Ellipticity Analysis v5

library(readxl)
library(ggplot2)
library(CGPfunctions)
library(plotly)
library(lmtest)

#Definir directorio
setwd("G:/TRABAJO/CONSULTORIAS/TRABAJOS VARIOS/JORGE CHAVARRIA/analisis3")

data1 = read_excel("Bicuspid3.xlsx")
head(data1,5)

ANALISIS ELLIPTICITY

#Relacion entre Maximum Sinus Diameter Indexed vs Ellipticity
g1=ggplot(data=data1,mapping=
            aes(x=MaxSinAnnDcalc,y=Ellipticity,))+geom_point()+theme_bw()+
            geom_smooth(method = "lm") 

g1 +  labs(title = "Ellipticity vs Maximum Sinus Diameter Indexed",
           x = "Maximum Sinus Diameter Indexed",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'

correlation_test1 <- cor.test(data1$MaxSinAnnDcalc, data1$Ellipticity)
print(correlation_test1)

    Pearson's product-moment correlation

data:  data1$MaxSinAnnDcalc and data1$Ellipticity
t = -0.037089, df = 97, p-value = 0.9705
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2010282  0.1937901
sample estimates:
         cor 
-0.003765816 

Podemos observar que la correlacion es nula entre estas variables

#Relacion entre Minimun Sinus Diameter Indexed vs Ellipticity
g2=ggplot(data=data1,mapping=
            aes(x=MinSinusAnnDcalc,y=Ellipticity,))+geom_point()+theme_bw()+
            geom_smooth(method = "lm") 

g2 +  labs(title = "Ellipticity vs Minimun Sinus Diameter Indexed",
           x = "Minimun Sinus Diameter Indexed",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'

correlation_test2 <- cor.test(data1$MinSinusAnnDcalc, data1$Ellipticity)
print(correlation_test2)

    Pearson's product-moment correlation

data:  data1$MinSinusAnnDcalc and data1$Ellipticity
t = 0.54578, df = 97, p-value = 0.5865
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1436501  0.2500118
sample estimates:
       cor 
0.05533081 

Tampoco existe correlacion entre estas variables

#Relacion entre Postdilatation vs Ellipticity
boxplot(data1$Ellipticity~data1$Postdilation,
        xlab = 'Postdilation',
        ylab = 'Ellipticity',
        title=('Postdilatation vs Ellipticity'),
          col= 'bisque')

t_test1 <- t.test(data1$Ellipticity ~ as.factor(data1$Postdilation))
print(t_test1)

    Welch Two Sample t-test

data:  data1$Ellipticity by as.factor(data1$Postdilation)
t = 0.95701, df = 28.291, p-value = 0.3467
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 -0.01228230  0.03384074
sample estimates:
mean in group 0 mean in group 1 
       1.097143        1.086364 

No se evidencia una diferencia significativa en la Ellipticity en funcion de la Postdilatation

ggplot(data = data1) + geom_density(aes(x=Ellipticity,fill=factor(Postdilation)),
                                    bins=10, position = "identity",alpha = 0.5)

Warning: Ignoring unknown parameters: bins

#Relacion entre Predilatation vs Ellipticity
boxplot(data1$Ellipticity~data1$Predilatation,
        xlab = 'Predilatation',
        ylab = 'Ellipticity',
        title=('Predilatation vs Ellipticity'),
          col= 'bisque')

t_test2 <- t.test(data1$Ellipticity ~ as.factor(data1$Predilatation))
print(t_test2)

    Welch Two Sample t-test

data:  data1$Ellipticity by as.factor(data1$Predilatation)
t = -0.28948, df = 56.74, p-value = 0.7733
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 -0.02078043  0.01553156
sample estimates:
mean in group 0 mean in group 1 
       1.093846        1.096471 

No existe una diferencia significativa de Ellipticity en función de la Predilatation

ggplot(data = data1) + geom_density(aes(x=Ellipticity,fill=factor(Predilatation)),
                                    bins=10, position = "identity",alpha = 0.5)

Warning: Ignoring unknown parameters: bins

#Relacion entre Raphe Calcification vs Ellipticity
boxplot(data1$Ellipticity~data1$Raphaecalcification,
        xlab = 'Raphae Calcification',
        ylab = 'Ellipticity',
        title=('Raphae Calcification vs Ellipticity'),
          col= 'ivory')

ggplot(data = data1) + geom_density(aes(x=Ellipticity,fill=factor(Raphaecalcification)),
                                    bins=10, position = "identity",alpha = 0.5)

