##chargement des packages----
library(questionr)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tableone)
library(labelled)
library(gtsummary)
## #StandWithUkraine
library(GGally)
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
library(readxl)
library(effects)
## Le chargement a nécessité le package : carData
## lattice theme set by effectsTheme()
## See ?effectsTheme for details.
library(survival)
library(survminer)
## Le chargement a nécessité le package : ggpubr
##
## Attachement du package : 'survminer'
##
## L'objet suivant est masqué depuis 'package:survival':
##
## myeloma
library(ggplot2)
library(dplyr)
library(knitr)
library(cowplot)
##
## Attachement du package : 'cowplot'
##
## L'objet suivant est masqué depuis 'package:ggpubr':
##
## get_legend
##
## L'objet suivant est masqué depuis 'package:lubridate':
##
## stamp
##chargement des données
bddpajea_180225_v3 <- read_excel("Y:/fp/FAC/2024-2025/Thèses/Pauline Jeanselme/analyses_stats/20250217/bddpajea_180225_v3.xlsx")
venlalt<-filter(bddpajea_180225_v3, evaluable=="oui")
##recodage des variables et bases de données le cas échéant----
venlalt$old65<-ifelse(venlalt$age_initiation>65, 1, 0)
venlalt$overalresponse<-ifelse(venlalt$mro =="rc"| venlalt$mro =="rp", 1, 0)
##renommer des variables pour présentation dans les tableaux de résultats
library(labelled)
var_label(venlalt$old65) <- "Patients older than 65"
var_label(venlalt$greffe_pre_ven) <- "Previous allograft"
var_label(venlalt$age_initiation) <- "Age at initiation"
var_label(venlalt$nb_ligne_avant_ven) <- "N previous lines"
var_label(venlalt$blastose) <- "% of circulating blasts"
var_label(venlalt$blastes_medullaires) <- "% of medullar blasts"
var_label(venlalt$oms_diagnostic) <- "Performans status at diagnosis"
var_label(venlalt$infiltration_snc) <- "Patients wiht cns inflitration"
var_label(venlalt$event_rc) <- "Patients wiht complete response"
var_label(venlalt$mro) <- "Best response observed"
var_label(venlalt$allogreffe_postven) <- "Patients who underwent allograft"
var_label(venlalt$consequence_clinique_toxicite) <- "Patients with clinically relevant toxicity"
var_label(venlalt$sexe) <- "Sex"
var_label(venlalt$diagnostic) <- "Disease"
var_label(venlalt$overalresponse) <- "Overall response rate"
venlaltrr<-filter(venlalt, lal_rr=="oui")
##tableau descriptif population globales ----
tbl_summary(
venlalt, include = c("age_initiation","old65","sexe", "diagnostic", "nb_ligne_avant_ven","greffe_pre_ven", "blastose", "blastes_medullaires", "oms_diagnostic", "infiltration_snc" ),
digits=all_categorical()~ c(0,1)
)
| Characteristic |
N = 16 |
| Age at initiation |
54 (37, 65) |
| Patients older than 65 |
4 (25.0%) |
| Sex |
|
| F |
5 (31.2%) |
| H |
11 (68.8%) |
| Disease |
|
| LAL biclonale |
1 (6.2%) |
| LAL early T |
1 (6.2%) |
| LAL ETP |
2 (12.5%) |
| LAL near ETP |
1 (6.2%) |
| LAL T |
3 (18.8%) |
| LAL T III |
1 (6.2%) |
| LAL T near ETP |
3 (18.8%) |
| LL T |
4 (25.0%) |
| N previous lines |
|
| 0 |
4 (25.0%) |
| 1 |
5 (31.2%) |
| 2 |
6 (37.5%) |
| 3 |
1 (6.2%) |
| Previous allograft |
5 (31.2%) |
| % of circulating blasts |
|
| 0 |
4 (36.4%) |
| 0.026 |
1 (9.1%) |
| 0.25 |
1 (9.1%) |
| 0.28 |
1 (9.1%) |
| 0.52 |
1 (9.1%) |
| 0.67 |
1 (9.1%) |
| 0.93 |
1 (9.1%) |
| 0.96 |
1 (9.1%) |
| Unknown |
5 |
| % of medullar blasts |
0.45 (0.10, 0.91) |
| Unknown |
2 |
| Performans status at diagnosis |
|
| 0 |
4 (25.0%) |
| 1 |
9 (56.2%) |
| 2 |
2 (12.5%) |
| NR |
1 (6.2%) |
| Patients wiht cns inflitration |
1 (6.2%) |
##tableau descriptif population LAL RR ----
tbl_summary(
venlaltrr, include = c("age_initiation","old65","sexe", "diagnostic", "nb_ligne_avant_ven","greffe_pre_ven", "blastose", "blastes_medullaires", "oms_diagnostic", "infiltration_snc" ),
