comparaison IgG4 Exces VS normaux-deficit

Evaluation des conditions de validité:Normalité pour les données IgG 2 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$age_inclusion[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$age_inclusion[Ig$IgG4_Exces == "Oui"]
## W = 0.95799, p-value = 0.2929

wilcoxon

shapiro.test(Ig$age_inclusion[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$age_inclusion[Ig$IgG4_Exces == "Non"]
## W = 0.98012, p-value = 0.002618

Student

IMC

shapiro.test(Ig$IMC[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$IMC[Ig$IgG4_Exces == "Oui"]
## W = 0.85252, p-value = 0.002436

la normalité est acceptée: T-test de Student

shapiro.test(Ig$IMC[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$IMC[Ig$IgG4_Exces == "Non"]
## W = 0.85616, p-value = 1.537e-10

student

Nombre d’exacerbation sans biotherapie

shapiro.test(Ig$Nb_Exa_an.sans_biotherapie[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Nb_Exa_an.sans_biotherapie[Ig$IgG4_Exces == "Oui"]
## W = 0.85872, p-value = 0.00211

shapiro.test(Ig$Nb_Exa_an.sans_biotherapie[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Nb_Exa_an.sans_biotherapie[Ig$IgG4_Exces == "Non"]
## W = 0.39678, p-value < 2.2e-16

Student

Dose de corticoides systemiques

shapiro.test(Ig$Corticoïde.systémique..dose.[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Corticoïde.systémique..dose.[Ig$IgG4_Exces == "Oui"]
## W = 0.54117, p-value = 6.629e-08

shapiro.test(Ig$Corticoïde.systémique..dose.[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Corticoïde.systémique..dose.[Ig$IgG4_Exces == "Non"]
## W = 0.52639, p-value < 2.2e-16

Student

CSI

shapiro.test(Ig$CSI_µg[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$CSI_µg[Ig$IgG4_Exces == "Oui"]
## W = 0.82949, p-value = 0.0004644

la normalité est acceptée : Student

shapiro.test(Ig$CSI_µg[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$CSI_µg[Ig$IgG4_Exces == "Non"]
## W = 0.87904, p-value = 2.986e-12

Student

Montélukast

shapiro.test(Ig$Montélukast[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Montélukast[Ig$IgG4_Exces == "Oui"]
## W = 0.54089, p-value = 3.093e-08

shapiro.test(Ig$Montélukast[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Montélukast[Ig$IgG4_Exces == "Non"]
## W = 0.61725, p-value < 2.2e-16

Student

Réponse Biotherapie

shapiro.test(Ig$Rep_biotherapie_GETE[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Rep_biotherapie_GETE[Ig$IgG4_Exces == "Oui"]
## W = 0.61811, p-value = 2.399e-07

la normalité est acceptée : t-test de Student

shapiro.test(Ig$Rep_biotherapie_GETE[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Rep_biotherapie_GETE[Ig$IgG4_Exces == "Non"]
## W = 0.64998, p-value < 2.2e-16

student

VEMS pre B2 (litre)

shapiro.test(Ig$VEMS_pre_B2_L[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$VEMS_pre_B2_L[Ig$IgG4_Exces == "Oui"]
## W = 0.95499, p-value = 0.3462

Wilcoxon

shapiro.test(Ig$VEMS_pre_B2_L[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$VEMS_pre_B2_L[Ig$IgG4_Exces == "Non"]
## W = 0.97809, p-value = 0.003425

Student

VEMS pre B2 (%)

shapiro.test(Ig$VEMS_PreB2_Pct[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$VEMS_PreB2_Pct[Ig$IgG4_Exces == "Oui"]
## W = 0.95197, p-value = 0.2578

wilcoxon

shapiro.test(Ig$VEMS_PreB2_Pct[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$VEMS_PreB2_Pct[Ig$IgG4_Exces == "Non"]
## W = 0.98285, p-value = 0.01491

Student

Tiffe

shapiro.test(Ig$Tiffenau[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Tiffenau[Ig$IgG4_Exces == "Oui"]
## W = 0.9791, p-value = 0.8542

Wilcoxon

shapiro.test(Ig$Tiffenau[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$Tiffenau[Ig$IgG4_Exces == "Non"]
## W = 0.98898, p-value = 0.1328

Wilcoxon

FeNO

shapiro.test(Ig$FeNo[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$FeNo[Ig$IgG4_Exces == "Non"]
## W = 0.7917, p-value = 6.335e-12

