)
summary(modelo_blancos)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Blancos, data = datos)
##
## n= 2096, number of events= 468
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Blancos 0.0030767 1.0030814 0.0002609 11.79 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Blancos 1.003 0.9969 1.003 1.004
##
## Concordance= 0.615 (se = 0.014 )
## Likelihood ratio test= 82.27 on 1 df, p=<2e-16
## Wald test = 139.1 on 1 df, p=<2e-16
## Score (logrank) test = 153.8 on 1 df, p=<2e-16#Modelo con MO
modelo_mo<-coxph( Surv(TIEMPOSG, SGSTATUS)~MO, data=datos)
summary(modelo_mo)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ MO, data = datos)
##
## n= 2096, number of events= 468
##
## coef exp(coef) se(coef) z Pr(>|z|)
## MO -0.0000736 0.9999264 0.0031069 -0.024 0.981
##
## exp(coef) exp(-coef) lower .95 upper .95
## MO 0.9999 1 0.9939 1.006
##
## Concordance= 0.482 (se = 0.014 )
## Likelihood ratio test= 0 on 1 df, p=1
## Wald test = 0 on 1 df, p=1
## Score (logrank) test = 0 on 1 df, p=1#Modelo con Blastos
modelo_blastos<- coxph( Surv(TIEMPOSG, SGSTATUS)~Blastos, data=datos)
summary(modelo_blastos)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Blastos, data = datos)
##
## n= 2083, number of events= 466
## (13 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Blastos 0.007007 1.007031 0.001343 5.217 1.82e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Blastos 1.007 0.993 1.004 1.01
##
## Concordance= 0.58 (se = 0.014 )
## Likelihood ratio test= 27.78 on 1 df, p=1e-07
## Wald test = 27.22 on 1 df, p=2e-07
## Score (logrank) test = 27.64 on 1 df, p=1e-07#Modelo con TEL
modelo_tel<- coxph( Surv(TIEMPOSG, SGSTATUS)~TEL, data=datos)
summary(modelo_tel)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ TEL, data = datos)
##
## n= 1695, number of events= 391
## (401 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## TEL1 -0.7011 0.4960 0.1930 -3.633 0.00028 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## TEL1 0.496 2.016 0.3398 0.7241
##
## Concordance= 0.53 (se = 0.008 )
## Likelihood ratio test= 16.15 on 1 df, p=6e-05
## Wald test = 13.2 on 1 df, p=3e-04
## Score (logrank) test = 13.75 on 1 df, p=2e-04#Modelo con MLL
modelo_mll<- coxph( Surv(TIEMPOSG, SGSTATUS)~MLL, data=datos)
summary(modelo_mll)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ MLL, data = datos)
##
## n= 2087, number of events= 467
## (9 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## MLL1 -0.1211 0.8859 0.4497 -0.269 0.788
##
## exp(coef) exp(-coef) lower .95 upper .95
## MLL1 0.8859 1.129 0.367 2.139
##
## Concordance= 0.501 (se = 0.003 )
## Likelihood ratio test= 0.08 on 1 df, p=0.8
## Wald test = 0.07 on 1 df, p=0.8
## Score (logrank) test = 0.07 on 1 df, p=0.8#Modelo con Estirpe
modelo_estirpe<- coxph( Surv(TIEMPOSG, SGSTATUS)~Estirpe, data=datos)
summary(modelo_estirpe)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Estirpe, data = datos)
##
## n= 2096, number of events= 468
##
## coef exp(coef) se(coef) z Pr(>|z|)
## EstirpeT 0.6344 1.8858 0.1217 5.213 1.86e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## EstirpeT 1.886 0.5303 1.486 2.394
##
## Concordance= 0.544 (se = 0.009 )
## Likelihood ratio test= 23.62 on 1 df, p=1e-06
## Wald test = 27.17 on 1 df, p=2e-07
## Score (logrank) test = 28.1 on 1 df, p=1e-07#Modelo con Ploidia
modelo_ploidia<- coxph( Surv(TIEMPOSG, SGSTATUS)~Ploidia, data=datos)
summary(modelo_ploidia)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Ploidia, data = datos)
##
## n= 2096, number of events= 468
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Ploidia2 -1.3654 0.2553 0.1869 -7.306 2.74e-13 ***
## Ploidia4 0.9572 2.6044 0.1369 6.990 2.75e-12 ***
## Ploidia5 -3.1259 0.0439 1.0084 -3.100 0.00194 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Ploidia2 0.2553 3.917 0.176982 0.3682
## Ploidia4 2.6044 0.384 1.991335 3.4061
## Ploidia5 0.0439 22.781 0.006082 0.3168
##
## Concordance= 0.744 (se = 0.009 )
## Likelihood ratio test= 433.8 on 3 df, p=<2e-16
## Wald test = 284.4 on 3 df, p=<2e-16
## Score (logrank) test = 433.4 on 3 df, p=<2e-16#Modelo con respuesta a la Prednisona
modelo_pred<- coxph( Surv(TIEMPOSG, SGSTATUS)~RTA_PRED, data=datos)
summary(modelo_pred)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ RTA_PRED, data = datos)
##
## n= 2091, number of events= 464
## (5 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## RTA_PRED1 -1.1487 0.3170 0.1111 -10.34 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## RTA_PRED1 0.317 3.154 0.255 0.3942
##
## Concordance= 0.578 (se = 0.01 )
## Likelihood ratio test= 85.64 on 1 df, p=<2e-16
## Wald test = 107 on 1 df, p=<2e-16
## Score (logrank) test = 119.1 on 1 df, p=<2e-16#Modelo con Sexo
modelo_sexo<- coxph( Surv(TIEMPOSG, SGSTATUS)~Sexo, data=datos)
summary(modelo_sexo)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Sexo, data = datos)
##
## n= 2096, number of events= 468
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Sexo1 0.12922 1.13794 0.09398 1.375 0.169
##
## exp(coef) exp(-coef) lower .95 upper .95
## Sexo1 1.138 0.8788 0.9465 1.368
##
## Concordance= 0.515 (se = 0.012 )
## Likelihood ratio test= 1.9 on 1 df, p=0.2
## Wald test = 1.89 on 1 df, p=0.2
## Score (logrank) test = 1.89 on 1 df, p=0.2#Modelo con edad como VA categorica
modelo_edad_cat<- coxph( Surv(TIEMPOSG, SGSTATUS)~Edad_cat, data=datos)
summary(modelo_edad_cat)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Edad_cat, data = datos)
##
## n= 2096, number of events= 468
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Edad_cat2 0.42730 1.53311 0.09299 4.595 4.32e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Edad_cat2 1.533 0.6523 1.278 1.84
##
## Concordance= 0.555 (se = 0.012 )
## Likelihood ratio test= 21.27 on 1 df, p=4e-06
## Wald test = 21.12 on 1 df, p=4e-06
## Score (logrank) test = 21.44 on 1 df, p=4e-06#Modelo con globulos blancos como VA categorica
modelo_gb_cat<- coxph( Surv(TIEMPOSG, SGSTATUS)~Blancos_cat, data=datos)
summary(modelo_gb_cat)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Blancos_cat, data = datos)
##
## n= 2096, number of events= 468
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Blancos_cat2 0.70475 2.02333 0.09256 7.614 2.66e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Blancos_cat2 2.023 0.4942 1.688 2.426
##
## Concordance= 0.592 (se = 0.012 )
## Likelihood ratio test= 56.43 on 1 df, p=6e-14
## Wald test = 57.97 on 1 df, p=3e-14
## Score (logrank) test = 60.4 on 1 df, p=8e-15#Modelo con edad como VA continua
modelo_edad<- coxph( Surv(TIEMPOSG, SGSTATUS)~Edad, data=datos)
summary(modelo_edad)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Edad, data = datos)
