###Analyse Trastuzumab_dxd et IMC
##chargement des packages----
library(questionr)
library(tidyverse)
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## ✔ purrr 1.0.1
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library(tableone)
library(labelled)
library(gtsummary)
## #Uighur
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)
##chargement des données globales
data_julie <- read_excel("C:/datar/data_julie.xlsx")
## New names:
## • `Statut HER 2 (0=neg;1=pos;2=low)` -> `Statut HER 2 (0=neg;1=pos;2=low)...29`
## • `Détail ligne 2` -> `Détail ligne 2...36`
## • `Détail ligne 2` -> `Détail ligne 2...37`
## • `Détail autre` -> `Détail autre...42`
## • `Détail autre` -> `Détail autre...51`
## • `Statut HER 2 (0=neg;1=pos;2=low)` -> `Statut HER 2 (0=neg;1=pos;2=low)...82`
##chargement des données description des tooxicités
data_tox <- read_excel("C:/datar/data_tox.xlsx")
## New names:
## • `Statut HER 2 (0=neg;1=pos;2=low)` -> `Statut HER 2 (0=neg;1=pos;2=low)...29`
## • `Détail ligne 2` -> `Détail ligne 2...36`
## • `Détail ligne 2` -> `Détail ligne 2...37`
## • `Détail autre` -> `Détail autre...42`
## • `Détail autre` -> `Détail autre...51`
## • `Statut HER 2 (0=neg;1=pos;2=low)` -> `Statut HER 2 (0=neg;1=pos;2=low)...82`
##recodage des variables et bases de données le cas échéant----
data_julie$imc25<-ifelse(data_julie$imc>25, 1, 0)
data_julie$imc30<-ifelse(data_julie$imc>30, 1, 0)
data_julie$old<-ifelse(data_julie$age>66, 1, 0)
data_julie$poidslourds<-ifelse(data_julie$poids>71, 1, 0)
data_tox$imc25<-ifelse(data_tox$imc>25, 1, 0)
data_tox$imc30<-ifelse(data_tox$imc>30, 1, 0)
tdxd<-filter(data_julie, c(eligible=="oui" ))
her2low<-filter(tdxd, c(her2_status=="low" ))
her2pos<-filter(tdxd, c(her2_status=="positive" ))
tox<-filter(data_tox, c(eligible=="oui" ))
toxher2low<-filter(tox, c(her2_status=="low" ))
toxher2pos<-filter(tox, c(her2_status=="positive" ))
##description population
tbl_summary(
tdxd, include = c("age", "poids", "taille", "imc", "imc25", "imc30", "her2_status", "rh", "Score ECOG", "oms.cat", "meta_liver", "meta_brain", "meta_lung", "previous_lines", "chemotherapy", "HER2i", "icdk4_6", "endocrine_therapy", "immunotherapy", "sacituzumab_govitecan", "hypoalbuminemie", "hyperbilirubinemie", "asat3n", "concession_initiale"),
)
| Characteristic |
N = 93 |
| age |
59 (48, 66) |
| poids |
61 (52, 71) |
| taille |
162 (158, 166) |
| imc |
23.6 (20.0, 26.2) |
| imc25 |
33 (35%) |
| imc30 |
7 (7.5%) |
| her2_status |
|
| low |
45 (48%) |
| positive |
48 (52%) |
| rh |
|
| 0 |
18 (19%) |
| 1 |
74 (80%) |
| NR |
1 (1.1%) |
| Score ECOG |
|
| 0 |
22 (24%) |
| 0/1 |
1 (1.1%) |
| 1 |
52 (56%) |
| 2 |
14 (15%) |
| 3 |
1 (1.1%) |
| NR |
3 (3.2%) |