Warning: Ignoring unknown parameters: bins

t_test3 <- t.test(data1$Ellipticity ~ as.factor(data1$Raphaecalcification))
print(t_test3)

    Welch Two Sample t-test

data:  data1$Ellipticity by as.factor(data1$Raphaecalcification)
t = -2.3326, df = 89.761, p-value = 0.02191
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
 -0.033216625 -0.002659574
sample estimates:
mean in group 0 mean in group 1 
       1.083514        1.101452 

Existe diferencia significativa de Ellipticity en función del Raphaecalcification

#Relacion entre AVAi vs Ellipticity
g4=ggplot(data=data1,mapping=
            aes(x=AVAi,y=Ellipticity,))+geom_point()+theme_bw()+
            geom_smooth(method = "lm") 

g4 +  labs(title = "Ellipticity vs AVAi",
           x = "AVAi",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'
Warning: Removed 24 rows containing non-finite values (stat_smooth).
Warning: Removed 24 rows containing missing values (geom_point).

correlation_test3 <- cor.test(data1$AVAi, data1$Ellipticity)
print(correlation_test3)

    Pearson's product-moment correlation

data:  data1$AVAi and data1$Ellipticity
t = -1.0448, df = 73, p-value = 0.2996
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3390060  0.1085693
sample estimates:
       cor 
-0.1213836 

No existe correlacion significativa

#Relacion entre Mean Gradient (mmHg) vs Ellipticity
g5=ggplot(data=data1,mapping=
            aes(x=MeanGradientmmHg,y=Ellipticity,))+geom_point()+theme_bw()+
            geom_smooth(method = "lm") 

g5 +  labs(title = "Ellipticity vs Mean Gradient (mmHg)",
           x = "Mean Gradient (mmHg)",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'

correlation_test4 <- cor.test(data1$MeanGradientmmHg, data1$Ellipticity)
print(correlation_test4)

    Pearson's product-moment correlation

data:  data1$MeanGradientmmHg and data1$Ellipticity
t = 1.8065, df = 97, p-value = 0.07394
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.01762871  0.36482930
sample estimates:
     cor 
0.180411 

la correlacion entre MeanGradientmmHg y RelativeStentExpansion es positiva y significativa

#Relacion entre ICD4mm vs Ellipticity
g6=ggplot(data=data1,mapping=
            aes(x=ICD4mm,y=Ellipticity,))+geom_point()+theme_bw()+
            geom_smooth(method = "lm") 

g6 +  labs(title = "Ellipticity vs Intercomisural Diameter at 4 mm Indexed",
           x = "Intercomisural Diameter at 4 mm Indexed",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'

correlation_test5 <- cor.test(data1$ICD4mm, data1$Ellipticity)
print(correlation_test5)

    Pearson's product-moment correlation

data:  data1$ICD4mm and data1$Ellipticity
t = 0.10506, df = 97, p-value = 0.9165
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1871391  0.2076414
sample estimates:
       cor 
0.01066681 

No existe correlacion significativa entre ICD4mm y Ellipticity

#Relacion entre CCV vs Ellipticity
g7=ggplot(data=data1,mapping=
            aes(x=CCV,y=Ellipticity,))+geom_point()+theme_bw()+
            geom_smooth(method = "lm") 

g7 +  labs(title = "Ellipticity vs Calcium contrast volume",
           x = "Calcium contrast volume",
           y= "Ellipticity") 

`geom_smooth()` using formula 'y ~ x'

correlation_test6 <- cor.test(data1$CCV, data1$Ellipticity)
print(correlation_test6)

    Pearson's product-moment correlation

data:  data1$CCV and data1$Ellipticity
t = 1.0569, df = 97, p-value = 0.2932
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.09266871  0.29783359
sample estimates:
     cor 
0.106695 

La correlacion entre CCV y Ellipticity no es significativa.

#Relacion entre CCV vs Ellipticitycalc
boxplot(data1$CCV~data1$Ellipticitycalc,
        xlab = 'Ellipticity',
        ylab = 'Calcium contrast volume',
        title=('Ellipticitycalc vs Calcium contrast volume'),
          col= 'ivory')

REGRESION LINEAL EN FUNCION DE Ellipticity

colnames(data1)

 [1] "StudyIDglobal"          "RelativeStentExpansion" "RSEcalc"                "Ellipticity"           
 [5] "Ellipticitycalc"        "MaxSinAnnDcalc"         "MinSinusAnnDcalc"       "Predilatation"         
 [9] "Postdilation"           "CCV"                    "Raphaecalcification"    "AVAi"                  
[13] "MeanGradientmmHg"       "ICD4mm"                 "ADDiameter"             "ICD4mm_calc"           
[17] "SVDmax"                 "SVDmin"                

