Resumé des données de comparaison

recapitulatif des données de comparaison

summary(Ig)
##     Patient    Sexe    age_inclusion        IMC       
##  BOULA  :  2   F:154   Min.   :15.38   Min.   :15.00  
##  BOUMA  :  2   M:104   1st Qu.:42.54   1st Qu.:22.00  
##  CHAMA  :  2           Median :53.83   Median :25.00  
##  MASCH  :  2           Mean   :52.94   Mean   :26.57  
##  VANFR  :  2           3rd Qu.:64.91   3rd Qu.:29.25  
##  ABDAB  :  1           Max.   :85.61   Max.   :71.00  
##  (Other):247                           NA's   :90     
##  Nb_Exa_an.sans_biotherapie Corticoïde.systémique..dose.     CSI_µg    
##  Min.   :  0.000            Min.   :  0.000              Min.   :   0  
##  1st Qu.:  1.000            1st Qu.:  0.000              1st Qu.: 800  
##  Median :  3.000            Median :  0.000              Median :1600  
##  Mean   :  4.545            Mean   :  6.052              Mean   :1605  
##  3rd Qu.:  6.000            3rd Qu.:  5.500              3rd Qu.:2000  
##  Max.   :104.000            Max.   :100.000              Max.   :4000  
##  NA's   :49                 NA's   :27                   NA's   :11    
##   Montélukast     Biotherapie     Biothérapie_type Rep_biotherapie_GETE
##  Min.   :0.0000   Non:168                 :168     Min.   :0.0000      
##  1st Qu.:0.0000   Oui: 90     Benralizumab: 10     1st Qu.:0.0000      
##  Median :0.0000               Dupilumab   :  4     Median :0.0000      
##  Mean   :0.3711               Mepolizumab : 52     Mean   :0.9918      
##  3rd Qu.:1.0000               Omalixumab  : 24     3rd Qu.:2.0000      
##  Max.   :1.0000                                    Max.   :5.0000      
##  NA's   :2                                         NA's   :14          
##     Tabac    Atopie    VEMS_pre_B2_L   VEMS_PreB2_Pct     Tiffenau    
##        :47   Non:160   Min.   :0.490   Min.   : 20.0   Min.   :30.36  
##  Actif :25   Oui: 98   1st Qu.:1.450   1st Qu.: 53.5   1st Qu.:58.00  
##  Non   :91             Median :2.000   Median : 72.0   Median :67.00  
##  Passif:10             Mean   :2.125   Mean   : 73.1   Mean   :66.19  
##  Sevré :85             3rd Qu.:2.792   3rd Qu.: 92.0   3rd Qu.:75.75  
##                        Max.   :5.000   Max.   :128.0   Max.   :95.00  
##                        NA's   :36      NA's   :31      NA's   :35     
##       FeNo        FeNo_sup_20_ppb PNN_sup_5 PNE_sup_0.15G_L PNE_sup_0.3.G_L
##  Min.   :  2.00      :120            : 22      : 23            : 23        
##  1st Qu.: 14.00   Non: 56         Non:106   Non: 98         Non:133        
##  Median : 24.50   Oui: 82         Oui:130   Oui:137         Oui:102        
##  Mean   : 35.64                                                            
##  3rd Qu.: 47.25                                                            
##  Max.   :177.00                                                            
##  NA's   :120                                                               
##     PNE_G_L       CRP_sup_5.mg_l   IgE_Total       IgE_Total_sup_30_kUA_l
##  Min.   :0.0000      :103        Min.   :    2.0      : 77               
##  1st Qu.:0.1000   Non:106        1st Qu.:   58.0   Non: 30               
##  Median :0.2000   Oui: 49        Median :  164.0   Oui:151               
##  Mean   :0.3468                  Mean   :  410.4                         
##  3rd Qu.:0.4000                  3rd Qu.:  400.0                         
##  Max.   :3.9000                  Max.   :12255.0                         
##  NA's   :23                      NA's   :77                              
##  Profil_T2   IgG_statut      IgG2_statut     IgG3_statut     IgG4_statut   
##     :128   Min.   :0.0000   Min.   :0.000   Min.   :0.000   Min.   :0.000  
##  Neg: 85   1st Qu.:1.0000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000  
##  Pos: 45   Median :1.0000   Median :1.000   Median :1.000   Median :1.000  
##            Mean   :0.9845   Mean   :1.101   Mean   :1.027   Mean   :1.081  
##            3rd Qu.:1.0000   3rd Qu.:1.000   3rd Qu.:1.000   3rd Qu.:1.000  
##            Max.   :2.0000   Max.   :2.000   Max.   :2.000   Max.   :2.000  
##                                                                            
##   IgG_statut_A IgG_deficit IgG2_statut_A IgG2_deficit IgG2_Exces IgG3_statut_A
##  Deficit:  8   Non:250     Deficit:  8   Non:250      Non:224    Deficit: 16  
##  Excès  :  4   Oui:  8     Excès  : 34   Oui:  8      Oui: 34    Excès  : 23  
##  Normal :246               Normal :216                           Normal :219  
##                                                                               
##                                                                               
##                                                                               
##                                                                               
##  IgG3_Exces IgG4_statut_A IgG4_Exces
##  Non:235    Deficit:  8   Non:229   
##  Oui: 23    Excès  : 29   Oui: 29   
##             Normal :221             
##                                     
##                                     
##                                     
##

