hl_20260718.R
puisset
2026-07-18
##chargement des packages----
library(questionr)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tableone)
library(labelled)
library(gtsummary)
library(GGally)
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
## Registered S3 method overwritten by 'car':
## method from
## na.action.merMod lme4
##
## Attachement du package : 'survminer'
##
## L'objet suivant est masqué depuis 'package:survival':
##
## myelomalibrary(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
bdd_hl <- read_excel("C:/datar/bdd_hl.xlsx")
data_hl<-filter(bdd_hl, c(eligible=="oui"))
##recodage des variables et bases de données le cas échéant----
data_hl$log10_leucopenie<-log10(data_hl$jours_leuco_g4)
data_hl$carre_leucopenie<-(data_hl$jours_leuco_g4)^2
data_hl$rac_leucopenie<-sqrt(data_hl$jours_leuco_g4)
data_hl$ligne.cat<-ifelse(data_hl$ligne>=4, 1, 0)
data_hl$bsa_plafonnee<-ifelse(data_hl$sc>=2, 1, 0)
data_hl$imc25<-ifelse(data_hl$imc>25, 1, 0)
hist(data_hl$carre_leucopenie, nclass=20)
hist(data_hl$rac_leucopenie, nclass=20)
hist(data_hl$jours_leuco_g4, nclass=20)
##renommer des variables pour présentation dans les tableaux de résultats
library(labelled)
var_label(data_hl$dfg_ckd_sc) <- "DFG selon CKD EPI rapporté à la SC (ml/min)"
var_label(data_hl$classe_ckd) <- "Type de fonction rénale"
var_label(data_hl$cart) <- "CAR T cell"
var_label(data_hl$adaptation_poso) <- "patient avec concession de dose sur fludarabine"
var_label(data_hl$motif_cs) <- "Motif du concession de dose"
var_label(data_hl$n_cart) <- "N cell CART injectées"
var_label(data_hl$cl_tot_estimee) <- "CL fludarabine estimée"
var_label(data_hl$auc_tot_fluda) <- "AUC fludarabine estimée"
##création de variables à plusieurs catégorie selon valeurs variable continue
data_hl$ligne <- case_when(
data_hl$ligne==1 ~ "1ère ligne",
data_hl$ligne==2 ~ "2ème ligne",
data_hl$ligne==3 ~ "3ème ligne",
data_hl$ligne>=4 ~ "4ème ligne et plus",
TRUE ~ "Autre"
)
##
##tableau descriptif population globales ----
###population globale selon fonction rénale
tbl_summary(
data_hl, include = c("age", "sexe",
"poids", "sc","imc",
"dfg_ckd_sc", "classe_ckd", "indication",
"ligne","cart"),
by="classe_ckd",
digits=all_categorical()~ c(0,1)
)%>%
add_overall(last = TRUE)%>%
add_p()## The following warnings were returned during `add_p()`:
## ! For variable `age` (`classe_ckd`) and "estimate", "statistic", "p.value",
## "conf.low", and "conf.high" statistics: impossible de calculer la p-value
## exacte avec des ex-aequos
## ! For variable `age` (`classe_ckd`) and "estimate", "statistic", "p.value",
## "conf.low", and "conf.high" statistics: impossible de calculer les
## intervalles de confiance exacts avec des ex-aequos
## ! For variable `poids` (`classe_ckd`) and "estimate", "statistic", "p.value",
## "conf.low", and "conf.high" statistics: impossible de calculer la p-value
## exacte avec des ex-aequos
## ! For variable `poids` (`classe_ckd`) and "estimate", "statistic", "p.value",
## "conf.low", and "conf.high" statistics: impossible de calculer les
## intervalles de confiance exacts avec des ex-aequos| Characteristic | modéré N = 121 |
normal N = 241 |
Overall N = 361 |
p-value2 |
|---|---|---|---|---|
| age | 70 (61, 72) | 69 (60, 72) | 69 (60, 72) | >0.9 |
| sexe | >0.9 | |||
| Femme | 6 (50.0%) | 12 (50.0%) | 18 (50.0%) | |
| Homme | 6 (50.0%) | 12 (50.0%) | 18 (50.0%) | |
| poids | 59 (52, 72) | 72 (63, 82) | 69 (55, 77) | 0.056 |
| sc | 1.63 (1.53, 1.86) | 1.86 (1.69, 2.01) | 1.81 (1.59, 1.95) | 0.072 |
| imc | 21.9 (19.1, 24.5) | 25.0 (21.3, 27.5) | 23.5 (20.3, 26.4) | 0.14 |
| DFG selon CKD EPI rapporté à la SC (ml/min) | 51 (41, 58) | 92 (82, 111) | 82 (58, 96) | <0.001 |
| indication | >0.9 | |||
| Lymphome B diffus à grandes cellules | 7 (58.3%) | 14 (58.3%) | 21 (58.3%) | |
| Lymphome non hodgkinien autres | 2 (16.7%) | 4 (16.7%) | 6 (16.7%) | |
| Myélome multiple | 3 (25.0%) | 6 (25.0%) | 9 (25.0%) | |
| ligne | >0.9 | |||
| 1ère ligne | 0 (0.0%) | 1 (4.2%) | 1 (2.8%) | |
| 2ème ligne | 1 (8.3%) | 4 (16.7%) | 5 (13.9%) | |
| 3ème ligne | 6 (50.0%) | 10 (41.7%) | 16 (44.4%) | |
| 4ème ligne et plus | 5 (41.7%) | 9 (37.5%) | 14 (38.9%) | |
| CAR T cell | 0.6 | |||
| ABECMA | 3 (25.0%) | 5 (20.8%) | 8 (22.2%) | |
| BREYANZI | 0 (0.0%) | 2 (8.3%) | 2 (5.6%) | |
| CARTITUDE 6 | 0 (0.0%) | 1 (4.2%) | 1 (2.8%) | |
| KYMRIAH | 3 (25.0%) | 1 (4.2%) | 4 (11.1%) | |
| TECARTUS | 1 (8.3%) | 3 (12.5%) | 4 (11.1%) | |
| YESCARTA | 5 (41.7%) | 12 (50.0%) | 17 (47.2%) | |
| 1 Median (Q1, Q3); n (%) | ||||