Student

shapiro.test(Ig$FeNo[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$FeNo[Ig$IgG4_Exces == "Oui"]
## W = 0.81347, p-value = 0.005503

Student

PNE G/L

shapiro.test(Ig$PNE_G_L[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$PNE_G_L[Ig$IgG4_Exces == "Oui"]
## W = 0.75662, p-value = 2.007e-05

Student

shapiro.test(Ig$PNE_G_L[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$PNE_G_L[Ig$IgG4_Exces == "Non"]
## W = 0.75389, p-value < 2.2e-16

Student

IgGE total

shapiro.test(Ig$IgE_Total[Ig$IgG4_Exces =="Oui"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$IgE_Total[Ig$IgG4_Exces == "Oui"]
## W = 0.81498, p-value = 0.000672

Student

shapiro.test(Ig$IgE_Total[Ig$IgG4_Exces =="Non"])

## 
##  Shapiro-Wilk normality test
## 
## data:  Ig$IgE_Total[Ig$IgG4_Exces == "Non"]
## W = 0.29522, p-value < 2.2e-16

student

Comparaison des données IgG deficit Vs Normaux + Excès

Age

wilcox.test(Ig$age_inclusion~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$age_inclusion by Ig$IgG4_Exces
## W = 2681, p-value = 0.09145
## alternative hypothesis: true location shift is not equal to 0

IMC

t.test(Ig$IMC~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Two Sample t-test
## 
## data:  Ig$IMC by Ig$IgG4_Exces
## t = 0.60936, df = 166, p-value = 0.5431
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -2.101604  3.977993
## sample estimates:
## mean in group Non mean in group Oui 
##          26.70486          25.76667

Nombre d’exacerbation sans biotherapie

t.test(Ig$Nb_Exa_an.sans_biotherapie~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Two Sample t-test
## 
## data:  Ig$Nb_Exa_an.sans_biotherapie by Ig$IgG4_Exces
## t = 0.12858, df = 207, p-value = 0.8978
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -3.262506  3.717739
## sample estimates:
## mean in group Non mean in group Oui 
##          4.573770          4.346154

Le test ne met pas en évidence une relation significative

Dose de corticoides systemiques

t.test(Ig$Corticoïde.systémique..dose.~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Two Sample t-test
## 
## data:  Ig$Corticoïde.systémique..dose. by Ig$IgG4_Exces
## t = 0.78504, df = 229, p-value = 0.4332
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -3.262168  7.583181
## sample estimates:
## mean in group Non mean in group Oui 
##          6.295122          4.134615

Le test ne met pas en évidence une relation significative

CSI

t.test(Ig$CSI_µg~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Two Sample t-test
## 
## data:  Ig$CSI_µg by Ig$IgG4_Exces
## t = -0.50734, df = 245, p-value = 0.6124
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -640.3216  378.0226
## sample estimates:
## mean in group Non mean in group Oui 
##          1591.073          1722.222

Montélukast

t.test(Ig$Montélukast~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Two Sample t-test
## 
## data:  Ig$Montélukast by Ig$IgG4_Exces
## t = 1.4054, df = 254, p-value = 0.1611
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.05456001  0.32648983
## sample estimates:
## mean in group Non mean in group Oui 
##         0.3859649         0.2500000

Réponse Biotherapie

t.test(Ig$Rep_biotherapie_GETE~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Two Sample t-test
## 
## data:  Ig$Rep_biotherapie_GETE by Ig$IgG4_Exces
## t = 0.59526, df = 242, p-value = 0.5522
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.4444280  0.8293486
## sample estimates:
## mean in group Non mean in group Oui 
##         1.0138889         0.8214286

VEMS pre B2 (litre)

wilcox.test(Ig$VEMS_pre_B2_L~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$VEMS_pre_B2_L by Ig$IgG4_Exces
## W = 2192.5, p-value = 0.538
## alternative hypothesis: true location shift is not equal to 0

VEMS pre B2 (%)

wilcox.test(Ig$VEMS_PreB2_Pct~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$VEMS_PreB2_Pct by Ig$IgG4_Exces
## W = 2716.5, p-value = 0.7437
## alternative hypothesis: true location shift is not equal to 0

Tiffe

wilcox.test(Ig$Tiffenau~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$Tiffenau by Ig$IgG4_Exces
## W = 2714.5, p-value = 0.6207
## alternative hypothesis: true location shift is not equal to 0