##
## n= 2096, number of events= 468
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Edad 0.049639 1.050891 0.009922 5.003 5.65e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Edad 1.051 0.9516 1.031 1.072
##
## Concordance= 0.557 (se = 0.015 )
## Likelihood ratio test= 23.94 on 1 df, p=1e-06
## Wald test = 25.03 on 1 df, p=6e-07
## Score (logrank) test = 25.31 on 1 df, p=5e-07#Modelo con globulos blancos como VA continua
modelo_blancos<- coxph( Surv(TIEMPOSG, SGSTATUS)~Blancos, data=datos)
summary(modelo_blancos)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Blancos, data = datos)
##
## n= 2096, number of events= 468
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Blancos 0.0030767 1.0030814 0.0002609 11.79 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Blancos 1.003 0.9969 1.003 1.004
##
## Concordance= 0.615 (se = 0.014 )
## Likelihood ratio test= 82.27 on 1 df, p=<2e-16
## Wald test = 139.1 on 1 df, p=<2e-16
## Score (logrank) test = 153.8 on 1 df, p=<2e-16#Test de Cox Mantel para comparacion de supervivencia en los tres grupos de ERM
summary(coxph( Surv(TIEMPOSG,SGSTATUS) ~ I(CAT_ERM=="2")+I(CAT_ERM=="3"), data=datos))## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ I(CAT_ERM == "2") +
## I(CAT_ERM == "3"), data = datos)
##
## n= 1742, number of events= 369
## (354 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## I(CAT_ERM == "2")TRUE 0.5966 1.8160 0.1310 4.555 5.25e-06 ***
## I(CAT_ERM == "3")TRUE 1.2912 3.6371 0.1452 8.893 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## I(CAT_ERM == "2")TRUE 1.816 0.5507 1.405 2.348
## I(CAT_ERM == "3")TRUE 3.637 0.2749 2.736 4.834
##
## Concordance= 0.628 (se = 0.014 )
## Likelihood ratio test= 77.78 on 2 df, p=<2e-16
## Wald test = 80.19 on 2 df, p=<2e-16
## Score (logrank) test = 87.6 on 2 df, p=<2e-16print(summary(coxph( Surv(TIEMPOSG,SGSTATUS) ~ I(CAT_ERM=="2")+I(CAT_ERM=="3"), data=datos)))## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ I(CAT_ERM == "2") +
## I(CAT_ERM == "3"), data = datos)
##
## n= 1742, number of events= 369
## (354 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## I(CAT_ERM == "2")TRUE 0.5966 1.8160 0.1310 4.555 5.25e-06 ***
## I(CAT_ERM == "3")TRUE 1.2912 3.6371 0.1452 8.893 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## I(CAT_ERM == "2")TRUE 1.816 0.5507 1.405 2.348
## I(CAT_ERM == "3")TRUE 3.637 0.2749 2.736 4.834
##
## Concordance= 0.628 (se = 0.014 )
## Likelihood ratio test= 77.78 on 2 df, p=<2e-16
## Wald test = 80.19 on 2 df, p=<2e-16
## Score (logrank) test = 87.6 on 2 df, p=<2e-16Tabla con HR de modelos univariados
datatable(outc, caption = "Resultados del analisis univariado", editable = "cell", extensions = "Buttons",
options = list(dom = "Bfrtip",
buttons =c("copy", "csv", "pdf", "print")))print(outc) %>%
kbl()%>%
kable_paper() HR Low .95 Upp .95 Pv| HR | Low .95 | Upp .95 | Pv | |
|---|---|---|---|---|
| CAT_ERM2 | 1.816 | 1.405 | 2.3480 | 0.000 |
| CAT_ERM3 | 3.637 | 2.736 | 4.8340 | 0.000 |
| SNC2 | 1.761 | 0.910 | 3.4070 | 0.093 |
| SNC3 | 1.801 | 1.229 | 2.6400 | 0.003 |
| Globulos Blancos | 2.023 | 1.688 | 2.4260 | 0.000 |
| Blastos en MO | 1.000 | 0.994 | 1.0060 | 0.981 |
| Blastos en SP | 1.007 | 1.005 | 1.0100 | 0.000 |
| Tel1 | 0.496 | 0.340 | 0.7240 | 0.000 |
| MLL1 | 0.886 | 0.367 | 2.1390 | 0.788 |
| Estirpe T | 1.886 | 1.486 | 2.3940 | 0.000 |
| Ploidia2 | 0.255 | 0.177 | 0.3680 | 0.000 |
| Ploidia4 | 2.604 | 1.991 | 3.4060 | 0.000 |
| Ploidia5 | 0.044 | 0.006 | 0.3160 | 0.002 |
| Respuesta a Prednisona | 0.317 | 0.255 | 0.3942 | 0.000 |
| Sexo1 | 1.138 | 0.947 | 1.3680 | 0.169 |
| Edad | 1.531 | 1.278 | 1.8400 | 0.000 |
| Down | 2.916 | 1.917 | 4.4340 | 0.000 |
De los analisis univariados efectuados, se excluyen para el analisis multivariado aquellas variables con pv> 0.2, MLL y MO.
#Modelo multivariado
#Modelo multivariado sin MLL ni MO (dan pv>0.1) utilizando Edad y Globulos blancos como VA categoricas
#con edad y gb como variables categoricas
modelo_mult<- coxph(Surv(TIEMPOSG, SGSTATUS)~Sexo + Down + RTA_PRED + Estirpe+ Edad_cat + TEL+ Blancos_cat + SNC+RTA_PRED +Ploidia + Blastos+ CAT_ERM, data=datos)
summary(modelo_mult)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Sexo + Down + RTA_PRED +
## Estirpe + Edad_cat + TEL + Blancos_cat + SNC + RTA_PRED +
## Ploidia + Blastos + CAT_ERM, data = datos)
##
## n= 1480, number of events= 322
## (616 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Sexo1 0.0218378 1.0220780 0.1155556 0.189 0.85011
## Down1 1.1497799 3.1574980 0.2777192 4.140 3.47e-05 ***
## RTA_PRED1 -0.5089549 0.6011235 0.1590766 -3.199 0.00138 **
## EstirpeT 0.0468603 1.0479756 0.1636782 0.286 0.77465
## Edad_cat2 0.0790463 1.0822544 0.1165434 0.678 0.49761
## TEL1 -0.4917902 0.6115307 0.2141550 -2.296 0.02165 *
## Blancos_cat2 0.3682409 1.4451901 0.1490434 2.471 0.01349 *
## SNC2 0.4035746 1.4971670 0.4168909 0.968 0.33302
## SNC3 0.5695412 1.7674559 0.2197318 2.592 0.00954 **
## Ploidia2 -1.3034923 0.2715817 0.2175331 -5.992 2.07e-09 ***
## Ploidia4 0.9800643 2.6646276 0.1535633 6.382 1.75e-10 ***
## Ploidia5 -2.0791775 0.1250330 1.0106309 -2.057 0.03966 *
## Blastos -0.0005152 0.9994850 0.0020302 -0.254 0.79968
## CAT_ERM2 0.3756233 1.4558985 0.1438508 2.611 0.00902 **
## CAT_ERM3 0.7632108 2.1451529 0.1747163 4.368 1.25e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Sexo1 1.0221 0.9784 0.81493 1.2819
## Down1 3.1575 0.3167 1.83210 5.4417
## RTA_PRED1 0.6011 1.6636 0.44011 0.8211
## EstirpeT 1.0480 0.9542 0.76038 1.4444
## Edad_cat2 1.0823 0.9240 0.86125 1.3600
## TEL1 0.6115 1.6352 0.40191 0.9305
## Blancos_cat2 1.4452 0.6920 1.07909 1.9355
## SNC2 1.4972 0.6679 0.66132 3.3894
## SNC3 1.7675 0.5658 1.14898 2.7188
## Ploidia2 0.2716 3.6821 0.17731 0.4160
## Ploidia4 2.6646 0.3753 1.97207 3.6004
## Ploidia5 0.1250 7.9979 0.01725 0.9063
## Blastos 0.9995 1.0005 0.99552 1.0035
## CAT_ERM2 1.4559 0.6869 1.09821 1.9301
## CAT_ERM3 2.1452 0.4662 1.52314 3.0212
##
## Concordance= 0.798 (se = 0.011 )
## Likelihood ratio test= 397.3 on 15 df, p=<2e-16
## Wald test = 323.8 on 15 df, p=<2e-16
## Score (logrank) test = 433.3 on 15 df, p=<2e-16library(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=stargazer#VIF del modelo
library(car)
vif_modelo<- vif(modelo_mult)## Warning in vif.default(modelo_mult): No intercept: vifs may not be sensible.print(vif_modelo)## GVIF Df GVIF^(1/(2*Df))
## Sexo 1.037140 1 1.018401
## Down 1.093849 1 1.045872
## RTA_PRED 1.453125 1 1.205456
## Estirpe 1.287930 1 1.134870
## Edad_cat 1.081776 1 1.040085
## TEL 1.056968 1 1.028089
## Blancos_cat 1.779789 1 1.334087
## SNC 1.086985 2 1.021071
## Ploidia 1.026765 3 1.004412
## Blastos 1.726685 1 1.314034
## CAT_ERM 1.349149 2 1.077742En el analisis del VIF, se observa que los valores son menores a 5, con lo cual no existe problema de colinealidad entre las variables explicativas.