| oms.cat |
15 (17%) |
| Unknown |
3 |
| meta_liver |
54 (58%) |
| meta_brain |
23 (25%) |
| meta_lung |
28 (30%) |
| previous_lines |
|
| 1 |
12 (13%) |
| 2 |
19 (20%) |
| 3 |
13 (14%) |
| 4 et + |
49 (53%) |
| chemotherapy |
92 (99%) |
| HER2i |
45 (48%) |
| icdk4_6 |
44 (47%) |
| endocrine_therapy |
67 (72%) |
| immunotherapy |
5 (5.4%) |
| sacituzumab_govitecan |
6 (6.5%) |
| hypoalbuminemie |
26 (41%) |
| Unknown |
29 |
| hyperbilirubinemie |
2 (2.8%) |
| Unknown |
22 |
| asat3n |
12 (15%) |
| Unknown |
12 |
| concession_initiale |
19 (20%) |
##description population caracteristiques patients
tbl_summary(
tdxd, include = c("age", "poids", "Dose prescrite (mg)", "taille", "imc", "imc25", "imc30", "Score ECOG", "oms.cat", "hypoalbuminemie", "hyperbilirubinemie", "asat3n", "concession_initiale"),
)
| Characteristic |
N = 93 |
| age |
59 (48, 66) |
| poids |
61 (52, 71) |
| Dose prescrite (mg) |
320 (280, 360) |
| taille |
162 (158, 166) |
| imc |
23.6 (20.0, 26.2) |
| imc25 |
33 (35%) |
| imc30 |
7 (7.5%) |
| Score ECOG |
|
| 0 |
22 (24%) |
| 0/1 |
1 (1.1%) |
| 1 |
52 (56%) |
| 2 |
14 (15%) |
| 3 |
1 (1.1%) |
| NR |
3 (3.2%) |
| oms.cat |
15 (17%) |
| Unknown |
3 |
| hypoalbuminemie |
26 (41%) |
| Unknown |
29 |
| hyperbilirubinemie |
2 (2.8%) |
| Unknown |
22 |
| asat3n |
12 (15%) |
| Unknown |
12 |
| concession_initiale |
19 (20%) |
##description population caracteristiques patients selon imc >25
tbl_summary(
tdxd, include = c("age", "poids", "Dose prescrite (mg)", "taille","Score ECOG", "oms.cat", "hypoalbuminemie", "hyperbilirubinemie", "asat3n", "concession_initiale"),
by="imc25",
digits=all_categorical()~ c(0,1)
)%>%
add_p()
| Characteristic |
0, N = 60 |
1, N = 33 |
p-value |
| age |
60 (50, 66) |
55 (46, 66) |
0.5 |
| poids |
55 (48, 61) |
74 (68, 80) |
<0.001 |
| Dose prescrite (mg) |
290 (260, 320) |
380 (360, 440) |
<0.001 |
| taille |
162 (157, 166) |
161 (160, 166) |
0.9 |
| Score ECOG |
|
|
0.10 |
| 0 |
16 (26.7%) |
6 (18.2%) |
|
| 0/1 |
1 (1.7%) |
0 (0.0%) |
|
| 1 |
30 (50.0%) |
22 (66.7%) |
|
| 2 |
12 (20.0%) |
2 (6.1%) |
|
| 3 |
0 (0.0%) |
1 (3.0%) |
|
| NR |
1 (1.7%) |
2 (6.1%) |
|
| oms.cat |
12 (20.3%) |
3 (9.7%) |
0.2 |
| Unknown |
1 |
2 |
|
| hypoalbuminemie |
15 (34.9%) |
11 (52.4%) |
0.2 |
| Unknown |
17 |
12 |
|
| hyperbilirubinemie |
1 (2.1%) |
1 (4.3%) |
0.5 |
| Unknown |
12 |
10 |
|
| asat3n |
6 (12.0%) |
6 (19.4%) |
0.5 |
| Unknown |
10 |
2 |
|
| concession_initiale |
13 (21.7%) |
6 (18.2%) |
0.7 |
##description population caracteristiques patients selon poids>71
tbl_summary(
tdxd, include = c("age", "poids", "Dose prescrite (mg)", "taille","Score ECOG", "oms.cat", "hypoalbuminemie", "hyperbilirubinemie", "asat3n", "concession_initiale"),