Evaluation des conditions de validité:Normalité pour les données IgG deficit Vs Normaux + Excès

la normalité va nous permettre de choisir le type de test pour comparer les données Un test de shapiro pour valider la normalité si la p-value est<0.05 la normalité n’est pas accepté on fait donc un test de Wilcoxon Si la normalité est respecté on fait le test de Student

Age à l’inclusion

shapiro.test(Ig$age_inclusion[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$age_inclusion[Ig$IgG_deficit == "Oui"]
## W = 0.92288, p-value = 0.4536

la normalité est acceptée: T-test de Student

shapiro.test(Ig$age_inclusion[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$age_inclusion[Ig$IgG_deficit == "Non"]
## W = 0.98192, p-value = 0.002862

la normalité n’est pas acceptée pour les IgG normaux+excès : test de Wilcoxon

IMC

shapiro.test(Ig$IMC[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$IMC[Ig$IgG_deficit == "Oui"]
## W = 0.87029, p-value = 0.2988

la normalité est acceptée: T-test de Student

shapiro.test(Ig$IMC[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$IMC[Ig$IgG_deficit == "Non"]
## W = 0.85802, p-value = 2.648e-11

la normalité n’est pas acceptés :test de Wilcoxon

Nombre d’exacerbation sans biotherapie

shapiro.test(Ig$Nb_Exa_an.sans_biotherapie[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Nb_Exa_an.sans_biotherapie[Ig$IgG_deficit == "Oui"]
## W = 0.77091, p-value = 0.04595

la normalité n’est pas acceptée: Wilcoxon

shapiro.test(Ig$Nb_Exa_an.sans_biotherapie[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Nb_Exa_an.sans_biotherapie[Ig$IgG_deficit == "Non"]
## W = 0.41589, p-value < 2.2e-16

la normalité n’est pas acceptés :test de Wilcoxon

Dose de corticoides systemiques

shapiro.test(Ig$Corticoïde.systémique..dose.[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Corticoïde.systémique..dose.[Ig$IgG_deficit == "Oui"]
## W = 0.62607, p-value = 0.0005479

la normalité n’est pas acceptée: Wilcoxon

shapiro.test(Ig$Corticoïde.systémique..dose.[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Corticoïde.systémique..dose.[Ig$IgG_deficit == "Non"]
## W = 0.54702, p-value < 2.2e-16

la normalité n’est pas acceptés :test de Wilcoxon

CSI

shapiro.test(Ig$CSI_µg[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$CSI_µg[Ig$IgG_deficit == "Oui"]
## W = 0.86768, p-value = 0.143

la normalité est acceptée : T test de student

shapiro.test(Ig$CSI_µg[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$CSI_µg[Ig$IgG_deficit == "Non"]
## W = 0.87507, p-value = 4.268e-13

la normalité n’est pas acceptés :test de Wilcoxon

Montélukast

shapiro.test(Ig$Montélukast[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Montélukast[Ig$IgG_deficit == "Oui"]
## W = 0.6412, p-value = 0.0004791

la normalité n’est pas acceptée : Wilcoxon

shapiro.test(Ig$Montélukast[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Montélukast[Ig$IgG_deficit == "Non"]
## W = 0.61178, p-value < 2.2e-16

