Peralta
Carro Gabriela
2026-08-24
library(readxl)
Base_de_datos_ampliada <- read_excel("C:/Users/Administrador/Downloads/Base de datos _ampliada.xlsx")
View(Base_de_datos_ampliada)datos=Base_de_datos_ampliada#transformar en factor
#datos <- datos %>%
# mutate(across(c(sexo, apetito, per_peso, movi, enf_ag, neuro, imc_cat, vive_ind, polim, ulceras, #comidas, lacteos, frutas, agua, forma, bien_nutrido, cir_panto, estado_nutricional_crib_eval, alta_obito, #clasi_dina, estado_nutricional_crib_eval),as.factor))#puntaje según estado nutricional de las variables
library(dplyr)
library(tidyr)
datos$estado_nutricional_crib_eval<-as.factor(datos$estado_nutricional_crib_eval)
library(purrr)
# Variables a analizar
variables_1 <- c(
"per_peso", "movi", "enf_ag", "apetito", "neuro",
"imc_cat", "vive_ind", "polim", "ulceras", "comidas",
"lacteos", "frutas", "agua", "forma", "bien_nutrido",
"estado_salud", "cir_braquial", "cir_panto"
)
# ---------------------------------------------------------
# 1. Mediana y RIC para cada grupo nutricional
# ---------------------------------------------------------
tabla_mediana <- Base_de_datos_ampliada %>%
dplyr::select(
estado_nutricional_crib_eval,
dplyr::all_of(variables_1)
) %>%
tidyr::pivot_longer(
cols = dplyr::all_of(variables_1),
names_to = "Variable",
values_to = "valor"
) %>%
dplyr::group_by(Variable, estado_nutricional_crib_eval) %>%
dplyr::summarise(
mediana = median(valor, na.rm = TRUE),
p25 = quantile(valor, 0.25, na.rm = TRUE),
p75 = quantile(valor, 0.75, na.rm = TRUE),
.groups = "drop"
) %>%
dplyr::mutate(
mediana_RIC = sprintf(
"%.1f [%.1f–%.1f]",
mediana, p25, p75
)
) %>%
dplyr::select(
Variable,
estado_nutricional_crib_eval,
mediana_RIC
) %>%
tidyr::pivot_wider(
names_from = estado_nutricional_crib_eval,
values_from = mediana_RIC
)
View(tabla_mediana)
knitr::kable(
tabla_mediana,
digits = 2,
caption = "Mediana de puntajes"
)| Variable | MALNUTRICION | NORMAL | RIESGO |
|---|---|---|---|
| agua | 0.5 [0.4–1.0] | 1.0 [0.5–1.0] | 1.0 [0.5–1.0] |
| apetito | 0.0 [0.0–0.0] | 2.0 [2.0–2.0] | 1.0 [0.0–1.2] |
| bien_nutrido | 1.0 [0.0–1.0] | 2.0 [1.0–2.0] | 1.5 [1.0–2.0] |
| cir_braquial | 1.0 [0.4–1.0] | 1.0 [1.0–1.0] | 1.0 [1.0–1.0] |
| cir_panto | 0.0 [0.0–1.0] | 1.0 [1.0–1.0] | 1.0 [1.0–1.0] |
| comidas | 2.0 [0.0–2.0] | 2.0 [2.0–2.0] | 2.0 [2.0–2.0] |
| enf_ag | 0.0 [0.0–2.0] | 2.0 [2.0–2.0] | 2.0 [0.0–2.0] |
| estado_salud | 0.5 [0.0–1.0] | 2.0 [1.0–2.0] | 1.0 [0.5–2.0] |
| forma | 2.0 [0.8–2.0] | 2.0 [2.0–2.0] | 2.0 [2.0–2.0] |
| frutas | 0.0 [0.0–1.0] | 1.0 [0.0–1.0] | 1.0 [0.0–1.0] |
| imc_cat | 1.5 [0.0–3.0] | 3.0 [3.0–3.0] | 3.0 [3.0–3.0] |
| lacteos | 0.5 [0.0–0.5] | 0.5 [0.5–1.0] | 0.5 [0.0–1.0] |
| movi | 0.0 [0.0–1.0] | 2.0 [2.0–2.0] | 1.5 [1.0–2.0] |
| neuro | 2.0 [1.8–2.0] | 2.0 [2.0–2.0] | 2.0 [2.0–2.0] |
| per_peso | 0.0 [0.0–0.0] | 3.0 [2.0–3.0] | 0.0 [0.0–2.0] |
| polim | 0.0 [0.0–0.0] | 1.0 [0.0–1.0] | 0.5 [0.0–1.0] |
| ulceras | 1.0 [0.0–1.0] | 1.0 [1.0–1.0] | 1.0 [1.0–1.0] |
| vive_ind | 0.0 [0.0–0.2] | 1.0 [1.0–1.0] | 1.0 [0.0–1.0] |
# ---------------------------------------------------------
# 2. Kruskal-Wallis para cada variable
# ---------------------------------------------------------
resultados_kw <- purrr::map_dfr(
variables_1,
function(v) {
datos_temp <- datos %>%
dplyr::select(
estado_nutricional_crib_eval,
dplyr::all_of(v)
) %>%
dplyr::filter(
!is.na(estado_nutricional_crib_eval),
!is.na(.data[[v]])
)
prueba <- kruskal.test(
datos_temp[[v]],
g = datos_temp$estado_nutricional_crib_eval
)
tibble(
Variable = v,
p_KW = prueba$p.value