| 2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Wilcoxon rank sum exact test; Fisher’s exact test | ||||
###données de traitements par fonction rénale
tbl_summary(
data_hl, include = c("dose_tot_endox","dose_endox_mg_m2","dose_tot_fluda", "dose_fluda_mg_m2",
"adaptation_poso",
"motif_cs", "n_cart", "base_leuco"),
by="classe_ckd",
digits=all_categorical()~ c(0,1)
)%>%
add_overall(last=TRUE)%>%
add_p()## The following warnings were returned during `add_p()`:
## ! For variable `base_leuco` (`classe_ckd`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: impossible de calculer la
## p-value exacte avec des ex-aequos
## ! For variable `base_leuco` (`classe_ckd`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: impossible de calculer les
## intervalles de confiance exacts avec des ex-aequos
## ! For variable `dose_tot_endox` (`classe_ckd`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: impossible de calculer la
## p-value exacte avec des ex-aequos
## ! For variable `dose_tot_endox` (`classe_ckd`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: impossible de calculer les
## intervalles de confiance exacts avec des ex-aequos
## ! For variable `dose_tot_fluda` (`classe_ckd`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: impossible de calculer la
## p-value exacte avec des ex-aequos
## ! For variable `dose_tot_fluda` (`classe_ckd`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: impossible de calculer les
## intervalles de confiance exacts avec des ex-aequos
## ! For variable `n_cart` (`classe_ckd`) and "estimate", "statistic", "p.value",
## "conf.low", and "conf.high" statistics: impossible de calculer la p-value
## exacte avec des ex-aequos
## ! For variable `n_cart` (`classe_ckd`) and "estimate", "statistic", "p.value",
## "conf.low", and "conf.high" statistics: impossible de calculer les
## intervalles de confiance exacts avec des ex-aequos| Characteristic | modéré N = 121 |
normal N = 241 |
Overall N = 361 |
p-value2 |
|---|---|---|---|---|
| dose_tot_endox | 1,800 (1,560, 2,280) | 2,340 (1,740, 2,820) | 2,190 (1,710, 2,640) | 0.062 |
| dose_endox_mg_m2 | 342 (292, 506) | 493 (301, 500) | 490 (298, 504) | >0.9 |
| dose_tot_fluda | 128 (86, 135) | 165 (146, 180) | 150 (131, 169) | <0.001 |
| dose_fluda_mg_m2 | 25.2 (14.7, 30.3) | 29.8 (29.2, 30.4) | 29.7 (26.9, 30.4) | 0.10 |
| patient avec concession de dose sur fludarabine | 0.010 | |||
| non | 7 (58.3%) | 23 (95.8%) | 30 (83.3%) | |
| oui | 5 (41.7%) | 1 (4.2%) | 6 (16.7%) | |
| Motif du concession de dose | 0.002 | |||
| dose max | 0 (0.0%) | 1 (4.2%) | 1 (2.8%) | |
| na | 7 (58.3%) | 22 (91.7%) | 29 (80.6%) | |
| NA | 0 (0.0%) | 1 (4.2%) | 1 (2.8%) | |
| rein | 5 (41.7%) | 0 (0.0%) | 5 (13.9%) | |
| N cell CART injectées | 297,500,000 (262,500,000, 393,050,000) | 330,000,000 (260,000,000, 375,000,000) | 310,000,000 (260,000,000, 375,000,000) | >0.9 |
| Unknown | 0 | 1 | 1 | |
| base_leuco | 10.9 (4.8, 16.6) | 4.7 (3.1, 9.4) | 6.5 (3.2, 11.0) | 0.063 |
| 1 Median (Q1, Q3); n (%) | ||||
| 2 Wilcoxon rank sum test; Wilcoxon rank sum exact test; Fisher’s exact test | ||||
###données de traitements par car T
tbl_summary(
data_hl, include = c("dose_tot_endox","dose_endox_mg_m2","dose_tot_fluda", "dose_fluda_mg_m2",
"adaptation_poso",
"motif_cs", "n_cart"),
by="cart",
digits=all_categorical()~ c(0,1)
)%>%
add_overall(last=TRUE)%>%
add_p()| Characteristic | ABECMA N = 81 |
BREYANZI N = 21 |
CARTITUDE 6 N = 11 |
KYMRIAH N = 41 |
TECARTUS N = 41 |
YESCARTA N = 171 |
Overall N = 361 |
p-value2 |
|---|---|---|---|---|---|---|---|---|
| dose_tot_endox | 1,680 (1,530, 1,800) | 1,740 (1,740, 1,740) | 1,629 (1,629, 1,629) | 1,350 (1,290, 1,530) | 2,340 (2,070, 2,700) | 2,580 (2,280, 2,820) | 2,190 (1,710, 2,640) | <0.001 |
| dose_endox_mg_m2 | 300 (291, 305) | 299 (290, 308) | 299 (299, 299) | 254 (252, 274) | 502 (439, 507) | 501 (497, 508) | 490 (298, 504) | <0.001 |
| dose_tot_fluda | 154 (94, 173) | 173 (165, 180) | 163 (163, 163) | 128 (105, 131) | 143 (107, 165) | 158 (135, 173) | 150 (131, 169) | 0.2 |
| dose_fluda_mg_m2 | 28.8 (17.6, 30.3) | 29.6 (29.2, 30.0) | 29.9 (29.9, 29.9) | 24.7 (19.4, 25.2) | 30.7 (22.7, 31.0) | 29.9 (29.4, 30.5) | 29.7 (26.9, 30.4) | 0.060 |
| patient avec concession de dose sur fludarabine | 0.3 | |||||||
| non | 5 (62.5%) | 2 (100.0%) | 1 (100.0%) | 3 (75.0%) | 3 (75.0%) | 16 (94.1%) | 30 (83.3%) | |
| oui | 3 (37.5%) | 0 (0.0%) | 0 (0.0%) | 1 (25.0%) | 1 (25.0%) | 1 (5.9%) | 6 (16.7%) | |
| Motif du concession de dose | 0.4 | |||||||