FeNO

t.test(Ig$FeNo~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Two Sample t-test
## 
## data:  Ig$FeNo by Ig$IgG4_Exces
## t = 0.41398, df = 136, p-value = 0.6795
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -14.28481  21.84904
## sample estimates:
## mean in group Non mean in group Oui 
##          36.04878          32.26667

PNE_Gl

t.test(Ig$PNE_G_L~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Two Sample t-test
## 
## data:  Ig$PNE_G_L by Ig$IgG4_Exces
## t = -4.4324, df = 233, p-value = 1.436e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.5558400 -0.2137528
## sample estimates:
## mean in group Non mean in group Oui 
##         0.3009179         0.6857143

la p value est significative

IgGE total

wilcox.test(Ig$IgE_Total~Ig$IgG4_Exces, var.equal=TRUE)

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  Ig$IgE_Total by Ig$IgG4_Exces
## W = 1134.5, p-value = 0.003672
## 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+IgG4_Exces, data=comp_qual)

##     IgG4_Exces
## Sexe Non Oui
##    F 144  10
##    M  85  19

chisq.test(comp_qual$Sexe,comp_qual$IgG4_Exces)$expected

##               comp_qual$IgG4_Exces
## comp_qual$Sexe       Non      Oui
##              F 136.68992 17.31008
##              M  92.31008 11.68992

chisq.test(comp_qual$Sexe,comp_qual$IgG4_Exces, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Sexe and comp_qual$IgG4_Exces
## X-squared = 8.6281, df = 1, p-value = 0.00331

fisher.test(comp_qual$Sexe,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Sexe and comp_qual$IgG4_Exces
## p-value = 0.004528
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  1.343692 8.097948
## sample estimates:
## odds ratio 
##   3.203632

Montélukast

xtabs(~Montélukast+IgG4_Exces, data=comp_qual)

##            IgG4_Exces
## Montélukast Non Oui
##         Non 140  21
##         Oui  88   7

chisq.test(comp_qual$Montélukast,comp_qual$IgG4_Exces)$expected

##                      comp_qual$IgG4_Exces
## comp_qual$Montélukast       Non      Oui
##                   Non 143.39062 17.60938
##                   Oui  84.60938 10.39062

chisq.test(comp_qual$Montélukast,comp_qual$IgG4_Exces, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Montélukast and comp_qual$IgG4_Exces
## X-squared = 1.9753, df = 1, p-value = 0.1599

fisher.test(comp_qual$Montélukast,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Montélukast and comp_qual$IgG4_Exces
## p-value = 0.2138
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.1830535 1.3657929
## sample estimates:
## odds ratio 
##  0.5315235

Biotherapie

xtabs(~Biotherapie+IgG4_Exces, data=comp_qual)

##            IgG4_Exces
## Biotherapie Non Oui
##         Non 149  19
##         Oui  80  10

chisq.test(comp_qual$Biotherapie,comp_qual$IgG4_Exces)$expected

##                      comp_qual$IgG4_Exces
## comp_qual$Biotherapie       Non      Oui
##                   Non 149.11628 18.88372
##                   Oui  79.88372 10.11628

chisq.test(comp_qual$Biotherapie,comp_qual$IgG4_Exces, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Biotherapie and comp_qual$IgG4_Exces
## X-squared = 0.0023125, df = 1, p-value = 0.9616

fisher.test(comp_qual$Biotherapie,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Biotherapie and comp_qual$IgG4_Exces
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.3875021 2.3427280
## sample estimates:
## odds ratio 
##  0.9803379

type de biotherapie

xtabs(~Biothérapie_type+IgG4_Exces, data=comp_qual)

##                 IgG4_Exces
## Biothérapie_type Non Oui
##     Benralizumab   6   4
##     Dupilumab      3   1
##     Mepolizumab   49   3
##     Omalixumab    22   2

chisq.test(comp_qual$Biothérapie_type,comp_qual$IgG4_Exces)$expected

## Warning in chisq.test(comp_qual$Biothérapie_type, comp_qual$IgG4_Exces): Chi-
## squared approximation may be incorrect

##                           comp_qual$IgG4_Exces
## comp_qual$Biothérapie_type       Non       Oui
##               Benralizumab  8.888889 1.1111111
##               Dupilumab     3.555556 0.4444444
##               Mepolizumab  46.222222 5.7777778
##               Omalixumab   21.333333 2.6666667

chisq.test(comp_qual$Biothérapie_type,comp_qual$IgG4_Exces, correct=FALSE)