by="poidslourds",
digits=all_categorical()~ c(0,1)
)%>%
add_p()
| Characteristic |
0, N = 71 |
1, N = 22 |
p-value |
| age |
59 (48, 66) |
54 (46, 64) |
0.5 |
| poids |
57 (50, 64) |
78 (75, 81) |
<0.001 |
| Dose prescrite (mg) |
300 (260, 340) |
420 (385, 440) |
<0.001 |
| taille |
161 (157, 165) |
164 (161, 166) |
0.039 |
| Score ECOG |
|
|
0.4 |
| 0 |
18 (25.4%) |
4 (18.2%) |
|
| 0/1 |
1 (1.4%) |
0 (0.0%) |
|
| 1 |
36 (50.7%) |
16 (72.7%) |
|
| 2 |
13 (18.3%) |
1 (4.5%) |
|
| 3 |
1 (1.4%) |
0 (0.0%) |
|
| NR |
2 (2.8%) |
1 (4.5%) |
|
| oms.cat |
14 (20.3%) |
1 (4.8%) |
0.2 |
| Unknown |
2 |
1 |
|
| hypoalbuminemie |
20 (39.2%) |
6 (46.2%) |
0.6 |
| Unknown |
20 |
9 |
|
| hyperbilirubinemie |
2 (3.5%) |
0 (0.0%) |
>0.9 |
| Unknown |
14 |
8 |
|
| asat3n |
9 (14.8%) |
3 (15.0%) |
>0.9 |
| Unknown |
10 |
2 |
|
| concession_initiale |
15 (21.1%) |
4 (18.2%) |
>0.9 |
##description pathologies
tbl_summary(
tdxd, include = c("her2_status", "rh", "meta_liver", "meta_brain", "meta_lung", "previous_lines", "chemotherapy", "HER2i", "icdk4_6", "endocrine_therapy", "immunotherapy", "sacituzumab_govitecan"),
)
| Characteristic |
N = 93 |
| her2_status |
|
| low |
45 (48%) |
| positive |
48 (52%) |
| rh |
|
| 0 |
18 (19%) |
| 1 |
74 (80%) |
| NR |
1 (1.1%) |
| meta_liver |
54 (58%) |
| meta_brain |
23 (25%) |
| meta_lung |
28 (30%) |
| previous_lines |
|
| 1 |
12 (13%) |
| 2 |
19 (20%) |
| 3 |
13 (14%) |
| 4 et + |
49 (53%) |
| chemotherapy |
92 (99%) |
| HER2i |
45 (48%) |
| icdk4_6 |
44 (47%) |
| endocrine_therapy |
67 (72%) |
| immunotherapy |
5 (5.4%) |
| sacituzumab_govitecan |
6 (6.5%) |
##description pathologies selon imc>25
tbl_summary(
tdxd, include = c("her2_status", "rh", "meta_liver", "meta_brain", "meta_lung", "previous_lines", "chemotherapy", "HER2i", "icdk4_6", "endocrine_therapy", "immunotherapy", "sacituzumab_govitecan"),
by="imc25",
digits=all_categorical()~ c(0,1)
)%>%
add_p()
| Characteristic |
0, N = 60 |
1, N = 33 |
p-value |
| her2_status |
|
|
>0.9 |
| low |
29 (48.3%) |
16 (48.5%) |
|
| positive |
31 (51.7%) |
17 (51.5%) |
|
| rh |
|
|
0.2 |
| 0 |
10 (16.7%) |
8 (24.2%) |
|
| 1 |
50 (83.3%) |
24 (72.7%) |
|
| NR |
0 (0.0%) |
1 (3.0%) |
|
| meta_liver |
34 (56.7%) |
20 (60.6%) |
0.7 |
| meta_brain |
14 (23.3%) |
9 (27.3%) |
0.7 |
| meta_lung |
22 (36.7%) |
6 (18.2%) |
0.063 |
| previous_lines |
|
|
0.4 |
| 1 |
10 (16.7%) |
2 (6.1%) |
|
| 2 |
11 (18.3%) |
8 (24.2%) |
|
| 3 |
7 (11.7%) |
6 (18.2%) |
|
| 4 et + |
32 (53.3%) |
17 (51.5%) |
|
| chemotherapy |
59 (98.3%) |
33 (100.0%) |
>0.9 |
| HER2i |
29 (48.3%) |
16 (48.5%) |
>0.9 |
| icdk4_6 |
28 (46.7%) |
16 (48.5%) |
0.9 |
| endocrine_therapy |
43 (71.7%) |
24 (72.7%) |
>0.9 |