| dose max | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (5.9%) | 1 (2.8%) | |
| na | 5 (62.5%) | 2 (100.0%) | 1 (100.0%) | 3 (75.0%) | 3 (75.0%) | 15 (88.2%) | 29 (80.6%) | |
| NA | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | 1 (5.9%) | 1 (2.8%) | |
| rein | 3 (37.5%) | 0 (0.0%) | 0 (0.0%) | 1 (25.0%) | 1 (25.0%) | 0 (0.0%) | 5 (13.9%) | |
| N cell CART injectées | 393,050,000 (376,930,000, 433,155,000) | 99,820,000 (99,220,000, 100,420,000) | NA (NA, NA) | 285,000,000 (205,000,000, 395,000,000) | 285,000,000 (197,500,000, 330,000,000) | 300,000,000 (270,000,000, 345,000,000) | 310,000,000 (260,000,000, 375,000,000) | 0.014 |
| Unknown | 0 | 0 | 1 | 0 | 0 | 0 | 1 | |
| 1 Median (Q1, Q3); n (%) | ||||||||
| 2 Kruskal-Wallis rank sum test; Fisher’s exact test | ||||||||
###données PK estimées fludarabine
tbl_summary(
data_hl, include = c("cl_tot_estimee","auc_tot_fluda"),
by="classe_ckd",
digits=all_categorical()~ c(0,1)
)%>%
add_overall(last=TRUE)%>%
add_p()| Characteristic | modéré N = 121 |
normal N = 241 |
Overall N = 361 |
p-value2 |
|---|---|---|---|---|
| CL fludarabine estimée | 5.08 (4.53, 5.57) | 7.59 (6.46, 8.33) | 6.46 (5.47, 8.01) | <0.001 |
| AUC fludarabine estimée | 18.1 (13.0, 22.5) | 17.2 (15.6, 18.1) | 17.5 (15.4, 18.4) | 0.4 |
| 1 Median (Q1, Q3) | ||||
| 2 Wilcoxon rank sum exact test | ||||
##resultats sur leucopénie
#Regression linéaires analyse univariée
##analyses univariées sur durée leucopénie g4
data_hl |>
tbl_uvregression(
y = jours_leuco_g4,
include = c(classe_ckd, imc, imc25, auc_tot_fluda,
dose_tot_fluda, dose_tot_endox,
dose_endox_mg_m2, dose_fluda_mg_m2,
ligne, ligne.cat, bsa_plafonnee, base_leuco),
method = lm,
pvalue_fun = scales::label_pvalue(accuracy = .001),
) |>
bold_labels()| Characteristic | N | Beta | 95% CI | p-value |
|---|---|---|---|---|
| Type de fonction rénale | 36 | |||
| modéré | — | — | ||
| normal | -18 | -38, 2.1 | 0.077 | |
| imc | 36 | -2.3 | -4.8, 0.17 | 0.067 |
| imc25 | 36 | -18 | -37, 1.5 | 0.070 |
| AUC fludarabine estimée | 36 | 4.1 | 1.2, 7.0 | 0.007 |
| dose_tot_fluda | 36 | -0.02 | -0.34, 0.30 | 0.887 |
| dose_tot_endox | 36 | 0.00 | -0.02, 0.02 | 0.888 |
| dose_endox_mg_m2 | 36 | 0.05 | -0.05, 0.14 | 0.301 |
| dose_fluda_mg_m2 | 36 | 1.3 | -0.67, 3.2 | 0.194 |
| ligne | 36 | |||
| 1ère ligne | — | — | ||
| 2ème ligne | 1.6 | -61, 64 | 0.959 | |
| 3ème ligne | 27 | -32, 85 | 0.356 | |
| 4ème ligne et plus | 4.8 | -54, 64 | 0.869 | |
| ligne.cat | 36 | -15 | -35, 4.7 | 0.130 |
| bsa_plafonnee | 36 | -14 | -38, 9.2 | 0.223 |
| base_leuco | 36 | 1.6 | 0.87, 2.3 | <0.001 |
| Abbreviation: CI = Confidence Interval | ||||
##analyses univariées sur racine de (durée leucopénie g4)
data_hl |>
tbl_uvregression(
y = rac_leucopenie,
include = c(classe_ckd, imc, imc25, auc_tot_fluda,
dose_tot_fluda, dose_tot_endox,
dose_endox_mg_m2, dose_fluda_mg_m2,
ligne, ligne.cat, bsa_plafonnee, base_leuco, adaptation_poso),
method = lm,
pvalue_fun = scales::label_pvalue(accuracy = .001),
) |>
bold_labels()| Characteristic | N | Beta | 95% CI | p-value |
|---|---|---|---|---|
| Type de fonction rénale | 36 | |||
| modéré | — | — | ||
| normal | -1.5 | -3.4, 0.43 | 0.125 | |
| imc | 36 | -0.24 | -0.47, -0.01 | 0.043 |
| imc25 | 36 | -2.0 | -3.8, -0.18 | 0.032 |
| AUC fludarabine estimée | 36 | 0.44 | 0.18, 0.71 | 0.002 |
| dose_tot_fluda | 36 | 0.00 | -0.03, 0.03 | 0.966 |
| dose_tot_endox | 36 | 0.00 | 0.00, 0.00 | 0.857 |
| dose_endox_mg_m2 | 36 | 0.01 | 0.00, 0.02 | 0.123 |
| dose_fluda_mg_m2 | 36 | 0.16 | -0.02, 0.33 | 0.081 |
| ligne | 36 | |||
| 1ère ligne | — | — | ||
| 2ème ligne | 1.1 | -4.5, 6.8 | 0.691 | |
| 3ème ligne | 4.0 | -1.3, 9.3 | 0.137 | |
| 4ème ligne et plus | 1.7 | -3.6, 7.1 | 0.512 | |
| ligne.cat | 36 | -1.4 | -3.3, 0.46 | 0.135 |
| bsa_plafonnee | 36 | -1.9 | -4.0, 0.30 | 0.090 |
| base_leuco | 36 | 0.13 | 0.06, 0.20 | <0.001 |
| patient avec concession de dose sur fludarabine | 36 | |||
| non | — | — | ||
| oui | -1.9 | -4.4, 0.52 | 0.120 | |
| Abbreviation: CI = Confidence Interval | ||||
##realtion entre leucos baseline due à une valeur aberrante
##régression linéaire multiple sur racine de durée de leucopénie
mod<-(lm(rac_leucopenie~classe_ckd+imc25+auc_tot_fluda+
dose_endox_mg_m2+dose_fluda_mg_m2+ligne.cat+
bsa_plafonnee+adaptation_poso, data=data_hl))
summary(mod)##