Seleccion de modelos
#Modelo multivariado eliminando las variables no significativas (Sexo, Edad, Estirpe,SNC y Blastos)
modelo1<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Down + RTA_PRED + TEL+ Blancos_cat+RTA_PRED +Ploidia+CAT_ERM, data=datos)
summary(modelo1)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down + RTA_PRED +
## TEL + Blancos_cat + RTA_PRED + Ploidia + CAT_ERM, data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down1 1.1402 3.1275 0.2671 4.269 1.97e-05 ***
## RTA_PRED1 -0.5322 0.5873 0.1525 -3.490 0.000483 ***
## TEL1 -0.5378 0.5840 0.2089 -2.574 0.010048 *
## Blancos_cat2 0.3504 1.4196 0.1186 2.955 0.003129 **
## Ploidia2 -1.2905 0.2751 0.2158 -5.980 2.23e-09 ***
## Ploidia4 0.9684 2.6337 0.1528 6.339 2.31e-10 ***
## Ploidia5 -2.1305 0.1188 1.0102 -2.109 0.034950 *
## CAT_ERM2 0.3673 1.4438 0.1421 2.584 0.009765 **
## CAT_ERM3 0.7815 2.1847 0.1727 4.526 6.03e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down1 3.1275 0.3197 1.8528 5.2791
## RTA_PRED1 0.5873 1.7027 0.4356 0.7919
## TEL1 0.5840 1.7123 0.3878 0.8796
## Blancos_cat2 1.4196 0.7044 1.1252 1.7911
## Ploidia2 0.2751 3.6346 0.1802 0.4200
## Ploidia4 2.6337 0.3797 1.9522 3.5530
## Ploidia5 0.1188 8.4193 0.0164 0.8603
## CAT_ERM2 1.4438 0.6926 1.0928 1.9076
## CAT_ERM3 2.1847 0.4577 1.5574 3.0646
##
## Concordance= 0.796 (se = 0.011 )
## Likelihood ratio test= 389.5 on 9 df, p=<2e-16
## Wald test = 318.6 on 9 df, p=<2e-16
## Score (logrank) test = 426.3 on 9 df, p=<2e-16#Modelo multivariado eliminando Sexo
modelo2<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Down + Estirpe+ Edad_cat + TEL+ Blancos_cat + SNC+RTA_PRED +Ploidia + Blastos+CAT_ERM, data=datos)
summary(modelo2)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down + Estirpe + Edad_cat +
## TEL + Blancos_cat + SNC + RTA_PRED + Ploidia + Blastos +
## CAT_ERM, data = datos)
##
## n= 1480, number of events= 322
## (616 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down1 1.1451578 3.1429371 0.2767197 4.138 3.50e-05 ***
## EstirpeT 0.0502630 1.0515476 0.1627324 0.309 0.75742
## Edad_cat2 0.0792828 1.0825104 0.1165453 0.680 0.49633
## TEL1 -0.4921581 0.6113057 0.2141752 -2.298 0.02157 *
## Blancos_cat2 0.3673185 1.4438577 0.1489792 2.466 0.01368 *
## SNC2 0.4074230 1.5029397 0.4164104 0.978 0.32787
## SNC3 0.5677835 1.7643520 0.2195504 2.586 0.00971 **
## RTA_PRED1 -0.5105466 0.6001674 0.1588015 -3.215 0.00130 **
## Ploidia2 -1.3038005 0.2714980 0.2175276 -5.994 2.05e-09 ***
## Ploidia4 0.9805425 2.6659022 0.1535390 6.386 1.70e-10 ***
## Ploidia5 -2.0819336 0.1246889 1.0105274 -2.060 0.03938 *
## Blastos -0.0005078 0.9994924 0.0020295 -0.250 0.80244
## CAT_ERM2 0.3764078 1.4570412 0.1437982 2.618 0.00885 **
## CAT_ERM3 0.7633816 2.1455192 0.1746841 4.370 1.24e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down1 3.1429 0.3182 1.82722 5.4060
## EstirpeT 1.0515 0.9510 0.76438 1.4466
## Edad_cat2 1.0825 0.9238 0.86145 1.3603
## TEL1 0.6113 1.6358 0.40175 0.9302
## Blancos_cat2 1.4439 0.6926 1.07823 1.9335
## SNC2 1.5029 0.6654 0.66450 3.3993
## SNC3 1.7644 0.5668 1.14737 2.7131
## RTA_PRED1 0.6002 1.6662 0.43964 0.8193
## Ploidia2 0.2715 3.6833 0.17726 0.4158
## Ploidia4 2.6659 0.3751 1.97311 3.6019
## Ploidia5 0.1247 8.0200 0.01721 0.9036
## Blastos 0.9995 1.0005 0.99552 1.0035
## CAT_ERM2 1.4570 0.6863 1.09918 1.9314
## CAT_ERM3 2.1455 0.4661 1.52349 3.0215
##
## Concordance= 0.798 (se = 0.011 )
## Likelihood ratio test= 397.2 on 14 df, p=<2e-16
## Wald test = 323.9 on 14 df, p=<2e-16
## Score (logrank) test = 433.2 on 14 df, p=<2e-16#Modelo multivariado eliminando Estirpe
modelo3<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Sexo+ Down + Edad_cat + TEL+ Blancos_cat + SNC+RTA_PRED +Ploidia + Blastos+CAT_ERM, data=datos)
summary(modelo3)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Sexo + Down + Edad_cat +
## TEL + Blancos_cat + SNC + RTA_PRED + Ploidia + Blastos +
## CAT_ERM, data = datos)
##
## n= 1480, number of events= 322
## (616 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Sexo1 0.0254203 1.0257462 0.1148676 0.221 0.824858
## Down1 1.1411225 3.1302801 0.2761926 4.132 3.60e-05 ***
## Edad_cat2 0.0831728 1.0867296 0.1156411 0.719 0.471998
## TEL1 -0.4963256 0.6087634 0.2135307 -2.324 0.020105 *
## Blancos_cat2 0.3779025 1.4592207 0.1451270 2.604 0.009216 **
## SNC2 0.4012447 1.4936828 0.4168387 0.963 0.335753
## SNC3 0.5798485 1.7857679 0.2166488 2.676 0.007441 **
## RTA_PRED1 -0.5181346 0.5956306 0.1556336 -3.329 0.000871 ***
## Ploidia2 -1.3018167 0.2720371 0.2174513 -5.987 2.14e-09 ***
## Ploidia4 0.9795284 2.6631999 0.1535418 6.380 1.78e-10 ***
## Ploidia5 -2.0788620 0.1250725 1.0106314 -2.057 0.039687 *
## Blastos -0.0005538 0.9994464 0.0020255 -0.273 0.784551
## CAT_ERM2 0.3752352 1.4553336 0.1438311 2.609 0.009084 **
## CAT_ERM3 0.7646356 2.1482115 0.1746344 4.378 1.20e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Sexo1 1.0257 0.9749 0.81896 1.2847
## Down1 3.1303 0.3195 1.82175 5.3787
## Edad_cat2 1.0867 0.9202 0.86634 1.3632
## TEL1 0.6088 1.6427 0.40058 0.9251
## Blancos_cat2 1.4592 0.6853 1.09797 1.9393
## SNC2 1.4937 0.6695 0.65985 3.3812
## SNC3 1.7858 0.5600 1.16792 2.7305
## RTA_PRED1 0.5956 1.6789 0.43904 0.8081
## Ploidia2 0.2720 3.6760 0.17764 0.4166
## Ploidia4 2.6632 0.3755 1.97110 3.5983
## Ploidia5 0.1251 7.9954 0.01725 0.9066
## Blastos 0.9994 1.0006 0.99549 1.0034
## CAT_ERM2 1.4553 0.6871 1.09783 1.9293
## CAT_ERM3 2.1482 0.4655 1.52556 3.0250
##
## Concordance= 0.798 (se = 0.011 )
## Likelihood ratio test= 397.2 on 14 df, p=<2e-16
## Wald test = 323.8 on 14 df, p=<2e-16
## Score (logrank) test = 433.1 on 14 df, p=<2e-16#Modelo multivariado eliminando Edad
modelo4<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Sexo + Down + Estirpe + TEL+ Blancos_cat + SNC+RTA_PRED +Ploidia + Blastos+ CAT_ERM, data=datos)
summary(modelo4)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Sexo + Down + Estirpe +
## TEL + Blancos_cat + SNC + RTA_PRED + Ploidia + Blastos +
## CAT_ERM, data = datos)
##
## n= 1480, number of events= 322
## (616 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Sexo1 0.0226349 1.0228930 0.1154256 0.196 0.84453
## Down1 1.1718308 3.2278970 0.2755991 4.252 2.12e-05 ***
## EstirpeT 0.0607311 1.0626131 0.1622829 0.374 0.70823
## TEL1 -0.5135302 0.5983795 0.2117149 -2.426 0.01528 *
## Blancos_cat2 0.3685555 1.4456448 0.1487169 2.478 0.01320 *
## SNC2 0.4072543 1.5026861 0.4166763 0.977 0.32838
## SNC3 0.5657795 1.7608198 0.2195930 2.576 0.00998 **
## RTA_PRED1 -0.5004293 0.6062704 0.1585387 -3.157 0.00160 **
## Ploidia2 -1.3009271 0.2722793 0.2174983 -5.981 2.21e-09 ***
## Ploidia4 0.9876222 2.6848428 0.1531226 6.450 1.12e-10 ***
## Ploidia5 -2.0723988 0.1258834 1.0105702 -2.051 0.04029 *
## Blastos -0.0004993 0.9995008 0.0020269 -0.246 0.80540
## CAT_ERM2 0.3783134 1.4598203 0.1437877 2.631 0.00851 **
## CAT_ERM3 0.7742492 2.1689631 0.1739567 4.451 8.55e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Sexo1 1.0229 0.9776 0.81579 1.2826
## Down1 3.2279 0.3098 1.88074 5.5400
## EstirpeT 1.0626 0.9411 0.77311 1.4605
## TEL1 0.5984 1.6712 0.39515 0.9061
## Blancos_cat2 1.4456 0.6917 1.08012 1.9349
## SNC2 1.5027 0.6655 0.66404 3.4005
## SNC3 1.7608 0.5679 1.14498 2.7079
## RTA_PRED1 0.6063 1.6494 0.44434 0.8272
## Ploidia2 0.2723 3.6727 0.17778 0.4170
## Ploidia4 2.6848 0.3725 1.98875 3.6246
## Ploidia5 0.1259 7.9439 0.01737 0.9124
## Blastos 0.9995 1.0005 0.99554 1.0035
## CAT_ERM2 1.4598 0.6850 1.10130 1.9350
## CAT_ERM3 2.1690 0.4610 1.54234 3.0502
##
## Concordance= 0.797 (se = 0.011 )
## Likelihood ratio test= 396.8 on 14 df, p=<2e-16
## Wald test = 322.5 on 14 df, p=<2e-16
## Score (logrank) test = 432.6 on 14 df, p=<2e-16#Modelo multivariado eliminando SNC
modelo5<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Sexo + Down +Edad_cat + Estirpe + TEL+ Blancos_cat + Sexo+RTA_PRED +Ploidia + Blastos+ CAT_ERM, data=datos)