)
}
)
# ---------------------------------------------------------
# 3. Corrección de Holm
# ---------------------------------------------------------
resultados_kw <- resultados_kw %>%
mutate(
p_Holm = p.adjust(p_KW, method = "holm")
)
# ---------------------------------------------------------
# 4. Unir resultados
# ---------------------------------------------------------
tabla_final <- tabla_mediana %>%
dplyr::left_join(
resultados_kw,
by = "Variable"
) %>%
dplyr::mutate(
p_KW = sprintf("%.4f", p_KW),
p_Holm = sprintf("%.4f", p_Holm)
) %>%
dplyr::select(
Variable,
MALNUTRICION,
NORMAL,
RIESGO,
p_KW,
p_Holm
)
View(tabla_final)library(dplyr)
library(tidyr)
library(ggplot2)
datos_grafico <- datos %>%
dplyr::select(
estado_nutricional_crib_eval,
dplyr::all_of(variables_1)
) %>%
tidyr::pivot_longer(
cols = dplyr::all_of(variables_1),
names_to = "Variable",
values_to = "Valor"
)
datos_grafico$estado_nutricional_crib_eval <- factor(
datos_grafico$estado_nutricional_crib_eval,
levels = c("MALNUTRICION", "RIESGO", "NORMAL")
)
levels(factor(Base_de_datos_ampliada$estado_nutricional_crib_eval))## [1] "MALNUTRICION" "NORMAL" "RIESGO"ggplot(
datos_grafico,
aes(
x = estado_nutricional_crib_eval,
y = Valor,
fill = estado_nutricional_crib_eval
)
) +
geom_boxplot(
width = 0.65,
alpha = 0.75,
outlier.alpha = 0.4
) +
facet_wrap(
~ Variable,
scales = "free_y",
ncol = 3
) +
labs(
title = "Variables según estado nutricional",
subtitle = "Distribución según la clasificación nutricional",
x = NULL,
y = NULL
) +
theme_classic(base_size = 12) +
theme(
legend.position = "none",
axis.text.x = element_text(
angle = 45,
hjust = 1
),
strip.text = element_text(
face = "bold"
),
plot.title = element_text(
face = "bold"
)
)
table(Base_de_datos_ampliada$estado_nutricional_crib_eval, useNA = "ifany")##
## MALNUTRICION NORMAL RIESGO
## 24 37 40#GRÁFICOS POR SEPARADO
grupo1 <- c(
"agua",
"apetito",
"bien_nutrido",
"cir_braquial",
"cir_panto",
"comidas"
)
grupo2 <- c(
"enf_ag",
"estado_salud",
"forma",
"frutas",
"imc_cat",
"lacteos"
)
grupo3 <- c(
"movi",
"neuro",
"per_peso",
"polim",
"ulceras",
"vive_ind"
)
#GRAFICO 1
grafico1 <- datos_grafico %>%
filter(Variable %in% grupo1) %>%
mutate(
Variable = factor(Variable, levels = grupo1)
) %>%
ggplot(
aes(
x = estado_nutricional_crib_eval,
y = Valor,
fill = estado_nutricional_crib_eval
)
) +
geom_boxplot(
width = 0.65,
alpha = 0.75
) +
facet_wrap(
~ Variable,
scales = "free_y",
ncol = 3
) +
labs(
title = "Variables según estado nutricional",
subtitle = "Distribución según la clasificación nutricional",
x = NULL,
y = NULL
) +
theme_classic(base_size = 13) +
theme(
legend.position = "none",
axis.text.x = element_text(
angle = 40,
hjust = 1,
size = 10
),
strip.text = element_text(
face = "bold",
size = 11
),
plot.title = element_text(
face = "bold",
size = 16
)
)
grafico1
#GRAFICO 2
grafico2 <- datos_grafico %>%
filter(Variable %in% grupo2) %>%
mutate(
Variable = factor(Variable, levels = grupo2)
) %>%
ggplot(
aes(
x = estado_nutricional_crib_eval,
y = Valor,
fill = estado_nutricional_crib_eval
)
) +
geom_boxplot(
width = 0.65,
alpha = 0.75
) +
facet_wrap(
~ Variable,
scales = "free_y",
ncol = 3
) +
labs(
title = "Variables según estado nutricional",
subtitle = "Distribución según la clasificación nutricional",
x = NULL,
y = NULL
) +
theme_classic(base_size = 13) +
theme(
legend.position = "none",
axis.text.x = element_text(
angle = 40,
hjust = 1,
size = 10
),
strip.text = element_text(
face = "bold",
size = 11
),
plot.title = element_text(
face = "bold",
size = 16
)
)
grafico2