## Call:
## lm(formula = rac_leucopenie ~ classe_ckd + imc25 + auc_tot_fluda +
## dose_endox_mg_m2 + dose_fluda_mg_m2 + ligne.cat + bsa_plafonnee +
## adaptation_poso, data = data_hl)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6932 -1.2775 -0.3628 0.9561 6.4564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.608973 6.515558 -0.400 0.6920
## classe_ckdnormal -4.154583 1.940428 -2.141 0.0415 *
## imc25 -0.288637 1.170556 -0.247 0.8071
## auc_tot_fluda -0.250724 0.368041 -0.681 0.5015
## dose_endox_mg_m2 0.002683 0.005421 0.495 0.6247
## dose_fluda_mg_m2 0.426666 0.305309 1.397 0.1736
## ligne.cat -0.447231 1.010868 -0.442 0.6617
## bsa_plafonnee -1.004742 1.177322 -0.853 0.4009
## adaptation_posooui 0.023944 2.821442 0.008 0.9933
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.438 on 27 degrees of freedom
## Multiple R-squared: 0.3907, Adjusted R-squared: 0.2101
## F-statistic: 2.164 on 8 and 27 DF, p-value: 0.06401mod%>%tbl_regression(intercept = TRUE)| Characteristic | Beta | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -2.6 | -16, 11 | 0.7 |
| Type de fonction rénale | |||
| modéré | — | — | |
| normal | -4.2 | -8.1, -0.17 | 0.041 |
| imc25 | -0.29 | -2.7, 2.1 | 0.8 |
| AUC fludarabine estimée | -0.25 | -1.0, 0.50 | 0.5 |
| dose_endox_mg_m2 | 0.00 | -0.01, 0.01 | 0.6 |
| dose_fluda_mg_m2 | 0.43 | -0.20, 1.1 | 0.2 |
| ligne.cat | -0.45 | -2.5, 1.6 | 0.7 |
| bsa_plafonnee | -1.0 | -3.4, 1.4 | 0.4 |
| patient avec concession de dose sur fludarabine | |||
| non | — | — | |
| oui | 0.02 | -5.8, 5.8 | >0.9 |
| Abbreviation: CI = Confidence Interval | |||
step(mod)## Start: AIC=71.81
## rac_leucopenie ~ classe_ckd + imc25 + auc_tot_fluda + dose_endox_mg_m2 +
## dose_fluda_mg_m2 + ligne.cat + bsa_plafonnee + adaptation_poso
##
## Df Sum of Sq RSS AIC
## - adaptation_poso 1 0.0004 160.49 69.809
## - imc25 1 0.3614 160.85 69.889
## - ligne.cat 1 1.1634 161.65 70.068
## - dose_endox_mg_m2 1 1.4559 161.94 70.134
## - auc_tot_fluda 1 2.7585 163.24 70.422
## - bsa_plafonnee 1 4.3290 164.81 70.767
## <none> 160.48 71.808
## - dose_fluda_mg_m2 1 11.6082 172.09 72.323
## - classe_ckd 1 27.2477 187.73 75.454
##
## Step: AIC=69.81
## rac_leucopenie ~ classe_ckd + imc25 + auc_tot_fluda + dose_endox_mg_m2 +
## dose_fluda_mg_m2 + ligne.cat + bsa_plafonnee
##
## Df Sum of Sq RSS AIC
## - imc25 1 0.3621 160.85 67.890
## - ligne.cat 1 1.2323 161.72 68.084
## - dose_endox_mg_m2 1 1.6582 162.14 68.179
## - auc_tot_fluda 1 2.7870 163.27 68.428
## - bsa_plafonnee 1 4.4370 164.92 68.790
## <none> 160.49 69.809
## - dose_fluda_mg_m2 1 19.2984 179.78 71.896
## - classe_ckd 1 27.2638 187.75 73.457
##
## Step: AIC=67.89
## rac_leucopenie ~ classe_ckd + auc_tot_fluda + dose_endox_mg_m2 +
## dose_fluda_mg_m2 + ligne.cat + bsa_plafonnee
##
## Df Sum of Sq RSS AIC
## - dose_endox_mg_m2 1 1.3687 162.22 66.195
## - ligne.cat 1 2.0011 162.85 66.335
## - auc_tot_fluda 1 2.4336 163.28 66.430
## - bsa_plafonnee 1 5.9318 166.78 67.193
## <none> 160.85 67.890
## - dose_fluda_mg_m2 1 19.0006 179.85 69.909
## - classe_ckd 1 27.1061 187.95 71.496
##
## Step: AIC=66.19
## rac_leucopenie ~ classe_ckd + auc_tot_fluda + dose_fluda_mg_m2 +
## ligne.cat + bsa_plafonnee
##
## Df Sum of Sq RSS AIC
## - auc_tot_fluda 1 1.6606 163.88 64.561
## - ligne.cat 1 3.4864 165.70 64.960
## - bsa_plafonnee 1 4.9706 167.19 65.281
## <none> 162.22 66.195
## - dose_fluda_mg_m2 1 18.6176 180.83 68.106
## - classe_ckd 1 25.9193 188.13 69.531
##
## Step: AIC=64.56
## rac_leucopenie ~ classe_ckd + dose_fluda_mg_m2 + ligne.cat +
## bsa_plafonnee
##
## Df Sum of Sq RSS AIC
## - ligne.cat 1 3.070 166.95 63.229
## - bsa_plafonnee 1 3.818 167.69 63.390
## <none> 163.88 64.561
## - classe_ckd 1 49.981 213.86 72.144
## - dose_fluda_mg_m2 1 52.790 216.67 72.614
##
## Step: AIC=63.23
## rac_leucopenie ~ classe_ckd + dose_fluda_mg_m2 + bsa_plafonnee
##
## Df Sum of Sq RSS AIC
## - bsa_plafonnee 1 4.447 171.39 62.176
## <none> 166.95 63.229
## - classe_ckd 1 52.985 219.93 71.153
## - dose_fluda_mg_m2 1 64.337 231.28 72.965
##
## Step: AIC=62.18
## rac_leucopenie ~ classe_ckd + dose_fluda_mg_m2
##
## Df Sum of Sq RSS AIC
## <none> 171.39 62.176
## - classe_ckd 1 69.046 240.44 72.362
## - dose_fluda_mg_m2 1 74.076 245.47 73.107##
## Call:
## lm(formula = rac_leucopenie ~ classe_ckd + dose_fluda_mg_m2,
## data = data_hl)
##
## Coefficients:
## (Intercept) classe_ckdnormal dose_fluda_mg_m2
## -4.4007 -3.5379 0.3402##modèle final
mod<-(lm(rac_leucopenie~classe_ckd+dose_fluda_mg_m2
, data=data_hl))
summary(mod)##
## Call:
## lm(formula = rac_leucopenie ~ classe_ckd + dose_fluda_mg_m2,
## data = data_hl)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1001 -1.1797 -0.4083 0.9152 7.0506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.40071 2.21664 -1.985 0.055473 .