digits=all_categorical()~ c(0,1)
)
| Characteristic |
N = 12 |
| Age at initiation |
52 (36, 57) |
| Patients older than 65 |
1 (8.3%) |
| Sex |
|
| F |
4 (33.3%) |
| H |
8 (66.7%) |
| Disease |
|
| LAL biclonale |
1 (8.3%) |
| LAL early T |
1 (8.3%) |
| LAL T |
2 (16.7%) |
| LAL T III |
1 (8.3%) |
| LAL T near ETP |
3 (25.0%) |
| LL T |
4 (33.3%) |
| N previous lines |
|
| 1 |
5 (41.7%) |
| 2 |
6 (50.0%) |
| 3 |
1 (8.3%) |
| Previous allograft |
5 (41.7%) |
| % of circulating blasts |
|
| 0 |
4 (40.0%) |
| 0.25 |
1 (10.0%) |
| 0.28 |
1 (10.0%) |
| 0.52 |
1 (10.0%) |
| 0.67 |
1 (10.0%) |
| 0.93 |
1 (10.0%) |
| 0.96 |
1 (10.0%) |
| Unknown |
2 |
| % of medullar blasts |
|
| 0 |
2 (20.0%) |
| 0.02 |
1 (10.0%) |
| 0.06 |
1 (10.0%) |
| 0.82 |
1 (10.0%) |
| 0.89 |
1 (10.0%) |
| 0.91 |
1 (10.0%) |
| 0.93 |
1 (10.0%) |
| 0.94 |
1 (10.0%) |
| 0.99 |
1 (10.0%) |
| Unknown |
2 |
| Performans status at diagnosis |
|
| 0 |
3 (25.0%) |
| 1 |
7 (58.3%) |
| 2 |
1 (8.3%) |
| NR |
1 (8.3%) |
| Patients wiht cns inflitration |
0 (0.0%) |
##variables continunes, variantes mediane [min-max] N = 16
tbl_summary(
venlalt, include = c("age_initiation","old65","sexe", "diagnostic", "nb_ligne_avant_ven","greffe_pre_ven", "blastose", "blastes_medullaires", "oms_diagnostic", "infiltration_snc" ),
statistic = all_continuous() ~ "{median} [{min} - {max}]",
digits=all_categorical()~ c(0,1)
)
| Characteristic |
N = 16 |
| Age at initiation |
54 [18 - 75] |
| Patients older than 65 |
4 (25.0%) |
| Sex |
|
| F |
5 (31.2%) |
| H |
11 (68.8%) |
| Disease |
|
| LAL biclonale |
1 (6.2%) |
| LAL early T |
1 (6.2%) |
| LAL ETP |
2 (12.5%) |
| LAL near ETP |
1 (6.2%) |
| LAL T |
3 (18.8%) |
| LAL T III |
1 (6.2%) |
| LAL T near ETP |
3 (18.8%) |
| LL T |
4 (25.0%) |
| N previous lines |
|
| 0 |
4 (25.0%) |
| 1 |
5 (31.2%) |
| 2 |
6 (37.5%) |
| 3 |
1 (6.2%) |
| Previous allograft |
5 (31.2%) |
| % of circulating blasts |
|
| 0 |
4 (36.4%) |
| 0.026 |
1 (9.1%) |
| 0.25 |
1 (9.1%) |
| 0.28 |
1 (9.1%) |
| 0.52 |
1 (9.1%) |
| 0.67 |
1 (9.1%) |
| 0.93 |
1 (9.1%) |
| 0.96 |
1 (9.1%) |
| Unknown |
5 |
| % of medullar blasts |
0.45 [0.00 - 0.99] |
| Unknown |
2 |
| Performans status at diagnosis |
|
| 0 |
4 (25.0%) |
| 1 |
9 (56.2%) |
| 2 |
2 (12.5%) |
| NR |
1 (6.2%) |
| Patients wiht cns inflitration |
1 (6.2%) |
##variables continunes, variantes mediane [min-max] LAL RR
tbl_summary(
venlaltrr, include = c("age_initiation","old65","sexe", "diagnostic", "nb_ligne_avant_ven","greffe_pre_ven", "blastose", "blastes_medullaires", "oms_diagnostic", "infiltration_snc" ),
statistic = all_continuous() ~ "{median} [{min} - {max}]",
digits=all_categorical()~ c(0,1)
)
| Characteristic |
N = 12 |
| Age at initiation |
52 [18 - 70] |
| Patients older than 65 |
1 (8.3%) |
| Sex |
|
| F |
4 (33.3%) |
| H |
8 (66.7%) |
| Disease |
|
| LAL biclonale |
1 (8.3%) |
| LAL early T |
1 (8.3%) |
| LAL T |
2 (16.7%) |
| LAL T III |
1 (8.3%) |
| LAL T near ETP |
3 (25.0%) |
| LL T |
4 (33.3%) |
| N previous lines |
|
| 1 |
5 (41.7%) |
| 2 |
6 (50.0%) |
| 3 |
1 (8.3%) |
| Previous allograft |
5 (41.7%) |
| % of circulating blasts |
|
| 0 |
4 (40.0%) |
| 0.25 |
1 (10.0%) |
| 0.28 |
1 (10.0%) |
| 0.52 |
1 (10.0%) |
| 0.67 |
1 (10.0%) |
| 0.93 |
1 (10.0%) |
| 0.96 |
1 (10.0%) |
| Unknown |
2 |
| % of medullar blasts |
|
| 0 |
2 (20.0%) |
| 0.02 |
1 (10.0%) |
| 0.06 |
1 (10.0%) |
| 0.82 |
1 (10.0%) |
| 0.89 |
1 (10.0%) |
| 0.91 |
1 (10.0%) |
| 0.93 |
1 (10.0%) |
| 0.94 |
1 (10.0%) |
| 0.99 |
1 (10.0%) |
| Unknown |
2 |
| Performans status at diagnosis |
|
| 0 |
3 (25.0%) |
| 1 |
7 (58.3%) |
| 2 |
1 (8.3%) |
| NR |
1 (8.3%) |
| Patients wiht cns inflitration |