la normalité n’est pas acceptés :test de Wilcoxon

Réponse Biotherapie

shapiro.test(Ig$Rep_biotherapie_GETE[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Rep_biotherapie_GETE[Ig$IgG_deficit == "Oui"]
## W = 0.86815, p-value = 0.1788

la normalité est acceptée : t-test de Student

shapiro.test(Ig$Rep_biotherapie_GETE[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Rep_biotherapie_GETE[Ig$IgG_deficit == "Non"]
## W = 0.63823, p-value < 2.2e-16

la normalité n’est pas acceptés :test de Wilcoxon

VEMS pre B2 (litre)

shapiro.test(Ig$VEMS_pre_B2_L[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$VEMS_pre_B2_L[Ig$IgG_deficit == "Oui"]
## W = 0.9242, p-value = 0.5574

la normalité est acceptée : t-test de Student

shapiro.test(Ig$VEMS_pre_B2_L[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$VEMS_pre_B2_L[Ig$IgG_deficit == "Non"]
## W = 0.97611, p-value = 0.0009656

la normalité n’est pas acceptés :test de Wilcoxon

VEMS pre B2 (%)

shapiro.test(Ig$VEMS_PreB2_Pct[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$VEMS_PreB2_Pct[Ig$IgG_deficit == "Oui"]
## W = 0.99294, p-value = 0.9889

la normalité est acceptée : t-test de Student

shapiro.test(Ig$VEMS_PreB2_Pct[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$VEMS_PreB2_Pct[Ig$IgG_deficit == "Non"]
## W = 0.98073, p-value = 0.003994

la normalité n’est pas acceptés :test de Wilcoxon

Tiffe

shapiro.test(Ig$Tiffenau[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Tiffenau[Ig$IgG_deficit == "Oui"]
## W = 0.96903, p-value = 0.869

la normalité est acceptée : t-test de Student

shapiro.test(Ig$Tiffenau[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Tiffenau[Ig$IgG_deficit == "Non"]
## W = 0.98818, p-value = 0.06882

la normalité est acceptée : t-test de Student

FeNO

nous ne nouvons pas calculer la difference entre ceux qui sont en deficit d’IgG et les autres par manque de données. Seul 2 patients avec deficit d’IGG ont une donnée de FeNO

shapiro.test(Ig$FeNo[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$FeNo[Ig$IgG_deficit == "Non"]
## W = 0.79511, p-value = 1.656e-12

la normalité n’est pas acceptée :wilcoxon

PNE G/L

shapiro.test(Ig$PNE_G_L[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$PNE_G_L[Ig$IgG_deficit == "Oui"]
## W = 0.59402, p-value = 0.0003882

la normalité n’est pas acceptée : t-test de Wilcoxon

shapiro.test(Ig$PNE_G_L[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$PNE_G_L[Ig$IgG_deficit == "Non"]
## W = 0.68732, p-value < 2.2e-16

#la normalité n’est pas acceptée ;wilcoxon

IgGE total

shapiro.test(Ig$IgE_Total[Ig$IgG_deficit =="Oui"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$IgE_Total[Ig$IgG_deficit == "Oui"]
## W = 0.7253, p-value = 0.0173

la normalité n’est pas acceptée : t-test de Wilcoxon

shapiro.test(Ig$IgE_Total[Ig$IgG_deficit =="Non"])
## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$IgE_Total[Ig$IgG_deficit == "Non"]
## W = 0.32334, p-value < 2.2e-16

#la normalité n’est pas acceptée ;wilcoxon

Comparaison des données IgG deficit Vs Normaux + Excès

Age

t.test(Ig$age_inclusion~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Two Sample t-test
## 
## data:  Ig$age_inclusion by Ig$IgG_deficit
## t = -2.5642, df = 256, p-value = 0.01091
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -25.227814  -3.310486
## sample estimates:
## mean in group Non mean in group Oui 
##          52.49460          66.76375