#Regresion inicial
mod1=lm(Ellipticity ~ Predilatation + Postdilation +
                      CCV + Raphaecalcification +
                      ICD4mm + SVDmax
        , 
        data = data1)
summary(mod1)

Call:
lm(formula = Ellipticity ~ Predilatation + Postdilation + CCV + 
    Raphaecalcification + ICD4mm + SVDmax, data = data1)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.07665 -0.02952 -0.00092  0.02322  0.11700 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          1.119e+00  4.281e-02  26.136   <2e-16 ***
Predilatation        7.993e-04  8.773e-03   0.091   0.9276    
Postdilation        -9.231e-03  9.642e-03  -0.957   0.3409    
CCV                  6.266e-06  7.988e-06   0.784   0.4349    
Raphaecalcification  1.648e-02  8.671e-03   1.901   0.0605 .  
ICD4mm               1.915e-03  2.116e-03   0.905   0.3678    
SVDmax              -2.460e-03  1.293e-03  -1.902   0.0602 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.0393 on 92 degrees of freedom
Multiple R-squared:  0.09752,   Adjusted R-squared:  0.03866 
F-statistic: 1.657 on 6 and 92 DF,  p-value: 0.1406

# Obtener el resumen del modelo
summary_mod1 <- summary(mod1)

# Extraer los coeficientes
coefficients <- coef(summary_mod1)[, 1]

# Extraer los intervalos de confianza
conf_intervals <- confint(mod1)

# Extraer los p-values
p_values <- summary_mod1$coefficients[, 4]

# Crear el data frame consolidado
consolidado <- data.frame(Coefficient = coefficients,
                          Lower_CI = conf_intervals[, 1],
                          Upper_CI = conf_intervals[, 2],
                          p_value = p_values)

# Mostrar el data frame consolidado
print(consolidado)

NA

# Aplicar el procedimiento stepwise
mod1_1 <- step(mod1)

Start:  AIC=-634.08
Ellipticity ~ Predilatation + Postdilation + CCV + Raphaecalcification + 
    ICD4mm + SVDmax

                      Df Sum of Sq     RSS     AIC
- Predilatation        1 0.0000128 0.14213 -636.07
- CCV                  1 0.0009503 0.14306 -635.42
- ICD4mm               1 0.0012651 0.14338 -635.20
- Postdilation         1 0.0014159 0.14353 -635.10
<none>                             0.14211 -634.08
- Raphaecalcification  1 0.0055811 0.14769 -632.27
- SVDmax               1 0.0055905 0.14770 -632.26

Step:  AIC=-636.07
Ellipticity ~ Postdilation + CCV + Raphaecalcification + ICD4mm + 
    SVDmax

                      Df Sum of Sq     RSS     AIC
- CCV                  1 0.0010521 0.14318 -637.34
- ICD4mm               1 0.0012618 0.14339 -637.20
- Postdilation         1 0.0014094 0.14353 -637.09
<none>                             0.14213 -636.07
- SVDmax               1 0.0055784 0.14770 -634.26
- Raphaecalcification  1 0.0056006 0.14773 -634.24

Step:  AIC=-637.34
Ellipticity ~ Postdilation + Raphaecalcification + ICD4mm + SVDmax

                      Df Sum of Sq     RSS     AIC
- Postdilation         1 0.0012544 0.14443 -638.48
- ICD4mm               1 0.0023359 0.14551 -637.74
<none>                             0.14318 -637.34
- SVDmax               1 0.0050845 0.14826 -635.89
- Raphaecalcification  1 0.0080923 0.15127 -633.90

Step:  AIC=-638.48
Ellipticity ~ Raphaecalcification + ICD4mm + SVDmax

                      Df Sum of Sq     RSS     AIC
- ICD4mm               1 0.0029232 0.14736 -638.49
<none>                             0.14443 -638.48
- SVDmax               1 0.0055768 0.15001 -636.73
- Raphaecalcification  1 0.0083392 0.15277 -634.92

Step:  AIC=-638.49
Ellipticity ~ Raphaecalcification + SVDmax

                      Df Sum of Sq     RSS     AIC
- SVDmax               1 0.0026574 0.15001 -638.72
<none>                             0.14736 -638.49
- Raphaecalcification  1 0.0082956 0.15565 -635.07

Step:  AIC=-638.72
Ellipticity ~ Raphaecalcification

                      Df Sum of Sq     RSS     AIC
<none>                             0.15001 -638.72
- Raphaecalcification  1 0.0074561 0.15747 -635.92

summary(mod1_1)