0 (0.0%) |
##réponses et toxicités N = 16
tbl_summary(
venlalt, include = c("mro","overalresponse", "event_rc","allogreffe_postven", "consequence_clinique_toxicite"),
digits=all_categorical()~ c(0,1)
)
| Characteristic |
N = 16 |
| Best response observed |
|
| dc |
2 (12.5%) |
| pd |
2 (12.5%) |
| rc |
9 (56.2%) |
| rp |
3 (18.8%) |
| Overall response rate |
12 (75.0%) |
| Patients wiht complete response |
9 (56.2%) |
| Patients who underwent allograft |
4 (25.0%) |
| Patients with clinically relevant toxicity |
6 (37.5%) |
##réponses et toxicités N = 12 RR
tbl_summary(
venlaltrr, include = c("mro","overalresponse", "event_rc","allogreffe_postven", "consequence_clinique_toxicite"),
digits=all_categorical()~ c(0,1)
)
| Characteristic |
N = 12 |
| Best response observed |
|
| dc |
2 (16.7%) |
| pd |
2 (16.7%) |
| rc |
6 (50.0%) |
| rp |
2 (16.7%) |
| Overall response rate |
8 (66.7%) |
| Patients wiht complete response |
6 (50.0%) |
| Patients who underwent allograft |
2 (16.7%) |
| Patients with clinically relevant toxicity |
5 (41.7%) |
##Durée de réponse N =16
km_duree_reponse<-survfit(Surv(venlalt$duree_reponse, venlalt$evt_duree_reponse)~1)
km_duree_reponse
## Call: survfit(formula = Surv(venlalt$duree_reponse, venlalt$evt_duree_reponse) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 16 11 5.33 2.57 NA
##Durée de réponse LAL RR
km_duree_reponserr<-survfit(Surv(venlaltrr$duree_reponse, venlaltrr$evt_duree_reponse)~1)
km_duree_reponserr
## Call: survfit(formula = Surv(venlaltrr$duree_reponse, venlaltrr$evt_duree_reponse) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 12 10 5 2.3 NA
##Survies N = 16
##PFS
km_pfs<-survfit(Surv(venlalt$pfs, venlalt$evt_pfs)~1)
km_pfs
## Call: survfit(formula = Surv(venlalt$pfs, venlalt$evt_pfs) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 16 12 6.43 3.87 NA
ggsurvplot(km_pfs, data = venlalt,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
##PFS censurée à la greffe
km_pfs_censure_greffe<-survfit(Surv(venlalt$pfs_censure_greffe, venlalt$evt_pfs_censure_greffe)~1)
km_pfs_censure_greffe
## Call: survfit(formula = Surv(venlalt$pfs_censure_greffe, venlalt$evt_pfs_censure_greffe) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 16 11 6 3.87 NA
ggsurvplot(km_pfs_censure_greffe, data = venlalt,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
##OS
km_os<-survfit(Surv(venlalt$os, venlalt$evt_os)~1)
km_os
## Call: survfit(formula = Surv(venlalt$os, venlalt$evt_os) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 16 9 27.4 5.83 NA
ggsurvplot(km_os, data = venlalt,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
##Survies LAL RR
##PFS
km_pfsrr<-survfit(Surv(venlaltrr$pfs, venlaltrr$evt_pfs)~1)
km_pfsrr
## Call: survfit(formula = Surv(venlaltrr$pfs, venlaltrr$evt_pfs) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 12 11 5.27 0.9 NA
ggsurvplot(km_pfsrr, data = venlaltrr,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
##PFS censurée à la greffe
km_pfs_censure_grefferr<-survfit(Surv(venlaltrr$pfs_censure_greffe, venlaltrr$evt_pfs_censure_greffe)~1)
km_pfs_censure_grefferr
## Call: survfit(formula = Surv(venlaltrr$pfs_censure_greffe, venlaltrr$evt_pfs_censure_greffe) ~
## 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 12 10 4.53 0.9 NA
ggsurvplot(km_pfs_censure_grefferr, data = venlaltrr,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
##OS
km_osrr<-survfit(Surv(venlaltrr$os, venlaltrr$evt_os)~1)
km_osrr
## Call: survfit(formula = Surv(venlaltrr$os, venlaltrr$evt_os) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 12 9 7.8 2.83 NA
ggsurvplot(km_osrr, data = venlaltrr,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