L’hypothèse nulle d’égalité des moyennes est rejetée car la p-value est < 0.05. Le test met en évidence une différence significative, dans le sens ou la moyenne d’age des patients ayant l’IgG en deficit est plus élevé que les autres patients( excès + normaux)

wilcox.test(Ig$age_inclusion~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$age_inclusion by Ig$IgG_deficit
## W = 438, p-value = 0.006881
## alternative hypothesis: true location shift is not equal to 0

même conclusion

IMC

t.test(Ig$IMC~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Two Sample t-test
## 
## data:  Ig$IMC by Ig$IgG_deficit
## t = -0.051878, df = 166, p-value = 0.9587
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -7.168456  6.801383
## sample estimates:
## mean in group Non mean in group Oui 
##          26.56646          26.75000

L’hypothèse nulle d’égalité des moyennes n’est pas rejetée car la p-value est > 0.05. Ainsi, rien ne permet d’affirmer que les moyennes des IMC des patients ayant l’IgG en excès et les autres patients sont différentes.

wilcox.test(Ig$IMC~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$IMC by Ig$IgG_deficit
## W = 330, p-value = 0.9875
## alternative hypothesis: true location shift is not equal to 0

même conclusion

Nombre d’exacerbation sans biotherapie

wilcox.test(Ig$Nb_Exa_an.sans_biotherapie~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$Nb_Exa_an.sans_biotherapie by Ig$IgG_deficit
## W = 527.5, p-value = 0.8979
## alternative hypothesis: true location shift is not equal to 0

Le test ne met pas en évidence une différence significative

Dose de corticoides systemiques

wilcox.test(Ig$Corticoïde.systémique..dose.~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$Corticoïde.systémique..dose. by Ig$IgG_deficit
## W = 463, p-value = 0.02416
## alternative hypothesis: true location shift is not equal to 0

Le test ne met pas en évidence une différence significative

CSI

wilcox.test(Ig$CSI_µg~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$CSI_µg by Ig$IgG_deficit
## W = 799.5, p-value = 0.4282
## alternative hypothesis: true location shift is not equal to 0

Le test ne met pas en évidence une différence significative

Montélukast

wilcox.test(Ig$Montélukast~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$Montélukast by Ig$IgG_deficit
## W = 988, p-value = 0.9838
## alternative hypothesis: true location shift is not equal to 0

Le test ne met pas en évidence une différence significative

Réponse Biotherapie

wilcox.test(Ig$Rep_biotherapie_GETE~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$Rep_biotherapie_GETE by Ig$IgG_deficit
## W = 586, p-value = 0.1085
## alternative hypothesis: true location shift is not equal to 0

Le test ne met pas en évidence une différence significative

VEMS pre B2 (litre)

wilcox.test(Ig$VEMS_pre_B2_L~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$VEMS_pre_B2_L by Ig$IgG_deficit
## W = 727.5, p-value = 0.1938
## alternative hypothesis: true location shift is not equal to 0

Le test ne met pas en évidence une différence significative

VEMS pre B2 (%)

wilcox.test(Ig$VEMS_PreB2_Pct~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$VEMS_PreB2_Pct by Ig$IgG_deficit
## W = 710, p-value = 0.2874
## alternative hypothesis: true location shift is not equal to 0

Le test ne met pas en évidence une différence significative

Tiffe

t.test(Ig$Tiffenau~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Two Sample t-test
## 
## data:  Ig$Tiffenau by Ig$IgG_deficit
## t = 0.0095199, df = 221, p-value = 0.9924
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -11.69751  11.81107
## sample estimates:
## mean in group Non mean in group Oui 
##          66.19078          66.13400

Le test ne met pas en évidence une différence significative

FeNO

malgre les données insuffisantes, le test de wilcoxon a été fait ( attention à ne pas prendre en consideration)

wilcox.test(Ig$FeNo~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$FeNo by Ig$IgG_deficit
## W = 172, p-value = 0.527
## alternative hypothesis: true location shift is not equal to 0

Le test ne met pas en évidence une différence significative

PNE_Gl

wilcox.test(Ig$PNE_G_L~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$PNE_G_L by Ig$IgG_deficit
## W = 1046.5, p-value = 0.02701
## alternative hypothesis: true location shift is not equal to 0