| immunotherapy |
4 (6.7%) |
1 (3.0%) |
0.7 |
| sacituzumab_govitecan |
3 (5.0%) |
3 (9.1%) |
0.7 |
##description pathologies selon poids >71
tbl_summary(
tdxd, include = c("her2_status", "rh", "meta_liver", "meta_brain", "meta_lung", "previous_lines", "chemotherapy", "HER2i", "icdk4_6", "endocrine_therapy", "immunotherapy", "sacituzumab_govitecan"),
by="poidslourds",
digits=all_categorical()~ c(0,1)
)%>%
add_p()
| Characteristic |
0, N = 71 |
1, N = 22 |
p-value |
| her2_status |
|
|
0.8 |
| low |
35 (49.3%) |
10 (45.5%) |
|
| positive |
36 (50.7%) |
12 (54.5%) |
|
| rh |
|
|
0.3 |
| 0 |
14 (19.7%) |
4 (18.2%) |
|
| 1 |
57 (80.3%) |
17 (77.3%) |
|
| NR |
0 (0.0%) |
1 (4.5%) |
|
| meta_liver |
42 (59.2%) |
12 (54.5%) |
0.7 |
| meta_brain |
16 (22.5%) |
7 (31.8%) |
0.4 |
| meta_lung |
24 (33.8%) |
4 (18.2%) |
0.2 |
| previous_lines |
|
|
0.7 |
| 1 |
10 (14.1%) |
2 (9.1%) |
|
| 2 |
13 (18.3%) |
6 (27.3%) |
|
| 3 |
9 (12.7%) |
4 (18.2%) |
|
| 4 et + |
39 (54.9%) |
10 (45.5%) |
|
| chemotherapy |
70 (98.6%) |
22 (100.0%) |
>0.9 |
| HER2i |
33 (46.5%) |
12 (54.5%) |
0.5 |
| icdk4_6 |
34 (47.9%) |
10 (45.5%) |
0.8 |
| endocrine_therapy |
51 (71.8%) |
16 (72.7%) |
>0.9 |
| immunotherapy |
5 (7.0%) |
0 (0.0%) |
0.3 |
| sacituzumab_govitecan |
4 (5.6%) |
2 (9.1%) |
0.6 |
##description toxicités
tbl_summary(
tox, include = c("introduction_gcsf", "neutropenie", "neutropenie_gcsf2","neurotoxicite", "infection","thrombopenie","anemie","diarrhee","nausees","vomissements","rgo_dyspepsie","hepatotoxicite","cholecystite","toxicite_cutanee","toxicite_muqueuse","neurotoxicite","fatigue","perte_poids","arthralgies","toux_dyspnee","pneumopathies interstitelle","cardiaque"),
by="grade_tox",
)
| Characteristic |
cliniquement_significatif, N = 93 |
tout_grade, N = 93 |
| introduction_gcsf |
4 (4.3%) |
14 (15%) |
| neutropenie |
12 (13%) |
15 (16%) |
| neutropenie_gcsf2 |
16 (17%) |
19 (20%) |
| neurotoxicite |
1 (1.1%) |
9 (9.7%) |
| infection |
1 (1.1%) |
1 (1.1%) |
| thrombopenie |
5 (5.4%) |
7 (7.5%) |
| anemie |
1 (1.1%) |
11 (12%) |
| diarrhee |
5 (5.4%) |
46 (49%) |
| nausees |
8 (8.6%) |
62 (67%) |
| vomissements |
2 (2.2%) |
22 (24%) |
| rgo_dyspepsie |
3 (3.2%) |
11 (12%) |
| hepatotoxicite |
3 (3.2%) |
46 (49%) |
| cholecystite |
1 (1.1%) |
1 (1.1%) |
| toxicite_cutanee |
0 (0%) |
2 (2.2%) |
| toxicite_muqueuse |
1 (1.1%) |
1 (1.1%) |
| fatigue |
20 (22%) |
83 (89%) |
| perte_poids |
3 (3.2%) |
26 (28%) |
| arthralgies |
0 (0%) |
17 (18%) |
| toux_dyspnee |
1 (1.1%) |
22 (24%) |
| pneumopathies interstitelle |
7 (7.5%) |
7 (7.5%) |
| cardiaque |
1 (1.1%) |
1 (1.1%) |
##description des consequences cliniques des toxicités
tbl_summary(