summary(modelo5)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Sexo + Down + Edad_cat +
## Estirpe + TEL + Blancos_cat + Sexo + RTA_PRED + Ploidia +
## Blastos + CAT_ERM, data = datos)
##
## n= 1480, number of events= 322
## (616 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Sexo1 0.0159680 1.0160962 0.1154188 0.138 0.88996
## Down1 1.1363231 3.1152927 0.2772467 4.099 4.16e-05 ***
## Edad_cat2 0.0740977 1.0769120 0.1164118 0.637 0.52444
## EstirpeT 0.1010669 1.1063506 0.1606465 0.629 0.52927
## TEL1 -0.4962636 0.6088011 0.2130934 -2.329 0.01987 *
## Blancos_cat2 0.3420055 1.4077681 0.1481737 2.308 0.02099 *
## RTA_PRED1 -0.5140603 0.5980623 0.1580324 -3.253 0.00114 **
## Ploidia2 -1.3135302 0.2688692 0.2177047 -6.034 1.60e-09 ***
## Ploidia4 0.9628862 2.6192452 0.1533775 6.278 3.43e-10 ***
## Ploidia5 -2.1275802 0.1191252 1.0104217 -2.106 0.03524 *
## Blastos -0.0001017 0.9998983 0.0020149 -0.050 0.95976
## CAT_ERM2 0.3613830 1.4353131 0.1435502 2.517 0.01182 *
## CAT_ERM3 0.7627497 2.1441638 0.1740849 4.381 1.18e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Sexo1 1.0161 0.9842 0.81038 1.2740
## Down1 3.1153 0.3210 1.80928 5.3640
## Edad_cat2 1.0769 0.9286 0.85722 1.3529
## EstirpeT 1.1064 0.9039 0.80751 1.5158
## TEL1 0.6088 1.6426 0.40095 0.9244
## Blancos_cat2 1.4078 0.7103 1.05294 1.8822
## RTA_PRED1 0.5981 1.6721 0.43876 0.8152
## Ploidia2 0.2689 3.7193 0.17548 0.4120
## Ploidia4 2.6192 0.3818 1.93919 3.5378
## Ploidia5 0.1191 8.3945 0.01644 0.8631
## Blastos 0.9999 1.0001 0.99596 1.0039
## CAT_ERM2 1.4353 0.6967 1.08332 1.9017
## CAT_ERM3 2.1442 0.4664 1.52432 3.0161
##
## Concordance= 0.798 (se = 0.011 )
## Likelihood ratio test= 390.7 on 13 df, p=<2e-16
## Wald test = 318.8 on 13 df, p=<2e-16
## Score (logrank) test = 427.8 on 13 df, p=<2e-16#Modelo multivariado eliminando Blastos
modelo6<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Sexo + Down +Edad_cat + Estirpe + TEL+ Blancos_cat + Sexo+RTA_PRED +Ploidia + SNC+ CAT_ERM, data=datos)
summary(modelo6)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Sexo + Down + Edad_cat +
## Estirpe + TEL + Blancos_cat + Sexo + RTA_PRED + Ploidia +
## SNC + CAT_ERM, data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Sexo1 0.03262 1.03316 0.11527 0.283 0.77717
## Down1 1.16845 3.21701 0.27173 4.300 1.71e-05 ***
## Edad_cat2 0.07960 1.08285 0.11611 0.686 0.49299
## EstirpeT 0.04624 1.04733 0.16336 0.283 0.77714
## TEL1 -0.50002 0.60652 0.21345 -2.343 0.01915 *
## Blancos_cat2 0.33786 1.40194 0.12204 2.768 0.00563 **
## RTA_PRED1 -0.50687 0.60238 0.15753 -3.218 0.00129 **
## Ploidia2 -1.28816 0.27578 0.21577 -5.970 2.37e-09 ***
## Ploidia4 0.97796 2.65903 0.15346 6.373 1.86e-10 ***
## Ploidia5 -2.08609 0.12417 1.01060 -2.064 0.03900 *
## SNC2 0.40688 1.50212 0.41597 0.978 0.32800
## SNC3 0.56203 1.75423 0.21818 2.576 0.01000 **
## CAT_ERM2 0.37808 1.45948 0.14257 2.652 0.00801 **
## CAT_ERM3 0.76758 2.15454 0.17399 4.412 1.03e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Sexo1 1.0332 0.9679 0.82423 1.2951
## Down1 3.2170 0.3108 1.88866 5.4796
## Edad_cat2 1.0829 0.9235 0.86246 1.3596
## EstirpeT 1.0473 0.9548 0.76037 1.4426
## TEL1 0.6065 1.6488 0.39917 0.9216
## Blancos_cat2 1.4019 0.7133 1.10370 1.7808
## RTA_PRED1 0.6024 1.6601 0.44236 0.8203
## Ploidia2 0.2758 3.6261 0.18067 0.4209
## Ploidia4 2.6590 0.3761 1.96833 3.5921
## Ploidia5 0.1242 8.0534 0.01713 0.9000
## SNC2 1.5021 0.6657 0.66471 3.3945
## SNC3 1.7542 0.5701 1.14384 2.6903
## CAT_ERM2 1.4595 0.6852 1.10367 1.9300
## CAT_ERM3 2.1545 0.4641 1.53199 3.0301
##
## Concordance= 0.798 (se = 0.011 )
## Likelihood ratio test= 397 on 14 df, p=<2e-16
## Wald test = 324.5 on 14 df, p=<2e-16
## Score (logrank) test = 432.9 on 14 df, p=<2e-16#Modelo multivariado eliminando Sexo y Blastos
modelo7<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Down +Edad_cat + Estirpe + TEL+ Blancos_cat + Sexo+RTA_PRED +Ploidia + SNC+ CAT_ERM, data=datos)
summary(modelo7)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down + Edad_cat +
## Estirpe + TEL + Blancos_cat + Sexo + RTA_PRED + Ploidia +
## SNC + CAT_ERM, data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down1 1.16845 3.21701 0.27173 4.300 1.71e-05 ***
## Edad_cat2 0.07960 1.08285 0.11611 0.686 0.49299
## EstirpeT 0.04624 1.04733 0.16336 0.283 0.77714
## TEL1 -0.50002 0.60652 0.21345 -2.343 0.01915 *
## Blancos_cat2 0.33786 1.40194 0.12204 2.768 0.00563 **
## Sexo1 0.03262 1.03316 0.11527 0.283 0.77717
## RTA_PRED1 -0.50687 0.60238 0.15753 -3.218 0.00129 **
## Ploidia2 -1.28816 0.27578 0.21577 -5.970 2.37e-09 ***
## Ploidia4 0.97796 2.65903 0.15346 6.373 1.86e-10 ***
## Ploidia5 -2.08609 0.12417 1.01060 -2.064 0.03900 *
## SNC2 0.40688 1.50212 0.41597 0.978 0.32800
## SNC3 0.56203 1.75423 0.21818 2.576 0.01000 **
## CAT_ERM2 0.37808 1.45948 0.14257 2.652 0.00801 **
## CAT_ERM3 0.76758 2.15454 0.17399 4.412 1.03e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down1 3.2170 0.3108 1.88866 5.4796
## Edad_cat2 1.0829 0.9235 0.86246 1.3596
## EstirpeT 1.0473 0.9548 0.76037 1.4426
## TEL1 0.6065 1.6488 0.39917 0.9216
## Blancos_cat2 1.4019 0.7133 1.10370 1.7808
## Sexo1 1.0332 0.9679 0.82423 1.2951
## RTA_PRED1 0.6024 1.6601 0.44236 0.8203
## Ploidia2 0.2758 3.6261 0.18067 0.4209
## Ploidia4 2.6590 0.3761 1.96833 3.5921
## Ploidia5 0.1242 8.0534 0.01713 0.9000
## SNC2 1.5021 0.6657 0.66471 3.3945
## SNC3 1.7542 0.5701 1.14384 2.6903
## CAT_ERM2 1.4595 0.6852 1.10367 1.9300
## CAT_ERM3 2.1545 0.4641 1.53199 3.0301
##
## Concordance= 0.798 (se = 0.011 )
## Likelihood ratio test= 397 on 14 df, p=<2e-16
## Wald test = 324.5 on 14 df, p=<2e-16
## Score (logrank) test = 432.9 on 14 df, p=<2e-16#Modelo multivariado eliminando Sexo y SNC
modelo8<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Down + RTA_PRED + Estirpe+ Edad_cat + TEL+ Blancos_cat +RTA_PRED +Ploidia + Blastos+ CAT_ERM , data=datos)
summary(modelo8)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down + RTA_PRED +