Le test met en évidence une différence significative

IgGE total

wilcox.test(Ig$IgE_Total~Ig$IgG_deficit, var.equal=TRUE)
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$IgE_Total by Ig$IgG_deficit
## W = 659, p-value = 0.05858
## alternative hypothesis: true location shift is not equal to 0

la p-value est dans la limite de la significativité

Test de comparaison des données qualitatives: IgG deficit Vs normaux+exces

comp_qual <- read.csv("C:/Users/mallah.s/Desktop/StatsTheses/Mauro anthony/comp_qual.csv", sep=";", stringsAsFactors=TRUE)

Pour le test de liaison entre deux variables qualitative: verification de la normalité -> selon la validation de la normalité :test de Chi2 ou test de fisher

le test du χ2 d’indépendance sert à étudier la liaison entre deux caractères qualitatifs XetY, lorsque les conditions ne sont pas remplies, il existe des corrections,dans notre cas je vais utiliser le tests exacts de Fisher

Sexe

xtabs(~Sexe+IgG_deficit, data=comp_qual)
##     IgG_deficit
## Sexe Non Oui
##    F 150   4
##    M 100   4
chisq.test(comp_qual$Sexe,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$Sexe, comp_qual$IgG_deficit): Chi-squared
## approximation may be incorrect
##               comp_qual$IgG_deficit
## comp_qual$Sexe      Non      Oui
##              F 149.2248 4.775194
##              M 100.7752 3.224806
chisq.test(comp_qual$Sexe,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$Sexe, comp_qual$IgG_deficit, correct = FALSE):
## Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Sexe and comp_qual$IgG_deficit
## X-squared = 0.32218, df = 1, p-value = 0.5703
fisher.test(comp_qual$Sexe,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Sexe and comp_qual$IgG_deficit
## p-value = 0.7179
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.2723202 8.2370330
## sample estimates:
## odds ratio 
##   1.497543

les variables Sexe et deficit d’IgG sont independantes

Montélukast

xtabs(~Montélukast+IgG_deficit, data=comp_qual)
##            IgG_deficit
## Montélukast Non Oui
##         Non 156   5
##         Oui  92   3
chisq.test(comp_qual$Montélukast,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$Montélukast, comp_qual$IgG_deficit): Chi-squared
## approximation may be incorrect
##                      comp_qual$IgG_deficit
## comp_qual$Montélukast       Non     Oui
##                   Non 155.96875 5.03125
##                   Oui  92.03125 2.96875
chisq.test(comp_qual$Montélukast,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$Montélukast, comp_qual$IgG_deficit, correct =
## FALSE): Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Montélukast and comp_qual$IgG_deficit
## X-squared = 0.00053992, df = 1, p-value = 0.9815
fisher.test(comp_qual$Montélukast,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Montélukast and comp_qual$IgG_deficit
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.1544207 5.3672921
## sample estimates:
## odds ratio 
##    1.01733

Pour les IgG en deficit, il y’a seulement 3 qui sont traités par Montélukast, donc pas assez de patients, ce qui explique le le resultats de la p-value du test exact de fisher

Biotherapie

xtabs(~Biotherapie+IgG_deficit, data=comp_qual)
##            IgG_deficit
## Biotherapie Non Oui
##         Non 165   3
##         Oui  85   5
chisq.test(comp_qual$Biotherapie,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$Biotherapie, comp_qual$IgG_deficit): Chi-squared
## approximation may be incorrect
##                      comp_qual$IgG_deficit
## comp_qual$Biotherapie      Non      Oui
##                   Non 162.7907 5.209302
##                   Oui  87.2093 2.790698
chisq.test(comp_qual$Biotherapie,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$Biotherapie, comp_qual$IgG_deficit, correct =
## FALSE): Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Biotherapie and comp_qual$IgG_deficit
## X-squared = 2.772, df = 1, p-value = 0.09593
fisher.test(comp_qual$Biotherapie,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Biotherapie and comp_qual$IgG_deficit
## p-value = 0.1317
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   0.609821 21.223824
## sample estimates:
## odds ratio 
##   3.219438