#GRAFICO 3
grafico3 <- datos_grafico %>%
filter(Variable %in% grupo3) %>%
mutate(
Variable = factor(Variable, levels = grupo3)
) %>%
ggplot(
aes(
x = estado_nutricional_crib_eval,
y = Valor,
fill = estado_nutricional_crib_eval
)
) +
geom_boxplot(
width = 0.65,
alpha = 0.75
) +
facet_wrap(
~ Variable,
scales = "free_y",
ncol = 3
) +
labs(
title = "Variables según estado nutricional",
subtitle = "Distribución según la clasificación nutricional",
x = NULL,
y = NULL
) +
theme_classic(base_size = 13) +
theme(
legend.position = "none",
axis.text.x = element_text(
angle = 40,
hjust = 1,
size = 10
),
strip.text = element_text(
face = "bold",
size = 11
),
plot.title = element_text(
face = "bold",
size = 16
)
)
grafico3
#correlación
cor.test(
datos$punt_panto,
datos$dinamom_kg,
method = "spearman",
use = "complete.obs",
exact = FALSE
)##
## Spearman's rank correlation rho
##
## data: datos$punt_panto and datos$dinamom_kg
## S = 104891, p-value = 0.00006
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.389cor.test(
datos$punt_panto,
datos$dinamom_kg,
method = "pearson",
use = "complete.obs"
)##
## Pearson's product-moment correlation
##
## data: datos$punt_panto and datos$dinamom_kg
## t = 4, df = 99, p-value = 0.00008
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.202 0.538
## sample estimates:
## cor
## 0.383library(dplyr)
library(ggplot2)
datos_cor <- datos %>%
dplyr::select(punt_panto, dinamom_kg) %>%
dplyr::filter(
!is.na(punt_panto),
!is.na(dinamom_kg)
)
cor_spearman <- cor.test(
datos_cor$punt_panto,
datos_cor$dinamom_kg,
method = "spearman",
exact = FALSE
)
rho <- cor_spearman$estimate
p <- cor_spearman$p.value
etiqueta <- paste0(
"\u03c1 = ", round(rho, 2),
"\n",
"p ", ifelse(p < 0.001, "< 0.001", paste0("= ", round(p, 3)))
)
#corrige por edad
library(ppcor)## Warning: package 'ppcor' was built under R version 4.4.3datos_parcial <- datos %>%
dplyr::select(
punt_panto,
dinamom_kg,
edad
) %>%
tidyr::drop_na()
ppcor::pcor.test(
x = datos_parcial$punt_panto,
y = datos_parcial$dinamom_kg,
z = datos_parcial$edad,
method = "spearman"
)## estimate p.value statistic n gp Method
## 1 0.354 0.000303 3.75 101 1 spearmanggplot(datos_cor, aes(x = punt_panto, y = dinamom_kg)) +
geom_point(size = 3, alpha = 0.7) +
annotate(
"text",
x = Inf,
y = Inf,
label = etiqueta,
hjust = 1.2,
vjust = 1.5,
size = 5
) +
labs(
x = "Puntaje de pantorrilla",
y = "Dinamometría (kg)",
title = "Correlación entre puntaje de pantorrilla y dinamometría"
) +
theme_classic(base_size = 14) +
theme(
plot.title = element_text(face = "bold", hjust = 0.5),
axis.title = element_text(face = "bold")
)
#ajustando por la edad
library(lmtest)## Warning: package 'lmtest' was built under R version 4.4.3## Cargando paquete requerido: zoo##
## Adjuntando el paquete: 'zoo'## The following objects are masked from 'package:data.table':
##
## yearmon, yearqtr## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Adjuntando el paquete: 'lmtest'## The following object is masked from 'package:rms':
##
## lrtestmod0<-lm(dinamom_kg ~ punt_panto , data=datos)
summary(mod0)##
## Call:
## lm(formula = dinamom_kg ~ punt_panto, data = datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.24 -6.97 -1.24 6.87 25.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.037 6.289 -0.64 0.52
## punt_panto 0.757 0.184 4.12 0.000079 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.6 on 99 degrees of freedom
## Multiple R-squared: 0.146, Adjusted R-squared: 0.138
## F-statistic: 17 on 1 and 99 DF, p-value: 0.0000789confint(mod0)## 2.5 % 97.5 %
## (Intercept) -16.516 8.44
## punt_panto 0.392 1.12mod1<-lm(dinamom_kg ~ punt_panto + edad, data=datos)
summary(mod1)##