## Estirpe + Edad_cat + TEL + Blancos_cat + RTA_PRED + Ploidia +
## Blastos + CAT_ERM, data = datos)
##
## n= 1480, number of events= 322
## (616 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down1 1.133e+00 3.105e+00 2.763e-01 4.101 4.11e-05 ***
## RTA_PRED1 -5.151e-01 5.974e-01 1.578e-01 -3.264 0.0011 **
## EstirpeT 1.035e-01 1.109e+00 1.597e-01 0.648 0.5168
## Edad_cat2 7.437e-02 1.077e+00 1.164e-01 0.639 0.5229
## TEL1 -4.964e-01 6.087e-01 2.131e-01 -2.330 0.0198 *
## Blancos_cat2 3.415e-01 1.407e+00 1.481e-01 2.305 0.0211 *
## Ploidia2 -1.314e+00 2.688e-01 2.177e-01 -6.034 1.60e-09 ***
## Ploidia4 9.634e-01 2.621e+00 1.533e-01 6.283 3.33e-10 ***
## Ploidia5 -2.129e+00 1.189e-01 1.010e+00 -2.108 0.0351 *
## Blastos -9.785e-05 9.999e-01 2.015e-03 -0.049 0.9613
## CAT_ERM2 3.621e-01 1.436e+00 1.435e-01 2.524 0.0116 *
## CAT_ERM3 7.629e-01 2.145e+00 1.741e-01 4.383 1.17e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down1 3.1049 0.3221 1.80676 5.3357
## RTA_PRED1 0.5974 1.6739 0.43848 0.8140
## EstirpeT 1.1091 0.9017 0.81101 1.5167
## Edad_cat2 1.0772 0.9283 0.85746 1.3533
## TEL1 0.6087 1.6429 0.40087 0.9243
## Blancos_cat2 1.4071 0.7107 1.05251 1.8812
## Ploidia2 0.2688 3.7196 0.17547 0.4119
## Ploidia4 2.6205 0.3816 1.94029 3.5392
## Ploidia5 0.1189 8.4106 0.01641 0.8613
## Blastos 0.9999 1.0001 0.99596 1.0039
## CAT_ERM2 1.4363 0.6962 1.08426 1.9027
## CAT_ERM3 2.1446 0.4663 1.52468 3.0165
##
## Concordance= 0.798 (se = 0.011 )
## Likelihood ratio test= 390.6 on 12 df, p=<2e-16
## Wald test = 318.8 on 12 df, p=<2e-16
## Score (logrank) test = 427.8 on 12 df, p=<2e-16#Modelo multivariado eliminando Edad y Blastos
modelo9<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Sexo + Down + RTA_PRED + Estirpe+ TEL+ Blancos_cat + SNC+RTA_PRED +Ploidia + CAT_ERM , data=datos)
summary(modelo9)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Sexo + Down + RTA_PRED +
## Estirpe + TEL + Blancos_cat + SNC + RTA_PRED + Ploidia +
## CAT_ERM, data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Sexo1 0.03354 1.03411 0.11514 0.291 0.7708
## Down1 1.19008 3.28735 0.26976 4.412 1.03e-05 ***
## RTA_PRED1 -0.49853 0.60742 0.15700 -3.175 0.0015 **
## EstirpeT 0.06027 1.06213 0.16194 0.372 0.7097
## TEL1 -0.52169 0.59352 0.21107 -2.472 0.0134 *
## Blancos_cat2 0.33861 1.40300 0.12198 2.776 0.0055 **
## SNC2 0.41045 1.50750 0.41578 0.987 0.3236
## SNC3 0.55823 1.74757 0.21804 2.560 0.0105 *
## Ploidia2 -1.28589 0.27640 0.21574 -5.960 2.51e-09 ***
## Ploidia4 0.98546 2.67904 0.15304 6.439 1.20e-10 ***
## Ploidia5 -2.07942 0.12500 1.01054 -2.058 0.0396 *
## CAT_ERM2 0.38046 1.46295 0.14254 2.669 0.0076 **
## CAT_ERM3 0.77847 2.17814 0.17325 4.493 7.01e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Sexo1 1.0341 0.9670 0.82520 1.2959
## Down1 3.2874 0.3042 1.93742 5.5779
## RTA_PRED1 0.6074 1.6463 0.44654 0.8263
## EstirpeT 1.0621 0.9415 0.77327 1.4589
## TEL1 0.5935 1.6849 0.39244 0.8976
## Blancos_cat2 1.4030 0.7128 1.10466 1.7819
## SNC2 1.5075 0.6634 0.66733 3.4054
## SNC3 1.7476 0.5722 1.13983 2.6794
## Ploidia2 0.2764 3.6179 0.18110 0.4219
## Ploidia4 2.6790 0.3733 1.98478 3.6162
## Ploidia5 0.1250 7.9998 0.01725 0.9059
## CAT_ERM2 1.4630 0.6835 1.10638 1.9344
## CAT_ERM3 2.1781 0.4591 1.55100 3.0589
##
## Concordance= 0.797 (se = 0.011 )
## Likelihood ratio test= 396.5 on 13 df, p=<2e-16
## Wald test = 323.2 on 13 df, p=<2e-16
## Score (logrank) test = 432.2 on 13 df, p=<2e-16#Modelo multivariado eliminando Sexo, SNC y Blastos
modelo10<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Down + RTA_PRED + Estirpe+ Edad_cat + TEL+ Blancos_cat + RTA_PRED +Ploidia + CAT_ERM , data=datos)
summary(modelo10)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down + RTA_PRED +
## Estirpe + Edad_cat + TEL + Blancos_cat + RTA_PRED + Ploidia +
## CAT_ERM, data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down1 1.13820 3.12116 0.27023 4.212 2.53e-05 ***
## RTA_PRED1 -0.51799 0.59572 0.15621 -3.316 0.000913 ***
## EstirpeT 0.10294 1.10842 0.15942 0.646 0.518487
## Edad_cat2 0.07519 1.07809 0.11598 0.648 0.516788
## TEL1 -0.50154 0.60560 0.21252 -2.360 0.018275 *
## Blancos_cat2 0.32862 1.38905 0.12230 2.687 0.007207 **
## Ploidia2 -1.29732 0.27326 0.21591 -6.009 1.87e-09 ***
## Ploidia4 0.96059 2.61324 0.15323 6.269 3.64e-10 ***
## Ploidia5 -2.13580 0.11815 1.01030 -2.114 0.034513 *
## CAT_ERM2 0.36711 1.44356 0.14219 2.582 0.009826 **
## CAT_ERM3 0.76861 2.15677 0.17338 4.433 9.29e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down1 3.1212 0.3204 1.83780 5.3007
## RTA_PRED1 0.5957 1.6786 0.43860 0.8091
## EstirpeT 1.1084 0.9022 0.81097 1.5150
## Edad_cat2 1.0781 0.9276 0.85888 1.3532
## TEL1 0.6056 1.6513 0.39929 0.9185
## Blancos_cat2 1.3891 0.7199 1.09300 1.7653
## Ploidia2 0.2733 3.6595 0.17898 0.4172
## Ploidia4 2.6132 0.3827 1.93529 3.5287
## Ploidia5 0.1182 8.4638 0.01631 0.8559
## CAT_ERM2 1.4436 0.6927 1.09246 1.9075
## CAT_ERM3 2.1568 0.4637 1.53541 3.0296
##
## Concordance= 0.798 (se = 0.011 )
## Likelihood ratio test= 390.4 on 11 df, p=<2e-16
## Wald test = 320 on 11 df, p=<2e-16
## Score (logrank) test = 427.5 on 11 df, p=<2e-16#Modelo multivariado eliminando Sexo, Edad, Estirpe y Blastos
modelo11<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Down + RTA_PRED + TEL+ Blancos_cat + SNC+RTA_PRED +Ploidia + CAT_ERM , data=datos)
summary(modelo11)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down + RTA_PRED +
## TEL + Blancos_cat + SNC + RTA_PRED + Ploidia + CAT_ERM, data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down1 1.1732 3.2325 0.2676 4.385 1.16e-05 ***
## RTA_PRED1 -0.5135 0.5984 0.1534 -3.347 0.000816 ***
## TEL1 -0.5310 0.5880 0.2099 -2.530 0.011422 *
## Blancos_cat2 0.3491 1.4178 0.1185 2.947 0.003211 **
## SNC2 0.4151 1.5145 0.4154 0.999 0.317647
## SNC3 0.5693 1.7671 0.2152 2.645 0.008160 **
## Ploidia2 -1.2838 0.2770 0.2156 -5.953 2.63e-09 ***
## Ploidia4 0.9861 2.6806 0.1530 6.445 1.16e-10 ***
## Ploidia5 -2.0840 0.1244 1.0104 -2.062 0.039166 *
## CAT_ERM2 0.3810 1.4637 0.1425 2.674 0.007495 **
## CAT_ERM3 0.7814 2.1844 0.1731 4.515 6.33e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down1 3.2325 0.3094 1.91327 5.4612
## RTA_PRED1 0.5984 1.6711 0.44302 0.8083
## TEL1 0.5880 1.7007 0.38967 0.8873
## Blancos_cat2 1.4178 0.7053 1.12401 1.7883
## SNC2 1.5145 0.6603 0.67097 3.4185
## SNC3 1.7671 0.5659 1.15895 2.6944
## Ploidia2 0.2770 3.6103 0.18151 0.4227
## Ploidia4 2.6806 0.3730 1.98610 3.6181
## Ploidia5 0.1244 8.0362 0.01717 0.9016
## CAT_ERM2 1.4637 0.6832 1.10709 1.9353
## CAT_ERM3 2.1844 0.4578 1.55609 3.0665
##
## Concordance= 0.796 (se = 0.011 )
## Likelihood ratio test= 396.3 on 11 df, p=<2e-16
## Wald test = 323.3 on 11 df, p=<2e-16
## Score (logrank) test = 432 on 11 df, p=<2e-16#Modelo multivariado eliminando Sexo, SNC, Estirpe y Blastos
modelo12<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Down + RTA_PRED + Edad_cat + TEL+ Blancos_cat + RTA_PRED +Ploidia + CAT_ERM , data=datos)
summary(modelo12)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down + RTA_PRED +