type de biotherapie

xtabs(~Biothérapie_type+IgG_deficit, data=comp_qual)
##                 IgG_deficit
## Biothérapie_type Non Oui
##     Benralizumab  10   0
##     Dupilumab      4   0
##     Mepolizumab   49   3
##     Omalixumab    22   2
chisq.test(comp_qual$Biothérapie_type,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$Biothérapie_type, comp_qual$IgG_deficit): Chi-
## squared approximation may be incorrect
##                           comp_qual$IgG_deficit
## comp_qual$Biothérapie_type       Non       Oui
##               Benralizumab  9.444444 0.5555556
##               Dupilumab     3.777778 0.2222222
##               Mepolizumab  49.111111 2.8888889
##               Omalixumab   22.666667 1.3333333
chisq.test(comp_qual$Biothérapie_type,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$Biothérapie_type, comp_qual$IgG_deficit, : Chi-
## squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Biothérapie_type and comp_qual$IgG_deficit
## X-squared = 1.181, df = 3, p-value = 0.7576
fisher.test(comp_qual$Biothérapie_type,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Biothérapie_type and comp_qual$IgG_deficit
## p-value = 0.8522
## alternative hypothesis: two.sided

Tabac

xtabs(~Tabac+IgG_deficit, data=comp_qual)
##         IgG_deficit
## Tabac    Non Oui
##   Actif   24   1
##   Non     88   3
##   Passif  10   0
##   Sevré   81   4
chisq.test(comp_qual$Tabac,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$Tabac, comp_qual$IgG_deficit): Chi-squared
## approximation may be incorrect
##                comp_qual$IgG_deficit
## comp_qual$Tabac       Non       Oui
##          Actif  24.052133 0.9478673
##          Non    87.549763 3.4502370
##          Passif  9.620853 0.3791469
##          Sevré  81.777251 3.2227488
chisq.test(comp_qual$Tabac,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$Tabac, comp_qual$IgG_deficit, correct = FALSE):
## Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Tabac and comp_qual$IgG_deficit
## X-squared = 0.65298, df = 3, p-value = 0.8842
fisher.test(comp_qual$Tabac,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Tabac and comp_qual$IgG_deficit
## p-value = 0.9225
## alternative hypothesis: two.sided

Atopie

xtabs(~Atopie+IgG_deficit, data=comp_qual)
##       IgG_deficit
## Atopie Non Oui
##    Non 152   8
##    Oui  98   0
chisq.test(comp_qual$Atopie,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$Atopie, comp_qual$IgG_deficit): Chi-squared
## approximation may be incorrect
##                 comp_qual$IgG_deficit
## comp_qual$Atopie       Non     Oui
##              Non 155.03876 4.96124
##              Oui  94.96124 3.03876
chisq.test(comp_qual$Atopie,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$Atopie, comp_qual$IgG_deficit, correct = FALSE):
## Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Atopie and comp_qual$IgG_deficit
## X-squared = 5.0568, df = 1, p-value = 0.02453
fisher.test(comp_qual$Atopie,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Atopie and comp_qual$IgG_deficit
## p-value = 0.02587
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.0000000 0.9345261
## sample estimates:
## odds ratio 
##          0

la p-value =0.025, IC[0.0000000 ;0.9345261]; OR=0 :Il y’a donc une correlation significative entre l’atopie et les IgG en deficit dans le sens de l’absence d’atopie

FeNO > 20

xtabs(~FeNo_sup_20_ppb+IgG_deficit, data=comp_qual)
##                IgG_deficit
## FeNo_sup_20_ppb Non Oui
##             Non  55   1
##             Oui  81   1
chisq.test(comp_qual$FeNo_sup_20_ppb,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$FeNo_sup_20_ppb, comp_qual$IgG_deficit): Chi-
## squared approximation may be incorrect
##                          comp_qual$IgG_deficit
## comp_qual$FeNo_sup_20_ppb      Non       Oui
##                       Non 55.18841 0.8115942
##                       Oui 80.81159 1.1884058
chisq.test(comp_qual$FeNo_sup_20_ppb,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$FeNo_sup_20_ppb, comp_qual$IgG_deficit, : Chi-
## squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$FeNo_sup_20_ppb and comp_qual$IgG_deficit
## X-squared = 0.074689, df = 1, p-value = 0.7846
fisher.test(comp_qual$FeNo_sup_20_ppb,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$FeNo_sup_20_ppb and comp_qual$IgG_deficit
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   0.008554044 54.237797647
## sample estimates:
## odds ratio 
##  0.6809822

il n’y a clairement pas assez de patients pour pouvoir affirmer ou refuter une relation de dependance ou independance des données