tdxd, include = c("total_diminution_dose", "total_report_cure", "total_arret_tt", "total_prophylaxie_gscf", "total_transfusion_cp", "total_transfusion_gr", "total_hospitalisation"),
)
| Characteristic |
N = 93 |
| total_diminution_dose |
32 (34%) |
| total_report_cure |
18 (19%) |
| total_arret_tt |
12 (13%) |
| total_prophylaxie_gscf |
12 (13%) |
| total_transfusion_cp |
1 (1.1%) |
| total_transfusion_gr |
1 (1.1%) |
| total_hospitalisation |
3 (3.2%) |
##durée de traitement
km_tt<-survfit(Surv(tdxd$durtt, tdxd$evtt)~1)
km_tt
## Call: survfit(formula = Surv(tdxd$durtt, tdxd$evtt) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 93 48 9.1 7.13 11.2
##motifs d'arrêt
tbl_summary(
tdxd, include = c("motif_arret"),
)
| Characteristic |
N = 93 |
| motif_arret |
|
| 0 |
44 (47%) |
| DECES |
3 (3.2%) |
| PERDU DE VUE |
1 (1.1%) |
| Perdu de vue (rappatriement en géorgie) |
1 (1.1%) |
| PROGRESSION |
32 (34%) |
| TOXICITE |
12 (13%) |
##Survie sans progression
km_pfs<-survfit(Surv(tdxd$pfs, tdxd$evtpfs)~1)
km_pfs
## Call: survfit(formula = Surv(tdxd$pfs, tdxd$evtpfs) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 93 37 10.3 8.63 NA
ggsurvplot(km_pfs, data = tdxd,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
##PFS selon statut HER2
km_pfs_selonher2<-survfit(Surv(pfs, evtpfs)~her2_status, data=tdxd)
ggsurvplot(km_pfs_selonher2, data = tdxd,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
##PFS chez HER2 low
km_pfsher2low<-survfit(Surv(her2low$pfs, her2low$evtpfs)~1)
km_pfsher2low
## Call: survfit(formula = Surv(her2low$pfs, her2low$evtpfs) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 45 18 6.03 5.17 NA
##PFS chez HER2 positives
km_pfsher2plus<-survfit(Surv(her2pos$pfs, her2pos$evtpfs)~1)
km_pfsher2plus
## Call: survfit(formula = Surv(her2pos$pfs, her2pos$evtpfs) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 48 19 11.2 10.3 NA
##Survie globale
km_os<-survfit(Surv(tdxd$os, tdxd$evtos)~1)
km_os
## Call: survfit(formula = Surv(tdxd$os, tdxd$evtos) ~ 1)
##
## 1 observation effacée parce que manquante
## n events median 0.95LCL 0.95UCL
## [1,] 92 16 24.7 22.3 NA
ggsurvplot(km_os, data = tdxd,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
##Survie sans toxicité significative
km_toxfs<-survfit(Surv(tdxd$toxfs, tdxd$evttox)~1)
km_toxfs
## Call: survfit(formula = Surv(tdxd$toxfs, tdxd$evttox) ~ 1)
##
## n events median 0.95LCL 0.95UCL
## [1,] 93 51 3.85 2.55 15.5
ggsurvplot(km_toxfs, data = tdxd,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
## Influence de IMC>25 sur survie sans toxicité significative
km_toxfsimc<-survfit(Surv(toxfs, evttox)~imc25, data=tdxd)
ggsurvplot(km_toxfsimc, data = tdxd,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