## Warning in chisq.test(comp_qual$Biothérapie_type, comp_qual$IgG4_Exces, : Chi-
## squared approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Biothérapie_type and comp_qual$IgG4_Exces
## X-squared = 10.921, df = 3, p-value = 0.01216

fisher.test(comp_qual$Biothérapie_type,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Biothérapie_type and comp_qual$IgG4_Exces
## p-value = 0.01538
## alternative hypothesis: two.sided

Tabac

xtabs(~Tabac+IgG4_Exces, data=comp_qual)

##         IgG4_Exces
## Tabac    Non Oui
##   Actif   25   0
##   Non     80  11
##   Passif  10   0
##   Sevré   73  12

chisq.test(comp_qual$Tabac,comp_qual$IgG4_Exces)$expected

## Warning in chisq.test(comp_qual$Tabac, comp_qual$IgG4_Exces): Chi-squared
## approximation may be incorrect

##                comp_qual$IgG4_Exces
## comp_qual$Tabac       Non      Oui
##          Actif  22.274882 2.725118
##          Non    81.080569 9.919431
##          Passif  8.909953 1.090047
##          Sevré  75.734597 9.265403

chisq.test(comp_qual$Tabac,comp_qual$IgG4_Exces, correct=FALSE)

## Warning in chisq.test(comp_qual$Tabac, comp_qual$IgG4_Exces, correct = FALSE):
## Chi-squared approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Tabac and comp_qual$IgG4_Exces
## X-squared = 5.3199, df = 3, p-value = 0.1498

fisher.test(comp_qual$Tabac,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Tabac and comp_qual$IgG4_Exces
## p-value = 0.1572
## alternative hypothesis: two.sided

Atopie

xtabs(~Atopie+IgG4_Exces, data=comp_qual)

##       IgG4_Exces
## Atopie Non Oui
##    Non 139  21
##    Oui  90   8

chisq.test(comp_qual$Atopie,comp_qual$IgG4_Exces)$expected

##                 comp_qual$IgG4_Exces
## comp_qual$Atopie      Non     Oui
##              Non 142.0155 17.9845
##              Oui  86.9845 11.0155

chisq.test(comp_qual$Atopie,comp_qual$IgG4_Exces, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Atopie and comp_qual$IgG4_Exces
## X-squared = 1.4997, df = 1, p-value = 0.2207

fisher.test(comp_qual$Atopie,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Atopie and comp_qual$IgG4_Exces
## p-value = 0.3098
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.2161731 1.4591991
## sample estimates:
## odds ratio 
##  0.5895024

FeNO > 20

xtabs(~FeNo_sup_20_ppb+IgG4_Exces, data=comp_qual)

##                IgG4_Exces
## FeNo_sup_20_ppb Non Oui
##             Non  51   5
##             Oui  72  10

chisq.test(comp_qual$FeNo_sup_20_ppb,comp_qual$IgG4_Exces)$expected

##                          comp_qual$IgG4_Exces
## comp_qual$FeNo_sup_20_ppb      Non      Oui
##                       Non 49.91304 6.086957
##                       Oui 73.08696 8.913043

chisq.test(comp_qual$FeNo_sup_20_ppb,comp_qual$IgG4_Exces, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$FeNo_sup_20_ppb and comp_qual$IgG4_Exces
## X-squared = 0.36649, df = 1, p-value = 0.5449

fisher.test(comp_qual$FeNo_sup_20_ppb,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$FeNo_sup_20_ppb and comp_qual$IgG4_Exces
## p-value = 0.5917
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.4100341 5.5958576
## sample estimates:
## odds ratio 
##   1.413199

PNN > 5

xtabs(~PNN_sup_5+IgG4_Exces, data=comp_qual)

##          IgG4_Exces
## PNN_sup_5 Non Oui
##       Non  92  14
##       Oui 116  14

chisq.test(comp_qual$PNN_sup_5,comp_qual$IgG4_Exces)$expected

##                    comp_qual$IgG4_Exces
## comp_qual$PNN_sup_5       Non      Oui
##                 Non  93.42373 12.57627
##                 Oui 114.57627 15.42373

chisq.test(comp_qual$PNN_sup_5,comp_qual$IgG4_Exces, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$PNN_sup_5 and comp_qual$IgG4_Exces
## X-squared = 0.33199, df = 1, p-value = 0.5645

fisher.test(comp_qual$PNN_sup_5,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$PNN_sup_5 and comp_qual$IgG4_Exces
## p-value = 0.6863
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.3322087 1.8968634
## sample estimates:
## odds ratio 
##   0.793894