## classe_ckdnormal -3.53793 0.97033 -3.646 0.000907 ***
## dose_fluda_mg_m2 0.34021 0.09008 3.777 0.000631 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.279 on 33 degrees of freedom
## Multiple R-squared: 0.3492, Adjusted R-squared: 0.3098
## F-statistic: 8.855 on 2 and 33 DF, p-value: 0.0008345mod%>%tbl_regression(intercept = TRUE)| Characteristic | Beta | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -4.4 | -8.9, 0.11 | 0.055 |
| Type de fonction rénale | |||
| modéré | — | — | |
| normal | -3.5 | -5.5, -1.6 | <0.001 |
| dose_fluda_mg_m2 | 0.34 | 0.16, 0.52 | <0.001 |
| Abbreviation: CI = Confidence Interval | |||
hist(resid(mod), col="grey",nclass=10, main="")
##régression linéaire multiple sur durée de leucopénie
mod<-(lm(jours_leuco_g4~classe_ckd+imc25+auc_tot_fluda+
dose_endox_mg_m2+dose_fluda_mg_m2+ligne.cat+
bsa_plafonnee, data=data_hl))
summary(mod)##
## Call:
## lm(formula = jours_leuco_g4 ~ classe_ckd + imc25 + auc_tot_fluda +
## dose_endox_mg_m2 + dose_fluda_mg_m2 + ligne.cat + bsa_plafonnee,
## data = data_hl)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.232 -12.311 -2.543 5.448 72.573
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -29.495254 34.156679 -0.864 0.3952
## classe_ckdnormal -44.684788 21.107244 -2.117 0.0433 *
## imc25 0.031973 12.732361 0.003 0.9980
## auc_tot_fluda -2.033236 3.988167 -0.510 0.6142
## dose_endox_mg_m2 -0.003045 0.055580 -0.055 0.9567
## dose_fluda_mg_m2 4.160318 2.566323 1.621 0.1162
## ligne.cat -9.271177 10.731553 -0.864 0.3950
## bsa_plafonnee -4.475356 12.631534 -0.354 0.7258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 26.53 on 28 degrees of freedom
## Multiple R-squared: 0.3366, Adjusted R-squared: 0.1707
## F-statistic: 2.029 on 7 and 28 DF, p-value: 0.08654mod%>%tbl_regression(intercept = TRUE)| Characteristic | Beta | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -29 | -99, 40 | 0.4 |
| Type de fonction rénale | |||
| modéré | — | — | |
| normal | -45 | -88, -1.4 | 0.043 |
| imc25 | 0.03 | -26, 26 | >0.9 |
| AUC fludarabine estimée | -2.0 | -10, 6.1 | 0.6 |
| dose_endox_mg_m2 | 0.00 | -0.12, 0.11 | >0.9 |
| dose_fluda_mg_m2 | 4.2 | -1.1, 9.4 | 0.12 |
| ligne.cat | -9.3 | -31, 13 | 0.4 |
| bsa_plafonnee | -4.5 | -30, 21 | 0.7 |
| Abbreviation: CI = Confidence Interval | |||
step(mod)## Start: AIC=242.98
## jours_leuco_g4 ~ classe_ckd + imc25 + auc_tot_fluda + dose_endox_mg_m2 +
## dose_fluda_mg_m2 + ligne.cat + bsa_plafonnee
##
## Df Sum of Sq RSS AIC
## - imc25 1 0.00 19701 240.98
## - dose_endox_mg_m2 1 2.11 19703 240.98
## - bsa_plafonnee 1 88.32 19789 241.14
## - auc_tot_fluda 1 182.87 19884 241.31
## - ligne.cat 1 525.13 20226 241.92
## <none> 19701 242.98
## - dose_fluda_mg_m2 1 1849.07 21550 244.21
## - classe_ckd 1 3153.40 22854 246.32
##
## Step: AIC=240.98
## jours_leuco_g4 ~ classe_ckd + auc_tot_fluda + dose_endox_mg_m2 +
## dose_fluda_mg_m2 + ligne.cat + bsa_plafonnee
##
## Df Sum of Sq RSS AIC
## - dose_endox_mg_m2 1 2.22 19703 238.98
## - bsa_plafonnee 1 98.36 19799 239.16
## - auc_tot_fluda 1 202.54 19903 239.34
## - ligne.cat 1 599.08 20300 240.05
## <none> 19701 240.98
## - dose_fluda_mg_m2 1 1861.26 21562 242.23
## - classe_ckd 1 3156.22 22857 244.33
##
## Step: AIC=238.98
## jours_leuco_g4 ~ classe_ckd + auc_tot_fluda + dose_fluda_mg_m2 +
## ligne.cat + bsa_plafonnee
##
## Df Sum of Sq RSS AIC
## - bsa_plafonnee 1 110.7 19814 237.18
## - auc_tot_fluda 1 231.7 19935 237.40
## - ligne.cat 1 637.6 20341 238.13
## <none> 19703 238.98
## - dose_fluda_mg_m2 1 1869.6 21573 240.24
## - classe_ckd 1 3247.3 22950 242.47
##
## Step: AIC=237.18
## jours_leuco_g4 ~ classe_ckd + auc_tot_fluda + dose_fluda_mg_m2 +
## ligne.cat
##
## Df Sum of Sq RSS AIC
## - auc_tot_fluda 1 165.3 19979 235.48
## - ligne.cat 1 669.0 20483 236.38
## <none> 19814 237.18
## - dose_fluda_mg_m2 1 1768.3 21582 238.26
## - classe_ckd 1 3186.1 23000 240.55
##
## Step: AIC=235.48
## jours_leuco_g4 ~ classe_ckd + dose_fluda_mg_m2 + ligne.cat
##
## Df Sum of Sq RSS AIC
## - ligne.cat 1 602.8 20582 234.55
## <none> 19979 235.48
## - dose_fluda_mg_m2 1 4916.6 24896 241.40
## - classe_ckd 1 6984.9 26964 244.27
##
## Step: AIC=234.55
## jours_leuco_g4 ~ classe_ckd + dose_fluda_mg_m2
##
## Df Sum of Sq RSS AIC
## <none> 20582 234.55
## - dose_fluda_mg_m2 1 6473.9 27056 242.40
## - classe_ckd 1 7656.3 28238 243.94##
## Call:
## lm(formula = jours_leuco_g4 ~ classe_ckd + dose_fluda_mg_m2,
## data = data_hl)
##
## Coefficients:
## (Intercept) classe_ckdnormal dose_fluda_mg_m2
## -48.57 -37.26 3.18##modèle final sur durée de leucopénie (plus facile à interpréter)
mod<-(lm(jours_leuco_g4~classe_ckd+dose_fluda_mg_m2, data=data_hl))
summary(mod)##
## Call:
## lm(formula = jours_leuco_g4 ~ classe_ckd + dose_fluda_mg_m2,
## data = data_hl)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44.095 -9.244 -4.096 3.285 78.326
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -48.5671 24.2908 -1.999 0.05385 .