## Influence de poids >3eme quartile sur survie sans toxicité significative
km_toxfspoids<-survfit(Surv(toxfs, evttox)~poidslourds, data=tdxd)
ggsurvplot(km_toxfspoids, data = tdxd,
risk.table=TRUE,
surv.scale="percent",
break.time.by=3,
surv.median.line = "hv"
)
#modele de cox effet de imc25
modsurv<-coxph(Surv(toxfs, evttox)~imc25, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| imc25 |
1.05 |
0.59, 1.88 |
0.9 |
#modele de cox effet de poids>71
modsurv<-coxph(Surv(toxfs, evttox)~poidslourds, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| poidslourds |
1.88 |
1.02, 3.46 |
0.042 |
#modele de cox effet de age>66
modsurv<-coxph(Surv(toxfs, evttox)~old, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| old |
1.81 |
0.98, 3.32 |
0.057 |
#modele de cox effet de concession initiale
modsurv<-coxph(Surv(toxfs, evttox)~concession_initiale, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| concession_initiale |
0.94 |
0.47, 1.88 |
0.9 |
#modele de cox effet de hypoalbuminemie
modsurv<-coxph(Surv(toxfs, evttox)~hypoalbuminemie, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| hypoalbuminemie |
0.55 |
0.25, 1.17 |
0.12 |
#modele de cox effet de hypoalbuminemie + concession posologie
modsurv<-coxph(Surv(toxfs, evttox)~hypoalbuminemie+concession_initiale, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| hypoalbuminemie |
0.44 |
0.19, 1.01 |
0.053 |
| concession_initiale |
2.07 |
0.83, 5.12 |
0.12 |
#modele de cox effet asat3n
modsurv<-coxph(Surv(toxfs, evttox)~asat3n, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| asat3n |
0.53 |
0.19, 1.48 |
0.2 |
#modele de cox effet asat3n + concession_posologie
modsurv<-coxph(Surv(toxfs, evttox)~asat3n+concession_initiale, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| asat3n |
0.44 |
0.14, 1.34 |
0.15 |
| concession_initiale |
1.46 |
0.63, 3.38 |
0.4 |
#modele de cox effet de oms.cat
modsurv<-coxph(Surv(toxfs, evttox)~oms.cat, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| oms.cat |
0.88 |
0.39, 1.96 |
0.8 |
#modele de cox effet de oms.cat + concession posologie
modsurv<-coxph(Surv(toxfs, evttox)~oms.cat+concession_initiale, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| oms.cat |
0.94 |
0.38, 2.31 |
0.9 |
| concession_initiale |
0.88 |
0.39, 1.97 |
0.7 |
#modele de cox effet de meta liver
modsurv<-coxph(Surv(toxfs, evttox)~meta_liver, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| meta_liver |
1.15 |
0.66, 2.02 |
0.6 |
#modele de cox effet de meta liver + concession posologie
modsurv<-coxph(Surv(toxfs, evttox)~meta_liver+concession_initiale, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| meta_liver |
1.15 |
0.66, 2.03 |
0.6 |
| concession_initiale |
0.93 |
0.47, 1.86 |
0.8 |