PNN > 5

xtabs(~PNN_sup_5+IgG_deficit, data=comp_qual)
##          IgG_deficit
## PNN_sup_5 Non Oui
##       Non 105   1
##       Oui 125   5
chisq.test(comp_qual$PNN_sup_5,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$PNN_sup_5, comp_qual$IgG_deficit): Chi-squared
## approximation may be incorrect
##                    comp_qual$IgG_deficit
## comp_qual$PNN_sup_5      Non      Oui
##                 Non 103.3051 2.694915
##                 Oui 126.6949 3.305085
chisq.test(comp_qual$PNN_sup_5,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$PNN_sup_5, comp_qual$IgG_deficit, correct =
## FALSE): Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$PNN_sup_5 and comp_qual$IgG_deficit
## X-squared = 1.9857, df = 1, p-value = 0.1588
fisher.test(comp_qual$PNN_sup_5,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$PNN_sup_5 and comp_qual$IgG_deficit
## p-value = 0.2274
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##    0.4573631 200.3188242
## sample estimates:
## odds ratio 
##   4.178951

Les deux variable sont independantes

PNE > 0.15

xtabs(~PNE_sup_0.15G_L+IgG_deficit, data=comp_qual)
##                IgG_deficit
## PNE_sup_0.15G_L Non Oui
##             Non  93   5
##             Oui 136   1
chisq.test(comp_qual$PNE_sup_0.15G_L,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$PNE_sup_0.15G_L, comp_qual$IgG_deficit): Chi-
## squared approximation may be incorrect
##                          comp_qual$IgG_deficit
## comp_qual$PNE_sup_0.15G_L       Non      Oui
##                       Non  95.49787 2.502128
##                       Oui 133.50213 3.497872
chisq.test(comp_qual$PNE_sup_0.15G_L,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$PNE_sup_0.15G_L, comp_qual$IgG_deficit, : Chi-
## squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$PNE_sup_0.15G_L and comp_qual$IgG_deficit
## X-squared = 4.3895, df = 1, p-value = 0.03616
fisher.test(comp_qual$PNE_sup_0.15G_L,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$PNE_sup_0.15G_L and comp_qual$IgG_deficit
## p-value = 0.08468
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.002876064 1.259896153
## sample estimates:
## odds ratio 
##  0.1378348

PNE >0.3G/L

xtabs(~PNE_sup_0.3.G_L+IgG_deficit, data=comp_qual)
##                IgG_deficit
## PNE_sup_0.3.G_L Non Oui
##             Non 128   5
##             Oui 101   1
chisq.test(comp_qual$PNE_sup_0.3.G_L,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$PNE_sup_0.3.G_L, comp_qual$IgG_deficit): Chi-
## squared approximation may be incorrect
##                          comp_qual$IgG_deficit
## comp_qual$PNE_sup_0.3.G_L       Non      Oui
##                       Non 129.60426 3.395745
##                       Oui  99.39574 2.604255
chisq.test(comp_qual$PNE_sup_0.3.G_L,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$PNE_sup_0.3.G_L, comp_qual$IgG_deficit, : Chi-
## squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$PNE_sup_0.3.G_L and comp_qual$IgG_deficit
## X-squared = 1.7919, df = 1, p-value = 0.1807
fisher.test(comp_qual$PNE_sup_0.3.G_L,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$PNE_sup_0.3.G_L and comp_qual$IgG_deficit
## p-value = 0.2374
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.005311982 2.327576263
## sample estimates:
## odds ratio 
##  0.2546856

CRP >5mg/l

xtabs(~CRP_sup_5.mg_l+IgG_deficit, data=comp_qual)
##               IgG_deficit
## CRP_sup_5.mg_l Non Oui
##            Non 103   3
##            Oui  47   2
chisq.test(comp_qual$CRP_sup_5.mg_l,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$CRP_sup_5.mg_l, comp_qual$IgG_deficit): Chi-
## squared approximation may be incorrect
##                         comp_qual$IgG_deficit
## comp_qual$CRP_sup_5.mg_l       Non      Oui
##                      Non 102.58065 3.419355
##                      Oui  47.41935 1.580645
chisq.test(comp_qual$CRP_sup_5.mg_l,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$CRP_sup_5.mg_l, comp_qual$IgG_deficit, correct =
## FALSE): Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$CRP_sup_5.mg_l and comp_qual$IgG_deficit
## X-squared = 0.16811, df = 1, p-value = 0.6818
fisher.test(comp_qual$CRP_sup_5.mg_l,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$CRP_sup_5.mg_l and comp_qual$IgG_deficit
## p-value = 0.6515
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   0.1180417 13.1608675
## sample estimates:
## odds ratio 
##   1.457236