Call:
lm(formula = Ellipticity ~ Raphaecalcification, data = data1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.071452 -0.032483 -0.001452  0.027517  0.108548 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         1.083514   0.006465 167.594   <2e-16 ***
Raphaecalcification 0.017938   0.008170   2.196   0.0305 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03933 on 97 degrees of freedom
Multiple R-squared:  0.04735,   Adjusted R-squared:  0.03753 
F-statistic: 4.821 on 1 and 97 DF,  p-value: 0.0305

REGRESION LOGISTICA EN FUNCION DE Ellipticitycalc(1;0)

#Modelo binario
mod2 <- glm(Ellipticitycalc ~ Predilatation + Postdilation +
                      CCV + Raphaecalcification +
                      ICD4mm + SVDmax
        , 
                data = data1, family = "binomial")
summary(mod2)

Call:
glm(formula = Ellipticitycalc ~ Predilatation + Postdilation + 
    CCV + Raphaecalcification + ICD4mm + SVDmax, family = "binomial", 
    data = data1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5372  -1.0507  -0.6165   1.0619   1.8547  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)          0.2145393  2.3226103   0.092  0.92640   
Predilatation       -0.2414236  0.4799513  -0.503  0.61495   
Postdilation        -0.3921355  0.5311670  -0.738  0.46036   
CCV                  0.0003218  0.0004496   0.716  0.47407   
Raphaecalcification  0.5690322  0.4702630   1.210  0.22627   
ICD4mm               0.2493679  0.1170813   2.130  0.03318 * 
SVDmax              -0.2091710  0.0764831  -2.735  0.00624 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 136.75  on 98  degrees of freedom
Residual deviance: 124.68  on 92  degrees of freedom
AIC: 138.68

Number of Fisher Scoring iterations: 4

# Aplicar el procedimiento stepwise
mod2_1 <- step(mod2)

Start:  AIC=138.68
Ellipticitycalc ~ Predilatation + Postdilation + CCV + Raphaecalcification + 
    ICD4mm + SVDmax

                      Df Deviance    AIC
- Predilatation        1   124.94 136.94
- CCV                  1   125.20 137.20
- Postdilation         1   125.23 137.24
- Raphaecalcification  1   126.17 138.17
<none>                     124.68 138.68
- ICD4mm               1   129.45 141.45
- SVDmax               1   133.22 145.22

Step:  AIC=136.94
Ellipticitycalc ~ Postdilation + CCV + Raphaecalcification + 
    ICD4mm + SVDmax

                      Df Deviance    AIC
- CCV                  1   125.32 135.32
- Postdilation         1   125.53 135.53
- Raphaecalcification  1   126.39 136.39
<none>                     124.94 136.94
- ICD4mm               1   129.68 139.68
- SVDmax               1   133.62 143.62

Step:  AIC=135.32
Ellipticitycalc ~ Postdilation + Raphaecalcification + ICD4mm + 
    SVDmax

                      Df Deviance    AIC
- Postdilation         1   125.84 133.84
<none>                     125.32 135.32
- Raphaecalcification  1   127.53 135.53
- ICD4mm               1   131.43 139.43
- SVDmax               1   133.66 141.66

Step:  AIC=133.84
Ellipticitycalc ~ Raphaecalcification + ICD4mm + SVDmax

                      Df Deviance    AIC
<none>                     125.84 133.84
- Raphaecalcification  1   128.14 134.14
- ICD4mm               1   132.64 138.64
- SVDmax               1   134.62 140.62

summary(mod2_1)

Call:
glm(formula = Ellipticitycalc ~ Raphaecalcification + ICD4mm + 
    SVDmax, family = "binomial", data = data1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6529  -1.0435  -0.6456   1.0645   1.7298  

Coefficients:
                    Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -0.64810    2.02707  -0.320  0.74918   
Raphaecalcification  0.66918    0.44553   1.502  0.13310   
ICD4mm               0.28054    0.11161   2.514  0.01195 * 
SVDmax              -0.20666    0.07406  -2.790  0.00527 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 136.75  on 98  degrees of freedom
Residual deviance: 125.84  on 95  degrees of freedom
AIC: 133.84

Number of Fisher Scoring iterations: 4

#Modelo binario Ellipticity (1;0) final
mod2_final <- glm(formula = Ellipticitycalc ~ Raphaecalcification + ICD4mm + 
    SVDmax, family = "binomial", data = data1)