### covariables sélectionnées en univariées : poids>71 + age >66 + asat3n + hpoalbuminémie (en prenant en compte concession initiale)
###Modèles multivariés global
#modele de cox effet de poids>71 + age >66+albuminemie + asat +concession poso
modsurv<-coxph(Surv(toxfs, evttox)~poidslourds+old+hypoalbuminemie+asat3n+concession_initiale, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| poidslourds |
2.66 |
1.20, 5.89 |
0.016 |
| old |
2.50 |
1.21, 5.17 |
0.014 |
| hypoalbuminemie |
0.60 |
0.26, 1.36 |
0.2 |
| asat3n |
0.36 |
0.08, 1.62 |
0.2 |
| concession_initiale |
1.58 |
0.62, 4.00 |
0.3 |
#modele de cox effet de poids>71 + age >66+ asat +concession poso
modsurv<-coxph(Surv(toxfs, evttox)~poidslourds+old+asat3n+concession_initiale, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| poidslourds |
2.99 |
1.52, 5.88 |
0.001 |
| old |
2.42 |
1.22, 4.78 |
0.011 |
| asat3n |
0.41 |
0.14, 1.21 |
0.11 |
| concession_initiale |
1.09 |
0.47, 2.52 |
0.8 |
#modele de cox effet de poids>71 + age >66+ concession poso
modsurv<-coxph(Surv(toxfs, evttox)~poidslourds+old+concession_initiale, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| poidslourds |
2.10 |
1.13, 3.91 |
0.019 |
| old |
2.07 |
1.11, 3.89 |
0.023 |
| concession_initiale |
0.83 |
0.41, 1.67 |
0.6 |
#modele de cox effet de poids>71 + age >66
modsurv<-coxph(Surv(toxfs, evttox)~poidslourds+old, data=tdxd)
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| poidslourds |
2.09 |
1.12, 3.89 |
0.020 |
| old |
2.01 |
1.08, 3.73 |
0.027 |
modsurv%>%tbl_regression(exponentiate = TRUE)
| Characteristic |
HR |
95% CI |
p-value |
| poidslourds |
2.09 |
1.12, 3.89 |
0.020 |
| old |
2.01 |
1.08, 3.73 |
0.027 |
##incidence des toxicités cliniquement pertinentes selon critère de poids
toxselonpoids<-table(tdxd$evttox, tdxd$poidslourds,deparse.level=2, useNA="always")
cprop(toxselonpoids)
## tdxd$poidslourds
## tdxd$evttox 0 1 Ensemble
## 0 49.3 31.8 45.2
## 1 50.7 68.2 54.8
## Total 100.0 100.0 100.0
##regression logistique influence poids sur incidence toxicité
mod<-glm( evttox ~ poidslourds, data=tdxd, family="binomial")
summary(mod)
##
## Call:
## glm(formula = evttox ~ poidslourds, family = "binomial", data = tdxd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5134 -1.1894 0.8752 1.1655 1.1655
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.02817 0.23738 0.119 0.906
## poidslourds 0.73397 0.51563 1.423 0.155
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 128.05 on 92 degrees of freedom
## Residual deviance: 125.93 on 91 degrees of freedom
## AIC: 129.93
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(mod))
## (Intercept) poidslourds
## 1.028571 2.083333
exp(confint(mod, level=0.95))