## Edad_cat + TEL + Blancos_cat + RTA_PRED + Ploidia + CAT_ERM,
## data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down1 1.11923 3.06250 0.26861 4.167 3.09e-05 ***
## RTA_PRED1 -0.53783 0.58401 0.15276 -3.521 0.00043 ***
## Edad_cat2 0.08409 1.08773 0.11514 0.730 0.46519
## TEL1 -0.51329 0.59853 0.21166 -2.425 0.01531 *
## Blancos_cat2 0.34680 1.41454 0.11879 2.920 0.00351 **
## Ploidia2 -1.29375 0.27424 0.21583 -5.994 2.05e-09 ***
## Ploidia4 0.95979 2.61114 0.15323 6.264 3.76e-10 ***
## Ploidia5 -2.13754 0.11794 1.01029 -2.116 0.03437 *
## CAT_ERM2 0.36572 1.44155 0.14216 2.573 0.01009 *
## CAT_ERM3 0.77076 2.16141 0.17329 4.448 8.67e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down1 3.0625 0.3265 1.80898 5.1846
## RTA_PRED1 0.5840 1.7123 0.43290 0.7879
## Edad_cat2 1.0877 0.9193 0.86798 1.3631
## TEL1 0.5985 1.6708 0.39529 0.9062
## Blancos_cat2 1.4145 0.7069 1.12073 1.7854
## Ploidia2 0.2742 3.6464 0.17964 0.4187
## Ploidia4 2.6111 0.3830 1.93375 3.5258
## Ploidia5 0.1179 8.4786 0.01628 0.8544
## CAT_ERM2 1.4415 0.6937 1.09100 1.9047
## CAT_ERM3 2.1614 0.4627 1.53898 3.0356
##
## Concordance= 0.797 (se = 0.011 )
## Likelihood ratio test= 390 on 10 df, p=<2e-16
## Wald test = 319.9 on 10 df, p=<2e-16
## Score (logrank) test = 427.1 on 10 df, p=<2e-16#Modelo multivariado eliminando Estirpe, Blastos y Sexo
modelo13<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Down + RTA_PRED + Edad_cat + TEL+ Blancos_cat + SNC+RTA_PRED +Ploidia + CAT_ERM , data=datos)
summary(modelo13)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down + RTA_PRED +
## Edad_cat + TEL + Blancos_cat + SNC + RTA_PRED + Ploidia +
## CAT_ERM, data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down1 1.15215 3.16498 0.26906 4.282 1.85e-05 ***
## RTA_PRED1 -0.51957 0.59478 0.15370 -3.380 0.000724 ***
## Edad_cat2 0.08459 1.08827 0.11520 0.734 0.462743
## TEL1 -0.50586 0.60299 0.21276 -2.378 0.017426 *
## Blancos_cat2 0.34567 1.41293 0.11863 2.914 0.003569 **
## SNC2 0.41133 1.50883 0.41551 0.990 0.322195
## SNC3 0.57037 1.76892 0.21524 2.650 0.008051 **
## Ploidia2 -1.28672 0.27618 0.21568 -5.966 2.44e-09 ***
## Ploidia4 0.97817 2.65959 0.15342 6.376 1.82e-10 ***
## Ploidia5 -2.09064 0.12361 1.01049 -2.069 0.038553 *
## CAT_ERM2 0.37865 1.46031 0.14251 2.657 0.007884 **
## CAT_ERM3 0.76926 2.15816 0.17385 4.425 9.65e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down1 3.1650 0.3160 1.86786 5.3629
## RTA_PRED1 0.5948 1.6813 0.44007 0.8039
## Edad_cat2 1.0883 0.9189 0.86833 1.3639
## TEL1 0.6030 1.6584 0.39738 0.9150
## Blancos_cat2 1.4129 0.7077 1.11981 1.7828
## SNC2 1.5088 0.6628 0.66828 3.4066
## SNC3 1.7689 0.5653 1.16010 2.6972
## Ploidia2 0.2762 3.6209 0.18097 0.4215
## Ploidia4 2.6596 0.3760 1.96891 3.5926
## Ploidia5 0.1236 8.0901 0.01706 0.8957
## CAT_ERM2 1.4603 0.6848 1.10443 1.9309
## CAT_ERM3 2.1582 0.4634 1.53499 3.0343
##
## Concordance= 0.797 (se = 0.011 )
## Likelihood ratio test= 396.8 on 12 df, p=<2e-16
## Wald test = 324.6 on 12 df, p=<2e-16
## Score (logrank) test = 432.7 on 12 df, p=<2e-16#Modelo multivariado eliminando Estirpe y Blastos
modelo14<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Sexo + Down + RTA_PRED + Edad_cat + TEL+ Blancos_cat + SNC+RTA_PRED +Ploidia + CAT_ERM , data=datos)
summary(modelo14)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Sexo + Down + RTA_PRED +
## Edad_cat + TEL + Blancos_cat + SNC + RTA_PRED + Ploidia +
## CAT_ERM, data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Sexo1 0.03615 1.03681 0.11459 0.315 0.752384
## Down1 1.16112 3.19351 0.27052 4.292 1.77e-05 ***
## RTA_PRED1 -0.51562 0.59713 0.15431 -3.342 0.000833 ***
## Edad_cat2 0.08368 1.08728 0.11520 0.726 0.467632
## TEL1 -0.50475 0.60366 0.21277 -2.372 0.017677 *
## Blancos_cat2 0.34583 1.41316 0.11865 2.915 0.003561 **
## SNC2 0.40513 1.49950 0.41595 0.974 0.330060
## SNC3 0.57187 1.77157 0.21530 2.656 0.007903 **
## Ploidia2 -1.28645 0.27625 0.21568 -5.965 2.45e-09 ***
## Ploidia4 0.97752 2.65785 0.15344 6.371 1.88e-10 ***
## Ploidia5 -2.08596 0.12419 1.01060 -2.064 0.039011 *
## CAT_ERM2 0.37752 1.45866 0.14255 2.648 0.008091 **
## CAT_ERM3 0.76890 2.15740 0.17391 4.421 9.81e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Sexo1 1.0368 0.9645 0.82826 1.2979
## Down1 3.1935 0.3131 1.87933 5.4267
## RTA_PRED1 0.5971 1.6747 0.44129 0.8080
## Edad_cat2 1.0873 0.9197 0.86752 1.3627
## TEL1 0.6037 1.6566 0.39781 0.9160
## Blancos_cat2 1.4132 0.7076 1.11994 1.7832
## SNC2 1.4995 0.6669 0.66358 3.3885
## SNC3 1.7716 0.5645 1.16171 2.7016
## Ploidia2 0.2763 3.6199 0.18101 0.4216
## Ploidia4 2.6578 0.3762 1.96753 3.5904
## Ploidia5 0.1242 8.0523 0.01713 0.9001
## CAT_ERM2 1.4587 0.6856 1.10309 1.9288
## CAT_ERM3 2.1574 0.4635 1.53425 3.0336
##
## Concordance= 0.798 (se = 0.011 )
## Likelihood ratio test= 396.9 on 13 df, p=<2e-16
## Wald test = 324.5 on 13 df, p=<2e-16
## Score (logrank) test = 432.7 on 13 df, p=<2e-16#modelo multivariado eliminando SNC y Blastos
modelo15<-coxph( Surv(TIEMPOSG,SGSTATUS) ~ Sexo + Down + RTA_PRED + Estirpe+ Edad_cat + TEL+ Blancos_cat + RTA_PRED +Ploidia + CAT_ERM , data=datos)
summary(modelo15)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Sexo + Down + RTA_PRED +
## Estirpe + Edad_cat + TEL + Blancos_cat + RTA_PRED + Ploidia +
## CAT_ERM, data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Sexo1 0.02749 1.02787 0.11515 0.239 0.811290
## Down1 1.14409 3.13958 0.27129 4.217 2.47e-05 ***
## RTA_PRED1 -0.51602 0.59689 0.15647 -3.298 0.000974 ***
## EstirpeT 0.09869 1.10372 0.16037 0.615 0.538292
## Edad_cat2 0.07468 1.07754 0.11599 0.644 0.519642
## TEL1 -0.50132 0.60573 0.21250 -2.359 0.018316 *
## Blancos_cat2 0.32926 1.38993 0.12233 2.692 0.007113 **
## Ploidia2 -1.29722 0.27329 0.21591 -6.008 1.88e-09 ***
## Ploidia4 0.95979 2.61114 0.15327 6.262 3.80e-10 ***
## Ploidia5 -2.13250 0.11854 1.01039 -2.111 0.034809 *
## CAT_ERM2 0.36599 1.44194 0.14226 2.573 0.010089 *
## CAT_ERM3 0.76837 2.15626 0.17341 4.431 9.39e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Sexo1 1.0279 0.9729 0.82021 1.2881
## Down1 3.1396 0.3185 1.84481 5.3431
## RTA_PRED1 0.5969 1.6753 0.43925 0.8111
## EstirpeT 1.1037 0.9060 0.80604 1.5113
## Edad_cat2 1.0775 0.9280 0.85843 1.3526
## TEL1 0.6057 1.6509 0.39940 0.9187
## Blancos_cat2 1.3899 0.7195 1.09362 1.7665
## Ploidia2 0.2733 3.6591 0.17900 0.4173
## Ploidia4 2.6111 0.3830 1.93359 3.5261
## Ploidia5 0.1185 8.4359 0.01636 0.8588
## CAT_ERM2 1.4419 0.6935 1.09109 1.9056
## CAT_ERM3 2.1563 0.4638 1.53494 3.0291
##
## Concordance= 0.798 (se = 0.011 )
## Likelihood ratio test= 390.5 on 12 df, p=<2e-16
## Wald test = 319.9 on 12 df, p=<2e-16
## Score (logrank) test = 427.5 on 12 df, p=<2e-16#Seleccion de modelo utilizando AIC
AIC(modelo1,modelo2, modelo3,modelo4,modelo5,modelo6,modelo7,modelo8,modelo9,modelo10,modelo11, modelo12, modelo13, modelo14, modelo15)## Warning in AIC.default(modelo1, modelo2, modelo3, modelo4, modelo5, modelo6, :
## models are not all fitted to the same number of observations## df AIC
## modelo1 9 4158.340
## modelo2 14 4130.160
## modelo3 14 4130.206
## modelo4 14 4130.585
## modelo5 13 4134.738
## modelo6 14 4160.813
## modelo7 14 4160.813
## modelo8 12 4132.758
## modelo9 13 4159.284
## modelo10 11 4161.394
## modelo11 11 4155.533
## modelo12 10 4159.805
## modelo13 12 4156.993
## modelo14 13 4158.893
## modelo15 12 4163.337#El modelo 2 es aquel que tiene menor AIC (4130.160)Del modelo seleccionado con menor AIC, no resultan significativas las variables Edad, Blastos y Estirpe. Por lo tanto, se excluyen del modelo final.