IgE totale >30 KUA/l

xtabs(~IgE_Total_sup_30_kUA_l+IgG_deficit, data=comp_qual)
##                       IgG_deficit
## IgE_Total_sup_30_kUA_l Non Oui
##                    Non  28   2
##                    Oui 148   3
chisq.test(comp_qual$IgE_Total_sup_30_kUA_l,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$IgE_Total_sup_30_kUA_l, comp_qual$IgG_deficit):
## Chi-squared approximation may be incorrect
##      comp_qual$IgG_deficit
##             Non       Oui
##   Non  29.17127 0.8287293
##   Oui 146.82873 4.1712707
chisq.test(comp_qual$IgE_Total_sup_30_kUA_l,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$IgE_Total_sup_30_kUA_l, comp_qual$IgG_deficit, :
## Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$IgE_Total_sup_30_kUA_l and comp_qual$IgG_deficit
## X-squared = 2.0407, df = 1, p-value = 0.1531
fisher.test(comp_qual$IgE_Total_sup_30_kUA_l,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$IgE_Total_sup_30_kUA_l and comp_qual$IgG_deficit
## p-value = 0.1926
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.03134404 3.57596023
## sample estimates:
## odds ratio 
##  0.2865624

Profil T2

xtabs(~Profil_T2+IgG_deficit, data=comp_qual)
##          IgG_deficit
## Profil_T2 Non Oui
##       Neg  83   2
##       Pos  45   0
chisq.test(comp_qual$Profil_T2,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$Profil_T2, comp_qual$IgG_deficit): Chi-squared
## approximation may be incorrect
##                    comp_qual$IgG_deficit
## comp_qual$Profil_T2      Non       Oui
##                 Neg 83.69231 1.3076923
##                 Pos 44.30769 0.6923077
chisq.test(comp_qual$Profil_T2,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$Profil_T2, comp_qual$IgG_deficit, correct =
## FALSE): Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Profil_T2 and comp_qual$IgG_deficit
## X-squared = 1.0754, df = 1, p-value = 0.2997
fisher.test(comp_qual$Profil_T2,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Profil_T2 and comp_qual$IgG_deficit
## p-value = 0.5438
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   0.00000 10.08195
## sample estimates:
## odds ratio 
##          0

Réponse Biotherapie

xtabs(~Rep_biotherapie_GETE+IgG_deficit, data=comp_qual)
##                     IgG_deficit
## Rep_biotherapie_GETE Non Oui
##                      177   4
##          Aggravation   8   1
##          Bonne        24   1
##          Excellent     5   0
##          Faible       27   1
##          modérée       9   1
chisq.test(comp_qual$Rep_biotherapie_GETE,comp_qual$IgG_deficit)$expected
## Warning in chisq.test(comp_qual$Rep_biotherapie_GETE, comp_qual$IgG_deficit):
## Chi-squared approximation may be incorrect
##                               comp_qual$IgG_deficit
## comp_qual$Rep_biotherapie_GETE        Non       Oui
##                                175.387597 5.6124031
##                    Aggravation   8.720930 0.2790698
##                    Bonne        24.224806 0.7751938
##                    Excellent     4.844961 0.1550388
##                    Faible       27.131783 0.8682171
##                    modérée       9.689922 0.3100775
chisq.test(comp_qual$Rep_biotherapie_GETE,comp_qual$IgG_deficit, correct=FALSE)
## Warning in chisq.test(comp_qual$Rep_biotherapie_GETE, comp_qual$IgG_deficit, :
## Chi-squared approximation may be incorrect
## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Rep_biotherapie_GETE and comp_qual$IgG_deficit
## X-squared = 4.2322, df = 5, p-value = 0.5165
fisher.test(comp_qual$Rep_biotherapie_GETE,comp_qual$IgG_deficit)
## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Rep_biotherapie_GETE and comp_qual$IgG_deficit
## p-value = 0.1948
## alternative hypothesis: two.sided