## Attente de la réalisation du profilage...
## 2.5 % 97.5 %
## (Intercept) 0.6448842 1.642221
## poidslourds 0.7793626 6.027248
mod%>%tbl_regression(intercept = TRUE, exponentiate = TRUE)
| Characteristic |
OR |
95% CI |
p-value |
| (Intercept) |
1.03 |
0.64, 1.64 |
>0.9 |
| poidslourds |
2.08 |
0.78, 6.03 |
0.2 |
##regressions logisitiques univariées
tdxd |>
tbl_uvregression(
y = evttox,
include = c(poidslourds, oms.cat, hypoalbuminemie, asat3n, age, old, meta_liver),
method = glm,
method.args = list(family = binomial),
exponentiate = TRUE
) |>
bold_labels()
| Characteristic |
N |
OR |
95% CI |
p-value |
| age |
93 |
1.03 |
0.99, 1.06 |
0.14 |
| oms.cat |
90 |
0.69 |
0.22, 2.10 |
0.5 |
| meta_liver |
93 |
1.07 |
0.47, 2.46 |
0.9 |
| hypoalbuminemie |
64 |
0.24 |
0.08, 0.69 |
0.009 |
| asat3n |
81 |
0.43 |
0.11, 1.51 |
0.2 |
| old |
93 |
2.50 |
0.91, 7.68 |
0.088 |
| poidslourds |
93 |
2.08 |
0.78, 6.03 |
0.2 |
##analyse multivariée avec hypoalbuminemie (mais 29 données manquantes)
mod<-glm( evttox ~ poidslourds+hypoalbuminemie+old, data=tdxd, family="binomial")
summary(mod)
##
## Call:
## glm(formula = evttox ~ poidslourds + hypoalbuminemie + old, family = "binomial",
## data = tdxd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1582 -1.2048 0.3158 1.0736 1.9920
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2492 0.3872 0.644 0.5199
## poidslourds 1.9001 0.8967 2.119 0.0341 *
## hypoalbuminemie -2.0852 0.7051 -2.957 0.0031 **
## old 1.9773 0.8149 2.426 0.0152 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 88.160 on 63 degrees of freedom
## Residual deviance: 68.991 on 60 degrees of freedom
## (29 observations effacées parce que manquantes)
## AIC: 76.991
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(mod))
## (Intercept) poidslourds hypoalbuminemie old
## 1.2830174 6.6866420 0.1242831 7.2233046
exp(confint(mod, level=0.95))
## Attente de la réalisation du profilage...
## 2.5 % 97.5 %
## (Intercept) 0.6027821 2.7994726
## poidslourds 1.3314214 48.1004653
## hypoalbuminemie 0.0257717 0.4422923
## old 1.6707463 43.5614364
mod%>%tbl_regression(intercept = TRUE, exponentiate = TRUE)
| Characteristic |
OR |
95% CI |
p-value |
| (Intercept) |
1.28 |
0.60, 2.80 |
0.5 |
| poidslourds |
6.69 |
1.33, 48.1 |
0.034 |
| hypoalbuminemie |
0.12 |
0.03, 0.44 |
0.003 |
| old |
7.22 |
1.67, 43.6 |
0.015 |
##analyse multivariée sans hypoalbuminemie
mod<-glm( evttox ~ poidslourds+old, data=tdxd, family="binomial")
summary(mod)
##
## Call:
## glm(formula = evttox ~ poidslourds + old, family = "binomial",
## data = tdxd)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.524 -1.094 0.867 1.263 1.263
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1987 0.2692 -0.738 0.4603
## poidslourds 0.8114 0.5252 1.545 0.1224
## old 0.9835 0.5452 1.804 0.0712 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 128.05 on 92 degrees of freedom
## Residual deviance: 122.44 on 90 degrees of freedom
## AIC: 128.44
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(mod))
## (Intercept) poidslourds old
## 0.819772 2.251139 2.673812
exp(confint(mod, level=0.95))
## Attente de la réalisation du profilage...
## 2.5 % 97.5 %
## (Intercept) 0.4797972 1.386518
## poidslourds 0.8273583 6.636739
## old 0.9552708 8.336419
mod%>%tbl_regression(intercept = TRUE, exponentiate = TRUE)
| Characteristic |
OR |
95% CI |
p-value |
| (Intercept) |
0.82 |
0.48, 1.39 |
0.5 |
| poidslourds |
2.25 |
0.83, 6.64 |
0.12 |
| old |
2.67 |
0.96, 8.34 |
0.071 |