Les deux variable sont independantes

PNE > 0.15

xtabs(~PNE_sup_0.15G_L+IgG4_Exces, data=comp_qual)

##                IgG4_Exces
## PNE_sup_0.15G_L Non Oui
##             Non  90   8
##             Oui 117  20

chisq.test(comp_qual$PNE_sup_0.15G_L,comp_qual$IgG4_Exces)$expected

##                          comp_qual$IgG4_Exces
## comp_qual$PNE_sup_0.15G_L      Non     Oui
##                       Non  86.3234 11.6766
##                       Oui 120.6766 16.3234

chisq.test(comp_qual$PNE_sup_0.15G_L,comp_qual$IgG4_Exces, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$PNE_sup_0.15G_L and comp_qual$IgG4_Exces
## X-squared = 2.2543, df = 1, p-value = 0.1332

fisher.test(comp_qual$PNE_sup_0.15G_L,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$PNE_sup_0.15G_L and comp_qual$IgG4_Exces
## p-value = 0.1559
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.7660622 5.2760122
## sample estimates:
## odds ratio 
##   1.918012

PNE >0.3G/L

xtabs(~PNE_sup_0.3.G_L+IgG4_Exces, data=comp_qual)

##                IgG4_Exces
## PNE_sup_0.3.G_L Non Oui
##             Non 123  10
##             Oui  84  18

chisq.test(comp_qual$PNE_sup_0.3.G_L,comp_qual$IgG4_Exces)$expected

##                          comp_qual$IgG4_Exces
## comp_qual$PNE_sup_0.3.G_L       Non      Oui
##                       Non 117.15319 15.84681
##                       Oui  89.84681 12.15319

chisq.test(comp_qual$PNE_sup_0.3.G_L,comp_qual$IgG4_Exces, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$PNE_sup_0.3.G_L and comp_qual$IgG4_Exces
## X-squared = 5.6424, df = 1, p-value = 0.01753

fisher.test(comp_qual$PNE_sup_0.3.G_L,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$PNE_sup_0.3.G_L and comp_qual$IgG4_Exces
## p-value = 0.02444
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  1.085738 6.701471
## sample estimates:
## odds ratio 
##   2.624659

CRP >5mg/l

xtabs(~CRP_sup_5.mg_l+IgG4_Exces, data=comp_qual)

##               IgG4_Exces
## CRP_sup_5.mg_l Non Oui
##            Non  95  11
##            Oui  41   8

chisq.test(comp_qual$CRP_sup_5.mg_l,comp_qual$IgG4_Exces)$expected

##                         comp_qual$IgG4_Exces
## comp_qual$CRP_sup_5.mg_l      Non       Oui
##                      Non 93.00645 12.993548
##                      Oui 42.99355  6.006452

chisq.test(comp_qual$CRP_sup_5.mg_l,comp_qual$IgG4_Exces, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$CRP_sup_5.mg_l and comp_qual$IgG4_Exces
## X-squared = 1.1027, df = 1, p-value = 0.2937

fisher.test(comp_qual$CRP_sup_5.mg_l,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$CRP_sup_5.mg_l and comp_qual$IgG4_Exces
## p-value = 0.3025
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.5435065 4.9792874
## sample estimates:
## odds ratio 
##   1.679123

IgE totale >30 KUA/l

xtabs(~IgE_Total_sup_30_kUA_l+IgG4_Exces, data=comp_qual)

##                       IgG4_Exces
## IgE_Total_sup_30_kUA_l Non Oui
##                    Non  28   2
##                    Oui 130  21

chisq.test(comp_qual$IgE_Total_sup_30_kUA_l,comp_qual$IgG4_Exces)$expected

## Warning in chisq.test(comp_qual$IgE_Total_sup_30_kUA_l, comp_qual$IgG4_Exces):
## Chi-squared approximation may be incorrect

##      comp_qual$IgG4_Exces
##             Non       Oui
##   Non  26.18785  3.812155
##   Oui 131.81215 19.187845

chisq.test(comp_qual$IgE_Total_sup_30_kUA_l,comp_qual$IgG4_Exces, correct=FALSE)