modelo_final<- coxph( Surv(TIEMPOSG,SGSTATUS) ~ Down + TEL+ Blancos_cat +
SNC+RTA_PRED +Ploidia + CAT_ERM, data=datos)
summary(modelo_final)## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down + TEL + Blancos_cat +
## SNC + RTA_PRED + Ploidia + CAT_ERM, data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down1 1.1732 3.2325 0.2676 4.385 1.16e-05 ***
## TEL1 -0.5310 0.5880 0.2099 -2.530 0.011422 *
## Blancos_cat2 0.3491 1.4178 0.1185 2.947 0.003211 **
## SNC2 0.4151 1.5145 0.4154 0.999 0.317647
## SNC3 0.5693 1.7671 0.2152 2.645 0.008160 **
## RTA_PRED1 -0.5135 0.5984 0.1534 -3.347 0.000816 ***
## Ploidia2 -1.2838 0.2770 0.2156 -5.953 2.63e-09 ***
## Ploidia4 0.9861 2.6806 0.1530 6.445 1.16e-10 ***
## Ploidia5 -2.0840 0.1244 1.0104 -2.062 0.039166 *
## CAT_ERM2 0.3810 1.4637 0.1425 2.674 0.007495 **
## CAT_ERM3 0.7814 2.1844 0.1731 4.515 6.33e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down1 3.2325 0.3094 1.91327 5.4612
## TEL1 0.5880 1.7007 0.38967 0.8873
## Blancos_cat2 1.4178 0.7053 1.12401 1.7883
## SNC2 1.5145 0.6603 0.67097 3.4185
## SNC3 1.7671 0.5659 1.15895 2.6944
## RTA_PRED1 0.5984 1.6711 0.44302 0.8083
## Ploidia2 0.2770 3.6103 0.18151 0.4227
## Ploidia4 2.6806 0.3730 1.98610 3.6181
## Ploidia5 0.1244 8.0362 0.01717 0.9016
## CAT_ERM2 1.4637 0.6832 1.10709 1.9353
## CAT_ERM3 2.1844 0.4578 1.55609 3.0665
##
## Concordance= 0.796 (se = 0.011 )
## Likelihood ratio test= 396.3 on 11 df, p=<2e-16
## Wald test = 323.3 on 11 df, p=<2e-16
## Score (logrank) test = 432 on 11 df, p=<2e-16Analisis de VIF del modelo seleccionado
vif_modelo_final<- vif(modelo_final)## Warning in vif.default(modelo_final): No intercept: vifs may not be sensible.print(vif_modelo_final)## GVIF Df GVIF^(1/(2*Df))
## Down 1.015711 1 1.007825
## TEL 1.016145 1 1.008040
## Blancos_cat 1.131060 1 1.063513
## SNC 1.035485 2 1.008756
## RTA_PRED 1.353425 1 1.163368
## Ploidia 1.015883 3 1.002630
## CAT_ERM 1.320868 2 1.072049Validacion de los supuestos de COX del modelo seleccionado
Riesgo proporcional
cox.zph(modelo_final)## chisq df p
## Down 2.039 1 0.153
## TEL 2.917 1 0.088
## Blancos_cat 2.465 1 0.116
## SNC 3.004 2 0.223
## RTA_PRED 0.825 1 0.364
## Ploidia 3.240 3 0.356
## CAT_ERM 3.553 2 0.169
## GLOBAL 16.817 11 0.113#Verificacion del supuesto de riesgo proporcional utilizando graficos:
require(rms) ## Loading required package: rms## Loading required package: SparseM##
## Attaching package: 'SparseM'## The following object is masked from 'package:base':
##
## backsolve##
## Attaching package: 'rms'## The following objects are masked from 'package:car':
##
## Predict, vifrequire(car)
survlla1<- npsurv(Surv(TIEMPOSG,SGSTATUS)~CAT_ERM,data=datos)
survplot(survlla1, loglog=T, logt=F, xlim = c(0,12),
xlab="Tiempo hasta la muerte", ylab = "Log(-Log (S(t))", label.curves = T, time.inc = 1,
levels.only = T, conf="none", type="kaplan-meier") 
#Sexo
survlla2<- npsurv(Surv(TIEMPOSG,SGSTATUS)~Sexo,data=datos)
survplot(survlla2, loglog=T, logt=F, xlim = c(0,12),
xlab="Tiempo hasta la muerte", ylab = "Log(-Log (S(t))", label.curves = T, time.inc = 1,
levels.only = T, conf="none", type="kaplan-meier") 
#Down
survlla3<- npsurv(Surv(TIEMPOSG,SGSTATUS)~Down,data=datos)
survplot(survlla3, loglog=T, logt=F, xlim = c(0,12),
xlab="Tiempo hasta la muerte", ylab = "Log(-Log (S(t))", label.curves = T, time.inc = 1,
levels.only = T, conf="none", type="kaplan-meier") 
#SNC
survlla4<- npsurv(Surv(TIEMPOSG,SGSTATUS)~SNC,data=datos)
survplot(survlla4, loglog=T, logt=F, xlim = c(0,12),
xlab="Tiempo hasta la muerte", ylab = "Log(-Log (S(t))", label.curves = T, time.inc = 1,
levels.only = T, conf="none", type="kaplan-meier") 
#Ploidia
survlla5<- npsurv(Surv(TIEMPOSG,SGSTATUS)~Ploidia,data=datos)
#ver no da con ploidia
#Rta a la prednisona
survlla6<- npsurv(Surv(TIEMPOSG,SGSTATUS)~RTA_PRED,data=datos)
survplot(survlla6, loglog=T, logt=F, xlim = c(0,12),
xlab="Tiempo hasta la muerte", ylab = "Log(-Log (S(t))", label.curves = T, time.inc = 1,
levels.only = T, conf="none", type="kaplan-meier") 
#Estirpe
survlla7<- npsurv(Surv(TIEMPOSG,SGSTATUS)~Estirpe,data=datos)
survplot(survlla7, loglog=T, logt=F, xlim = c(0,12),
xlab="Tiempo hasta la muerte", ylab = "Log(-Log (S(t))", label.curves = T, time.inc = 1,
levels.only = T, conf="none", type="kaplan-meier") 
#TEL
survlla8<- npsurv(Surv(TIEMPOSG,SGSTATUS)~TEL,data=datos)
survplot(survlla8, loglog=T, logt=F, xlim = c(0,12),
xlab="Tiempo hasta la muerte", ylab = "Log(-Log (S(t))", label.curves = T, time.inc = 1,
levels.only = T, conf="none", type="kaplan-meier") 
#MLL
survlla9<- npsurv(Surv(TIEMPOSG,SGSTATUS)~MLL,data=datos)
survplot(survlla9, loglog=T, logt=F, xlim = c(0,12),
xlab="Tiempo hasta la muerte", ylab = "Log(-Log (S(t))", label.curves = T, time.inc = 1,
levels.only = T, conf="none", type="kaplan-meier") 
Se cumple supuesto de riesgo proporcional, en todos los casos, los pv obtenidos son mayores a 0.05 ( de forma global y por cada variable predictora) De esta manera, el modelo supone que el HR para cada variable Xj es el mismo cualquiera sea el tiempo t
Residuos Schoenfeld
library(ggfortify)
residuos_mfinal<-cox.zph(modelo_final)
ggcoxzph(residuos_mfinal)
#Residuos por variables predictoras:
#Blancos categorizada
ggcoxzph(residuos_mfinal,var= c("Blancos_cat"), font.main=10)
#Down
ggcoxzph(residuos_mfinal,var= c("Down"), font.main=10)
#TEL
ggcoxzph(residuos_mfinal,var= c("TEL"), font.main=10)
#SNC
ggcoxzph(residuos_mfinal,var= c("SNC"), font.main=10)
#Rta prednisona
ggcoxzph(residuos_mfinal,var= c("RTA_PRED"), font.main=10)
#Ploidia
ggcoxzph(residuos_mfinal,var= c("Ploidia"), font.main=10)
#Categorias de ERM
ggcoxzph(residuos_mfinal,var= c("CAT_ERM"), font.main=10)
#Residuos Schoenfeld para todas las variables:
ggcoxdiagnostics(modelo_final,type = "schoenfeld")## `geom_smooth()` using formula 'y ~ x'
Modelo final de regresion de COX
#$$H\left(t\right)= \ho(t)*exp^(beta_{Down}Xdown*\beta_{Pred}Xpred*\beta_{Estirpe}Xestirpe*\beta_{Edad} Xedad*\beta_{TEL}Xtel*\beta_{Blancos}Xblancos*\beta_{SNC}Xsnc*\beta_{Ploidia}Xploidia*\ beta_{Blastos}Xblastos)*\beta_{ERM}Xerm)$$H(t) = ho(t) x e ^ (Down Xdown x Rta pred Xrta pred x TEL Xtel x Blancos Xblancos x SNC Xsnc x Ploidia X ploidia x ERM X erm)
Forest plot del modelo seleccionado