## classe_ckdnormal -37.2554 10.6332 -3.504 0.00134 **
## dose_fluda_mg_m2 3.1805 0.9872 3.222 0.00286 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.97 on 33 degrees of freedom
## Multiple R-squared: 0.3069, Adjusted R-squared: 0.2649
## F-statistic: 7.307 on 2 and 33 DF, p-value: 0.00236mod%>%tbl_regression(intercept = TRUE)| Characteristic | Beta | 95% CI | p-value |
|---|---|---|---|
| (Intercept) | -49 | -98, 0.85 | 0.054 |
| Type de fonction rénale | |||
| modéré | — | — | |
| normal | -37 | -59, -16 | 0.001 |
| dose_fluda_mg_m2 | 3.2 | 1.2, 5.2 | 0.003 |
| Abbreviation: CI = Confidence Interval | |||
##vérification distribution normale du bruit résiduel
hist(resid(mod), col="grey",nclass=10, main="")
##illustre que la transformation en racine carré de la durée de leucopénie est préférable
##ANALYSE SUR L'EXPANSION CART
data_pic<-filter(data_hl, c(pic_cd4_cd8!=""))
hist(data_pic$pic_cd4_cd8, nclass=10)
data_pic$log_pic<-log10(data_pic$pic_cd4_cd8)
hist(data_pic$log_pic, nclass=10)
data_pic$rac_pic<-sqrt(data_pic$pic_cd4_cd8)
hist(data_pic$rac_pic, nclass=10)
data_pic$car_pic<-(data_pic$pic_cd4_cd8)^2
hist(data_pic$car_pic, nclass=10)
##==> chois de transformation racine carré du pic
##analyses univariées sur racine de pic cart
data_pic |>
tbl_uvregression(
y = rac_pic,
include = c(classe_ckd, imc, imc25, auc_tot_fluda,
dose_tot_fluda, dose_tot_endox,
dose_endox_mg_m2, dose_fluda_mg_m2,
ligne, ligne.cat, bsa_plafonnee, base_leuco, n_cart),
method = lm,
pvalue_fun = scales::label_pvalue(accuracy = .001),
) |>
bold_labels()| Characteristic | N | Beta | 95% CI | p-value |
|---|---|---|---|---|
| Type de fonction rénale | 19 | |||
| modéré | — | — | ||
| normal | 2.6 | -7.3, 13 | 0.584 | |
| imc | 19 | 0.47 | -0.81, 1.7 | 0.452 |
| imc25 | 19 | 4.2 | -5.3, 14 | 0.363 |
| AUC fludarabine estimée | 19 | -0.85 | -2.2, 0.49 | 0.199 |
| dose_tot_fluda | 19 | -0.03 | -0.17, 0.11 | 0.665 |
| dose_tot_endox | 19 | 0.00 | -0.01, 0.00 | 0.369 |
| dose_endox_mg_m2 | 19 | -0.02 | -0.07, 0.03 | 0.352 |
| dose_fluda_mg_m2 | 19 | -0.28 | -1.1, 0.57 | 0.501 |
| ligne | 19 | |||
| 2ème ligne | — | — | ||
| 3ème ligne | 0.33 | -13, 14 | 0.959 | |
| 4ème ligne et plus | 4.5 | -8.6, 18 | 0.480 | |
| ligne.cat | 19 | 4.3 | -5.3, 14 | 0.362 |
| bsa_plafonnee | 19 | -5.4 | -15, 4.7 | 0.276 |
| base_leuco | 19 | -0.08 | -0.41, 0.25 | 0.616 |
| N cell CART injectées | 19 | 0.00 | 0.00, 0.00 | 0.383 |
| Abbreviation: CI = Confidence Interval | ||||
##AUC fluda = seule covariable sélectionnable (p<0.2) donc pas d'analyse mutlivariée
##description de la réponse
tbl_summary(
data_hl, include = c("best_result","delai_best_result"),
by="classe_ckd",
digits=all_categorical()~ c(0,1)
)%>%
add_overall(last=TRUE)%>%
add_p()## The following warnings were returned during `add_p()`:
## ! For variable `delai_best_result` (`classe_ckd`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: impossible de calculer la
## p-value exacte avec des ex-aequos
## ! For variable `delai_best_result` (`classe_ckd`) and "estimate", "statistic",
## "p.value", "conf.low", and "conf.high" statistics: impossible de calculer les
## intervalles de confiance exacts avec des ex-aequos| Characteristic | modéré N = 121 |
normal N = 241 |
Overall N = 361 |
p-value2 |
|---|---|---|---|---|
| best_result | <0.001 | |||
| progression | 0 (0.0%) | 2 (8.3%) | 2 (5.6%) | |
| rc | 5 (41.7%) | 22 (91.7%) | 27 (75.0%) | |
| rp | 7 (58.3%) | 0 (0.0%) | 7 (19.4%) | |
| delai_best_result | 29 (28, 32) | 30 (27, 40) | 29 (28, 34) | >0.9 |
| 1 n (%); Median (Q1, Q3) | ||||
| 2 Fisher’s exact test; Wilcoxon rank sum test | ||||
##REULTATS SUR PFS
##Description graphique population globale
##Courbe kaplan Meier Population globale
km_pfs<-survfit(Surv(data_hl$pfs, data_hl$evt_pfs)~1)
km_pfs## Call: survfit(formula = Surv(data_hl$pfs, data_hl$evt_pfs) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 36 26 11.1 6.1 31ggsurvplot(
km_pfs, # survfit object with calculated statistics.
data = data_hl, # data used to fit survival curves.
risk.table = TRUE, # show risk table.
pval = FALSE, # show p-value of log-rank test.
conf.int = TRUE, # show confidence intervals for
# point estimates of survival curves.
palette = c("#2E9FDF"),
xlim = c(0,66), # present narrower X axis, but not affect
# survival estimates.
xlab = "Time in months", # customize X axis label.
break.time.by = 6, # break X axis in time intervals by 500.
ggtheme = theme_light(), # customize plot and risk table with a theme.
risk.table.y.text.col = T,# colour risk table text annotations.
risk.table.height = 0.25, # the height of the risk table
risk.table.y.text = FALSE,# show bars instead of names in text annotations
# in legend of risk table.
ncensor.plot = FALSE, # plot the number of censored subjects at time t
ncensor.plot.height = 0.25,
conf.int.style = "step", # customize style of confidence intervals
surv.median.line = "hv", # add the median survival pointer.
#legend.labs =
#c("no", "yes") # change legend labels.
)## Ignoring unknown labels:
## • colour : "Strata"
##PFS par pathologie
##Courbe kaplan Meier selon indication
km__pfs_indication<-survfit(Surv(pfs, evt_pfs)~indication, data=data_hl)
km__pfs_indication## Call: survfit(formula = Surv(pfs, evt_pfs) ~ indication, data = data_hl)
##
## n events median 0.95LCL
## indication=Lymphome B diffus à grandes cellules 21 16 12.5 3.13
## indication=Lymphome non hodgkinien autres 6 3 31.0 11.10
## indication=Myélome multiple 9 7 6.1 3.07
## 0.95UCL
## indication=Lymphome B diffus à grandes cellules NA
## indication=Lymphome non hodgkinien autres NA
## indication=Myélome multiple NAggsurvplot(
km__pfs_indication, # survfit object with calculated statistics.
data = data_hl, # data used to fit survival curves.
risk.table = TRUE, # show risk table.
pval = TRUE, # show p-value of log-rank test.
conf.int = TRUE, # show confidence intervals for
# point estimates of survival curves.
palette = c("#E7B800", "#2E9FDF", "green4"),
xlim = c(0,66), # present narrower X axis, but not affect
# survival estimates.
xlab = "Time in months", # customize X axis label.
break.time.by = 6, # break X axis in time intervals by 500.
ggtheme = theme_light(), # customize plot and risk table with a theme.
risk.table.y.text.col = T,# colour risk table text annotations.
risk.table.height = 0.25, # the height of the risk table
risk.table.y.text = FALSE,# show bars instead of names in text annotations
# in legend of risk table.
ncensor.plot = FALSE, # plot the number of censored subjects at time t
ncensor.plot.height = 0.25,
conf.int.style = "step", # customize style of confidence intervals
surv.median.line = "hv", # add the median survival pointer.