## Warning in chisq.test(comp_qual$IgE_Total_sup_30_kUA_l, comp_qual$IgG4_Exces, :
## Chi-squared approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$IgE_Total_sup_30_kUA_l and comp_qual$IgG4_Exces
## X-squared = 1.1829, df = 1, p-value = 0.2768

fisher.test(comp_qual$IgE_Total_sup_30_kUA_l,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$IgE_Total_sup_30_kUA_l and comp_qual$IgG4_Exces
## p-value = 0.377
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   0.5014243 20.9354270
## sample estimates:
## odds ratio 
##   2.253455

Profil T2

xtabs(~Profil_T2+IgG4_Exces, data=comp_qual)

##          IgG4_Exces
## Profil_T2 Non Oui
##       Neg  76   9
##       Pos  39   6

chisq.test(comp_qual$Profil_T2,comp_qual$IgG4_Exces)$expected

##                    comp_qual$IgG4_Exces
## comp_qual$Profil_T2      Non      Oui
##                 Neg 75.19231 9.807692
##                 Pos 39.80769 5.192308

chisq.test(comp_qual$Profil_T2,comp_qual$IgG4_Exces, correct=FALSE)

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Profil_T2 and comp_qual$IgG4_Exces
## X-squared = 0.21722, df = 1, p-value = 0.6412

fisher.test(comp_qual$Profil_T2,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Profil_T2 and comp_qual$IgG4_Exces
## p-value = 0.7739
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.3527725 4.4267541
## sample estimates:
## odds ratio 
##   1.296419

Réponse biotherapie

xtabs(~Rep_biotherapie_GETE+IgG4_Exces, data=comp_qual)

##                     IgG4_Exces
## Rep_biotherapie_GETE Non Oui
##                      160  21
##          Aggravation   8   1
##          Bonne        22   3
##          Excellent     4   1
##          Faible       26   2
##          modérée       9   1

chisq.test(comp_qual$Rep_biotherapie_GETE,comp_qual$IgG4_Exces)$expected

## Warning in chisq.test(comp_qual$Rep_biotherapie_GETE, comp_qual$IgG4_Exces):
## Chi-squared approximation may be incorrect

##                               comp_qual$IgG4_Exces
## comp_qual$Rep_biotherapie_GETE        Non        Oui
##                                160.655039 20.3449612
##                    Aggravation   7.988372  1.0116279
##                    Bonne        22.189922  2.8100775
##                    Excellent     4.437984  0.5620155
##                    Faible       24.852713  3.1472868
##                    modérée       8.875969  1.1240310

chisq.test(comp_qual$Rep_biotherapie_GETE,comp_qual$IgG4_Exces, correct=FALSE)

## Warning in chisq.test(comp_qual$Rep_biotherapie_GETE, comp_qual$IgG4_Exces, :
## Chi-squared approximation may be incorrect

## 
##  Pearson's Chi-squared test
## 
## data:  comp_qual$Rep_biotherapie_GETE and comp_qual$IgG4_Exces
## X-squared = 0.90953, df = 5, p-value = 0.9695

fisher.test(comp_qual$Rep_biotherapie_GETE,comp_qual$IgG4_Exces)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  comp_qual$Rep_biotherapie_GETE and comp_qual$IgG4_Exces
## p-value = 0.9123
## alternative hypothesis: two.sided

comparaison IgG4 Exces VS normaux-deficit

MALLAH Siham

08/09/2021

Evaluation des conditions de validité:Normalité pour les données IgG 2 deficit Vs Normaux + Excès

Age à l’inclusion

IMC

Nombre d’exacerbation sans biotherapie

Dose de corticoides systemiques

CSI

Montélukast

Réponse Biotherapie

VEMS pre B2 (litre)

VEMS pre B2 (%)

Tiffe

FeNO

PNE G/L

IgGE total

Comparaison des données IgG deficit Vs Normaux + Excès

Age

IMC

Nombre d’exacerbation sans biotherapie

Dose de corticoides systemiques

CSI

Montélukast

Réponse Biotherapie

VEMS pre B2 (litre)

VEMS pre B2 (%)

Tiffe

FeNO

PNE_Gl

IgGE total

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

Sexe

Montélukast

Biotherapie

type de biotherapie

Tabac

Atopie

FeNO > 20

PNN > 5

PNE > 0.15

PNE >0.3G/L

CRP >5mg/l

IgE totale >30 KUA/l

Profil T2

Réponse biotherapie