ggforest(modelo_final, data = datos, main="Hazard Ratio de modelo final",fontsize = 0.6)
Tabla con HR del modelo final
## Call:
## coxph(formula = Surv(TIEMPOSG, SGSTATUS) ~ Down + TEL + Blancos_cat +
## SNC + RTA_PRED + Ploidia + CAT_ERM, data = datos)
##
## n= 1492, number of events= 324
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down1 1.1732 3.2325 0.2676 4.385 1.16e-05 ***
## TEL1 -0.5310 0.5880 0.2099 -2.530 0.011422 *
## Blancos_cat2 0.3491 1.4178 0.1185 2.947 0.003211 **
## SNC2 0.4151 1.5145 0.4154 0.999 0.317647
## SNC3 0.5693 1.7671 0.2152 2.645 0.008160 **
## RTA_PRED1 -0.5135 0.5984 0.1534 -3.347 0.000816 ***
## Ploidia2 -1.2838 0.2770 0.2156 -5.953 2.63e-09 ***
## Ploidia4 0.9861 2.6806 0.1530 6.445 1.16e-10 ***
## Ploidia5 -2.0840 0.1244 1.0104 -2.062 0.039166 *
## CAT_ERM2 0.3810 1.4637 0.1425 2.674 0.007495 **
## CAT_ERM3 0.7814 2.1844 0.1731 4.515 6.33e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down1 3.2325 0.3094 1.91327 5.4612
## TEL1 0.5880 1.7007 0.38967 0.8873
## Blancos_cat2 1.4178 0.7053 1.12401 1.7883
## SNC2 1.5145 0.6603 0.67097 3.4185
## SNC3 1.7671 0.5659 1.15895 2.6944
## RTA_PRED1 0.5984 1.6711 0.44302 0.8083
## Ploidia2 0.2770 3.6103 0.18151 0.4227
## Ploidia4 2.6806 0.3730 1.98610 3.6181
## Ploidia5 0.1244 8.0362 0.01717 0.9016
## CAT_ERM2 1.4637 0.6832 1.10709 1.9353
## CAT_ERM3 2.1844 0.4578 1.55609 3.0665
##
## Concordance= 0.796 (se = 0.011 )
## Likelihood ratio test= 396.3 on 11 df, p=<2e-16
## Wald test = 323.3 on 11 df, p=<2e-16
## Score (logrank) test = 432 on 11 df, p=<2e-16## Registered S3 method overwritten by 'Publish':
## method from
## print.ci coin## Variable Units Missing HazardRatio CI.95 p-value
## Down 0 0 Ref
## 1 3.23 [1.91;5.46] < 0.001
## TEL 0 401 Ref
## 1 0.59 [0.39;0.89] 0.01142
## Blancos_cat 1 0 Ref
## 2 1.42 [1.12;1.79] 0.00321
## SNC 1 0 Ref
## 2 1.51 [0.67;3.42] 0.31765
## 3 1.77 [1.16;2.69] 0.00816
## RTA_PRED 0 5 Ref
## 1 0.60 [0.44;0.81] < 0.001
## Ploidia 1 0 Ref
## 2 0.28 [0.18;0.42] < 0.001
## 4 2.68 [1.99;3.62] < 0.001
## 5 0.12 [0.02;0.90] 0.03917
## CAT_ERM 1 354 Ref
## 2 1.46 [1.11;1.94] 0.00750
## 3 2.18 [1.56;3.07] < 0.001print(tabla_final) %>%
kbl(caption = "Tabla 3: Hazard Ratio para modelo multiple")%>%
kable_paper()## Variable Units Missing HazardRatio CI.95 p-value
## 1 Down 0 0 Ref
## 2 1 3.23 [1.91;5.46] < 0.001
## 3 TEL 0 401 Ref
## 4 1 0.59 [0.39;0.89] 0.01142
## 5 Blancos_cat 1 0 Ref
## 6 2 1.42 [1.12;1.79] 0.00321
## 7 SNC 1 0 Ref
## 8 2 1.51 [0.67;3.42] 0.31765
## 9 3 1.77 [1.16;2.69] 0.00816
## 10 RTA_PRED 0 5 Ref
## 11 1 0.60 [0.44;0.81] < 0.001
## 12 Ploidia 1 0 Ref
## 13 2 0.28 [0.18;0.42] < 0.001
## 14 4 2.68 [1.99;3.62] < 0.001
## 15 5 0.12 [0.02;0.90] 0.03917
## 16 CAT_ERM 1 354 Ref
## 17 2 1.46 [1.11;1.94] 0.00750
## 18 3 2.18 [1.56;3.07] < 0.001| Variable | Units | Missing | HazardRatio | CI.95 | p-value |
|---|---|---|---|---|---|
| Down | 0 | 0 | Ref | ||
| 1 | 3.23 | [1.91;5.46] | < 0.001 | ||
| TEL | 0 | 401 | Ref | ||
| 1 | 0.59 | [0.39;0.89] | 0.01142 | ||
| Blancos_cat | 1 | 0 | Ref | ||
| 2 | 1.42 | [1.12;1.79] | 0.00321 | ||
| SNC | 1 | 0 | Ref | ||
| 2 | 1.51 | [0.67;3.42] | 0.31765 | ||
| 3 | 1.77 | [1.16;2.69] | 0.00816 | ||
| RTA_PRED | 0 | 5 | Ref | ||
| 1 | 0.60 | [0.44;0.81] | < 0.001 | ||
| Ploidia | 1 | 0 | Ref | ||
| 2 | 0.28 | [0.18;0.42] | < 0.001 | ||
| 4 | 2.68 | [1.99;3.62] | < 0.001 | ||
| 5 | 0.12 | [0.02;0.90] | 0.03917 | ||
| CAT_ERM | 1 | 354 | Ref | ||
| 2 | 1.46 | [1.11;1.94] | 0.00750 | ||
| 3 | 2.18 | [1.56;3.07] | < 0.001 |
#Analisis de sobrevida libre de evento
## Warning in .add_surv_median(p, fit, type = surv.median.line, fun = fun, : Median
## survival not reached.
## Call:
## survdiff(formula = Surv(TiempoEFS, EFSstatus) ~ CAT_ERM, data = datos_sle)
##
## n=1742, 354 observations deleted due to missingness.
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## CAT_ERM=1 656 97 171.9 32.61 59.05
## CAT_ERM=2 806 218 195.3 2.64 5.25
## CAT_ERM=3 280 112 59.8 45.48 57.37
##
## Chisq= 87.6 on 2 degrees of freedom, p= <2e-16## Warning: NAs introducidos por coerciĆ³n## Call:
## coxph(formula = Surv(TiempoEFS, EFSstatus) ~ I(CAT_ERM == "1") +
## I(CAT_ERM == "2") + I(CAT_ERM == "3"), data = datos_sle)
##
## n= 1742, number of events= 427
## (354 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## I(CAT_ERM == "1")TRUE -1.2574 0.2844 0.1389 -9.050 < 2e-16 ***
## I(CAT_ERM == "2")TRUE -0.5542 0.5746 0.1163 -4.763 1.91e-06 ***
## I(CAT_ERM == "3")TRUE NA NA 0.0000 NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## I(CAT_ERM == "1")TRUE 0.2844 3.516 0.2166 0.3734
## I(CAT_ERM == "2")TRUE 0.5746 1.740 0.4574 0.7217
## I(CAT_ERM == "3")TRUE NA NA NA NA
##
## Concordance= 0.624 (se = 0.013 )
## Likelihood ratio test= 84.34 on 2 df, p=<2e-16
## Wald test = 82 on 2 df, p=<2e-16
## Score (logrank) test = 88.97 on 2 df, p=<2e-16## Call:
## coxph(formula = Surv(TiempoEFS, EFSstatus) ~ Down + TEL + Blancos_cat +
## SNC + RTA_PRED + Ploidia + CAT_ERM, data = datos_sle)
##
## n= 1492, number of events= 374
## (604 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Down2 1.0357 2.8170 0.2672 3.876 0.000106 ***
## TEL1 -0.5619 0.5701 0.1894 -2.967 0.003010 **
## Blancos_cat2 0.3809 1.4635 0.1095 3.479 0.000503 ***
## SNC2 0.7090 2.0319 0.3408 2.080 0.037513 *
## SNC3 0.3017 1.3522 0.2216 1.362 0.173259
## RTA_PRED1 -0.3514 0.7037 0.1462 -2.403 0.016252 *
## Ploidia2 -1.3087 0.2702 0.2003 -6.534 6.39e-11 ***
## Ploidia4 1.0155 2.7607 0.1409 7.209 5.64e-13 ***
## Ploidia5 -2.2169 0.1089 1.0087 -2.198 0.027965 *
## CAT_ERM2 0.5427 1.7206 0.1342 4.045 5.23e-05 ***
## CAT_ERM3 0.8772 2.4042 0.1645 5.333 9.65e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Down2 2.8170 0.3550 1.66849 4.7560
## TEL1 0.5701 1.7541 0.39329 0.8264
## Blancos_cat2 1.4635 0.6833 1.18093 1.8138
## SNC2 2.0319 0.4921 1.04181 3.9630
## SNC3 1.3522 0.7395 0.87588 2.0876
## RTA_PRED1 0.7037 1.4211 0.52834 0.9372
## Ploidia2 0.2702 3.7013 0.18246 0.4001
## Ploidia4 2.7607 0.3622 2.09464 3.6384
## Ploidia5 0.1089 9.1787 0.01509 0.7867
## CAT_ERM2 1.7206 0.5812 1.32277 2.2381
## CAT_ERM3 2.4042 0.4159 1.74165 3.3187
##
## Concordance= 0.805 (se = 0.011 )
## Likelihood ratio test= 453.9 on 11 df, p=<2e-16
## Wald test = 356.6 on 11 df, p=<2e-16
## Score (logrank) test = 482.7 on 11 df, p=<2e-16