#legend.labs =
#c("no", "yes") # change legend labels.
)## Ignoring unknown labels:
## • colour : "Strata"
##Avec les pfs pondérées
km__pfs_indication_pond<-survfit(Surv(pfs_ponderee, evt_pfs)~indication, data=data_hl)
km__pfs_indication_pond## Call: survfit(formula = Surv(pfs_ponderee, evt_pfs) ~ indication, data = data_hl)
##
## n events median 0.95LCL
## indication=Lymphome B diffus à grandes cellules 21 16 1.706 0.537
## indication=Lymphome non hodgkinien autres 6 3 2.103 1.200
## indication=Myélome multiple 9 7 0.441 0.348
## 0.95UCL
## indication=Lymphome B diffus à grandes cellules NA
## indication=Lymphome non hodgkinien autres NA
## indication=Myélome multiple NAggsurvplot(
km__pfs_indication_pond, # survfit object with calculated statistics.
data = data_hl, # data used to fit survival curves.
risk.table = TRUE, # show risk table.
pval = TRUE, # show p-value of log-rank test.
conf.int = TRUE, # show confidence intervals for
# point estimates of survival curves.
palette = c("#E7B800", "#2E9FDF", "green4"),
xlim = c(0,12), # present narrower X axis, but not affect
# survival estimates.
xlab = "Time in months", # customize X axis label.
break.time.by = 3, # break X axis in time intervals by 500.
ggtheme = theme_light(), # customize plot and risk table with a theme.
risk.table.y.text.col = T,# colour risk table text annotations.
risk.table.height = 0.25, # the height of the risk table
risk.table.y.text = FALSE,# show bars instead of names in text annotations
# in legend of risk table.
ncensor.plot = FALSE, # plot the number of censored subjects at time t
ncensor.plot.height = 0.25,
conf.int.style = "step", # customize style of confidence intervals
surv.median.line = "hv", # add the median survival pointer.
#legend.labs =
#c("no", "yes") # change legend labels.
)## Ignoring unknown labels:
## • colour : "Strata"
##Courbe kaplan Meier selon CAR T
km__pfs_cart<-survfit(Surv(pfs, evt_pfs)~cart, data=data_hl)
km__pfs_cart## Call: survfit(formula = Surv(pfs, evt_pfs) ~ cart, data = data_hl)
##
## n events median 0.95LCL 0.95UCL
## cart=ABECMA 8 7 4.917 3.067 NA
## cart=BREYANZI 2 1 0.933 0.933 NA
## cart=CARTITUDE 6 1 0 NA NA NA
## cart=KYMRIAH 4 3 3.983 1.533 NA
## cart=TECARTUS 4 3 26.067 11.100 NA
## cart=YESCARTA 17 12 17.767 9.633 NAggsurvplot(
km__pfs_cart, # survfit object with calculated statistics.
data = data_hl, # data used to fit survival curves.
risk.table = TRUE, # show risk table.
pval = TRUE, # show p-value of log-rank test.
conf.int = TRUE, # show confidence intervals for
# point estimates of survival curves.
palette = c("#E7B800", "#2E9FDF", "green4", "red", "purple", "grey"),
xlim = c(0,66), # present narrower X axis, but not affect
# survival estimates.
xlab = "Time in months", # customize X axis label.
break.time.by = 3, # break X axis in time intervals by 500.
ggtheme = theme_light(), # customize plot and risk table with a theme.
risk.table.y.text.col = T,# colour risk table text annotations.
risk.table.height = 0.25, # the height of the risk table
risk.table.y.text = FALSE,# show bars instead of names in text annotations
# in legend of risk table.
ncensor.plot = FALSE, # plot the number of censored subjects at time t
ncensor.plot.height = 0.25,
conf.int.style = "step", # customize style of confidence intervals
surv.median.line = "hv", # add the median survival pointer.
#legend.labs =
#c("no", "yes") # change legend labels.
)## Ignoring unknown labels:
## • colour : "Strata"
##Courbe kaplan Meier selon CAR T avec pfs pondéréees
km__pfs_cart_pond<-survfit(Surv(pfs_ponderee, evt_pfs)~cart, data=data_hl)
km__pfs_cart_pond## Call: survfit(formula = Surv(pfs_ponderee, evt_pfs) ~ cart, data = data_hl)
##
## n events median 0.95LCL 0.95UCL
## cart=ABECMA 8 7 0.441 0.348 NA
## cart=BREYANZI 2 1 0.137 0.137 NA
## cart=CARTITUDE 6 1 0 NA NA NA
## cart=KYMRIAH 4 3 1.374 0.529 NA
## cart=TECARTUS 4 3 1.010 0.430 NA
## cart=YESCARTA 17 12 2.124 1.633 NAggsurvplot(
km__pfs_cart_pond, # survfit object with calculated statistics.
data = data_hl, # data used to fit survival curves.
risk.table = TRUE, # show risk table.
pval = TRUE, # show p-value of log-rank test.
conf.int = TRUE, # show confidence intervals for
# point estimates of survival curves.
palette = c("#E7B800", "#2E9FDF", "green4", "red", "purple", "grey"),
xlim = c(0,12), # present narrower X axis, but not affect
# survival estimates.
xlab = "Time in months", # customize X axis label.
break.time.by = 3, # break X axis in time intervals by 500.
ggtheme = theme_light(), # customize plot and risk table with a theme.
risk.table.y.text.col = T,# colour risk table text annotations.
risk.table.height = 0.25, # the height of the risk table
risk.table.y.text = FALSE,# show bars instead of names in text annotations
# in legend of risk table.
ncensor.plot = FALSE, # plot the number of censored subjects at time t
ncensor.plot.height = 0.25,
conf.int.style = "step", # customize style of confidence intervals
surv.median.line = "hv", # add the median survival pointer.
#legend.labs =
#c("no", "yes") # change legend labels.