# Obtener el resumen del modelo
summary_mod2 <- summary(mod2_final)
summary_mod2

Call:
glm(formula = Ellipticitycalc ~ Raphaecalcification + ICD4mm + 
    SVDmax, family = "binomial", data = data1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6529  -1.0435  -0.6456   1.0645   1.7298  

Coefficients:
                    Estimate Std. Error z value Pr(>|z|)   
(Intercept)         -0.64810    2.02707  -0.320  0.74918   
Raphaecalcification  0.66918    0.44553   1.502  0.13310   
ICD4mm               0.28054    0.11161   2.514  0.01195 * 
SVDmax              -0.20666    0.07406  -2.790  0.00527 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 136.75  on 98  degrees of freedom
Residual deviance: 125.84  on 95  degrees of freedom
AIC: 133.84

Number of Fisher Scoring iterations: 4

Calculos de Odd Ratio

# Extraer los odds ratios
odds_ratios <- exp(summary_mod2$coefficients[, 1])

# Extraer los intervalos de confianza
conf_intervals <- exp(confint(mod2_final))

Waiting for profiling to be done...

# Extraer los p-values
p_values <- summary_mod2$coefficients[, 4]

# Crear el data frame consolidado
consolidado <- data.frame(Odds_Ratio = odds_ratios,
                          Lower_CI = conf_intervals[, 1],
                          Upper_CI = conf_intervals[, 2],
                          p_value = p_values)

# Mostrar el data frame consolidado
print(consolidado)

NA

correlation_test6 <- cor.test(data1$ICD4mm_calc, data1$MinSinusAnnDcalc )
print(correlation_test6)

    Pearson's product-moment correlation

data:  data1$ICD4mm_calc and data1$MinSinusAnnDcalc
t = 2.5412, df = 97, p-value = 0.01263
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.0551484 0.4262298
sample estimates:
      cor 
0.2498402 

correlation_test7 <- cor.test(data1$ICD4mm, data1$SVDmin)
print(correlation_test7)

    Pearson's product-moment correlation

data:  data1$ICD4mm and data1$SVDmin
t = 6.6989, df = 97, p-value = 1.378e-09
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.4105785 0.6838884
sample estimates:
      cor 
0.5624056 

correlation_test8 <- cor.test(data1$ICD4mm, data1$Ellipticity)
print(correlation_test8)

    Pearson's product-moment correlation

data:  data1$ICD4mm and data1$Ellipticity
t = 0.10506, df = 97, p-value = 0.9165
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1871391  0.2076414
sample estimates:
       cor 
0.01066681 

correlation_test9 <- cor.test(data1$SVDmin, data1$Ellipticity)
print(correlation_test9)

    Pearson's product-moment correlation

data:  data1$SVDmin and data1$Ellipticity
t = -1.0149, df = 97, p-value = 0.3127
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.29396809  0.09686709
sample estimates:
       cor 
-0.1025049 

En el análisis realizado, se están evaluando dos situaciones diferentes:

Correlación de Pearson: En este caso, se calculó el coeficiente de correlación de Pearson entre las variables “ICD4mm” y “Ellipticity”. El coeficiente de correlación obtenido fue de 0.012, lo cual indica una correlación extremadamente débil entre ambas variables. Además, el valor p asociado a la prueba de hipótesis es de 0.9052, lo cual indica que no hay evidencia suficiente para rechazar la hipótesis nula de que la correlación sea igual a cero. El intervalo de confianza del 95% para la correlación está entre -0.1838663 y 0.2069580.

Regresión lineal: En este caso, se ajustó un modelo de regresión lineal utilizando la variable “Ellipticitycalc” como variable de respuesta y otras variables independientes. En el modelo, la variable “ICD4mm_calc” resultó significativa (p-value = 0.00157), lo cual indica que tiene un efecto estadísticamente significativo en la variable de respuesta. Sin embargo, es importante tener en cuenta que la significancia en un modelo de regresión no implica necesariamente una fuerte correlación entre las variables.

La diferencia entre los resultados puede deberse a que el coeficiente de correlación de Pearson evalúa la relación lineal entre dos variables de forma independiente de otras variables, mientras que la regresión lineal considera el efecto conjunto de todas las variables independientes en la variable de respuesta. En otras palabras, la significancia de “ICD4mm_calc” en el modelo de regresión puede deberse a su relación con las otras variables incluidas en el modelo, y no necesariamente a su correlación directa con “Ellipticitycalc”.

Es importante destacar que el análisis de los resultados debe considerar el contexto y los objetivos del estudio, así como tener en cuenta otras consideraciones estadísticas y de interpretación.