)## Ignoring unknown labels:
## • colour : "Strata"
##MODELE DE COX SUR PFS PONDEREE
##analyses univariée tableau récap plusieurs variables
tbl_uvregression(
data_hl,
method = coxph,
y = Surv(pfs_ponderee, evt_pfs),
exponentiate = TRUE,
include = c(classe_ckd, indication, ligne.cat,auc_tot_fluda,
dose_tot_fluda, dose_tot_endox,
dose_endox_mg_m2, dose_fluda_mg_m2,
ligne, ligne.cat, bsa_plafonnee, base_leuco, n_cart),
pvalue_fun = scales::label_pvalue(accuracy = .001)
)| Characteristic | N | HR | 95% CI | p-value |
|---|---|---|---|---|
| Type de fonction rénale | 36 | |||
| modéré | — | — | ||
| normal | 0.34 | 0.14, 0.81 | 0.014 | |
| indication | 36 | |||
| Lymphome B diffus à grandes cellules | — | — | ||
| Lymphome non hodgkinien autres | 0.71 | 0.21, 2.47 | 0.595 | |
| Myélome multiple | 3.40 | 1.27, 9.13 | 0.015 | |
| ligne.cat | 36 | 0.73 | 0.32, 1.64 | 0.448 |
| AUC fludarabine estimée | 36 | 1.10 | 0.97, 1.24 | 0.134 |
| dose_tot_fluda | 36 | 0.99 | 0.98, 1.00 | 0.133 |
| dose_tot_endox | 36 | 1.00 | 1.00, 1.00 | 0.076 |
| dose_endox_mg_m2 | 36 | 1.00 | 1.0, 1.00 | 0.356 |
| dose_fluda_mg_m2 | 36 | 0.99 | 0.91, 1.07 | 0.786 |
| ligne | 36 | |||
| 1ère ligne | — | — | ||
| 2ème ligne | 0.61 | 0.13, 2.84 | 0.526 | |
| 3ème ligne | 1.64 | 0.72, 3.76 | 0.239 | |
| 4ème ligne et plus | ||||
| bsa_plafonnee | 36 | 0.53 | 0.18, 1.58 | 0.255 |
| base_leuco | 36 | 1.01 | 0.98, 1.05 | 0.355 |
| N cell CART injectées | 35 | 1.00 | 1.00, 1.00 | 0.390 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | ||||
##modèle mutlivarié
modsurv<-coxph(Surv(pfs_ponderee, evt_pfs)~classe_ckd+indication+auc_tot_fluda+
dose_tot_fluda+ dose_tot_endox+ ligne, data=data_hl)
modsurv%>%tbl_regression(
exponentiate = TRUE,pvalue_fun = scales::label_pvalue(accuracy = .001)
)| Characteristic | HR | 95% CI | p-value |
|---|---|---|---|
| Type de fonction rénale | |||
| modéré | — | — | |
| normal | 0.34 | 0.07, 1.82 | 0.210 |
| indication | |||
| Lymphome B diffus à grandes cellules | — | — | |
| Lymphome non hodgkinien autres | 0.74 | 0.12, 4.67 | 0.746 |
| Myélome multiple | 4.86 | 0.99, 23.9 | 0.052 |
| AUC fludarabine estimée | 1.04 | 0.87, 1.24 | 0.662 |
| dose_tot_fluda | 1.01 | 0.98, 1.03 | 0.550 |
| dose_tot_endox | 1.00 | 1.00, 1.00 | 0.333 |
| ligne | |||
| 1ère ligne | — | — | |
| 2ème ligne | 1.34 | 0.23, 7.88 | 0.746 |
| 3ème ligne | 2.61 | 0.88, 7.76 | 0.085 |
| 4ème ligne et plus | |||
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||
step(modsurv)## Start: AIC=145.97
## Surv(pfs_ponderee, evt_pfs) ~ classe_ckd + indication + auc_tot_fluda +
## dose_tot_fluda + dose_tot_endox + ligne
##
## Df AIC
## - auc_tot_fluda 1 144.16
## - dose_tot_fluda 1 144.33
## - dose_tot_endox 1 144.90
## - ligne 2 144.94
## - classe_ckd 1 145.66
## <none> 145.97
## - indication 2 149.28
##
## Step: AIC=144.16
## Surv(pfs_ponderee, evt_pfs) ~ classe_ckd + indication + dose_tot_fluda +
## dose_tot_endox + ligne
##
## Df AIC
## - dose_tot_fluda 1 142.90
## - dose_tot_endox 1 143.72
## <none> 144.16
## - classe_ckd 1 144.55
## - ligne 2 145.10
## - indication 2 147.33
##
## Step: AIC=142.9
## Surv(pfs_ponderee, evt_pfs) ~ classe_ckd + indication + dose_tot_endox +
## ligne
##
## Df AIC
## - dose_tot_endox 1 142.18
## - classe_ckd 1 142.81
## <none> 142.90
## - ligne 2 143.98
## - indication 2 147.18
##
## Step: AIC=142.18
## Surv(pfs_ponderee, evt_pfs) ~ classe_ckd + indication + ligne
##
## Df AIC
## <none> 142.18
## - ligne 2 142.37
## - classe_ckd 1 144.30
## - indication 2 146.88## Call:
## coxph(formula = Surv(pfs_ponderee, evt_pfs) ~ classe_ckd + indication +
## ligne, data = data_hl)
##
## coef exp(coef) se(coef) z
## classe_ckdnormal -0.9043 0.4048 0.4407 -2.052
## indicationLymphome non hodgkinien autres -0.4682 0.6261 0.6464 -0.724
## indicationMyélome multiple 1.6339 5.1236 0.6111 2.674
## ligne2ème ligne 0.2245 1.2517 0.8792 0.255
## ligne3ème ligne 0.9692 2.6359 0.5068 1.913
## ligne4ème ligne et plus NA NA 0.0000 NA
## p
## classe_ckdnormal 0.0402
## indicationLymphome non hodgkinien autres 0.4689
## indicationMyélome multiple 0.0075
## ligne2ème ligne 0.7985
## ligne3ème ligne 0.0558
## ligne4ème ligne et plus NA
##
## Likelihood ratio test=15.66 on 5 df, p=0.007887
## n= 36, number of events= 26##modèle final
modsurv<-coxph(Surv(pfs_ponderee, evt_pfs)~classe_ckd+indication+auc_tot_fluda, data=data_hl)
modsurv%>%tbl_regression(
exponentiate = TRUE,pvalue_fun = scales::label_pvalue(accuracy = .001)
)| Characteristic | HR | 95% CI | p-value |
|---|---|---|---|
| Type de fonction rénale | |||
| modéré | — | — | |
| normal | 0.45 | 0.18, 1.09 | 0.077 |
| indication | |||
| Lymphome B diffus à grandes cellules | — | — | |
| Lymphome non hodgkinien autres | 1.12 | 0.29, 4.37 | 0.874 |
| Myélome multiple | 5.68 | 1.68, 19.2 | 0.005 |
| AUC fludarabine estimée | 1.14 | 0.99, 1.31 | 0.075 |
| Abbreviations: CI = Confidence Interval, HR = Hazard Ratio | |||