## Call:
## lm(formula = dinamom_kg ~ punt_panto + edad, data = datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.56 -6.89 -1.40 6.20 24.95
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.150 13.064 1.16 0.24901
## punt_panto 0.665 0.190 3.49 0.00072 ***
## edad -0.236 0.141 -1.67 0.09788 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.51 on 98 degrees of freedom
## Multiple R-squared: 0.17, Adjusted R-squared: 0.153
## F-statistic: 10 on 2 and 98 DF, p-value: 0.000108confint(mod1)## 2.5 % 97.5 %
## (Intercept) -10.776 41.0757
## punt_panto 0.287 1.0422
## edad -0.517 0.0443anova(mod0,mod1)## Analysis of Variance Table
##
## Model 1: dinamom_kg ~ punt_panto
## Model 2: dinamom_kg ~ punt_panto + edad
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 99 9118
## 2 98 8865 1 253 2.79 0.098 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(mod1)| dinamom_kg | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 15.15 | -10.78 – 41.08 | 0.249 |
| punt panto | 0.66 | 0.29 – 1.04 | 0.001 |
| edad | -0.24 | -0.52 – 0.04 | 0.098 |
| Observations | 101 | ||
| R2 / R2 adjusted | 0.170 / 0.153 | ||
#supuestos del modelo
#homocedasticidad no se cumple
#linealidad no se cumple
#
predichos <- fitted.values(mod0)
standres <-rstandard(mod0)
res <- residuals(mod0)
plot(y=datos$punt_panto, x= datos$dinamom_kg)
abline(h=0, lty = 2)
#homocesdasticidad no se cumple
plot(
x = mod0$fitted.values,
y = mod0$residuals,
xlab = "Valores predichos",
ylab = "Residuos",
main = "Residuos vs valores predichos"
)
abline(h = 0, lty = 2)
bptest(datos$punt_panto ~ datos$dinamom_kg, data=datos)##
## studentized Breusch-Pagan test
##
## data: datos$punt_panto ~ datos$dinamom_kg
## BP = 5, df = 1, p-value = 0.03#normalidad se cumple
library(car)## Cargando paquete requerido: carData##
## Adjuntando el paquete: 'carData'## The following object is masked from 'package:vcdExtra':
##
## Burt##
## Adjuntando el paquete: 'car'## The following object is masked from 'package:psych':
##
## logit## The following objects are masked from 'package:rms':
##
## Predict, vif## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## somedensityPlot(mod0$residuals)
hist(mod1$residuals)
boxplot(mod0$residuals)
qqPlot(standres) #se utilizan residuos estandarizados
## [1] 38 58describe(mod0$residuals)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 101 0 9.55 -1.24 -0.41 9.69 -22.2 25.3 47.5 0.33 -0.35 0.95shapiro.test(mod0$residuals)##
## Shapiro-Wilk normality test
##
## data: mod0$residuals
## W = 1, p-value = 0.3#spearman controlando por la edad
install.packages("ppcor") # solo la primera vez## Warning: package 'ppcor' is in use and will not be installedlibrary(ppcor)
datos_parcial <- datos %>%
dplyr::select(punt_panto, dinamom_kg, edad) %>%
tidyr::drop_na()
ppcor::pcor.test(
x = datos_parcial$punt_panto,
y = datos_parcial$dinamom_kg,
z = datos_parcial$edad,
method = "spearman"
)## estimate p.value statistic n gp Method
## 1 0.354 0.000303 3.75 101 1 spearman#tabla 1
#transformar en factor
Base_de_datos_ampliada$estado_nutricional_crib_eval <- factor(
Base_de_datos_ampliada$estado_nutricional_crib_eval,
levels = c("MALNUTRICION", "RIESGO", "NORMAL")
)
psych::describeBy(
imc ~ estado_nutricional_crib_eval,
data = Base_de_datos_ampliada
)##
## Descriptive statistics by group
## estado_nutricional_crib_eval: MALNUTRICION
## vars n mean sd median trimmed mad min max range skew kurtosis se
## imc 1 24 22.5 7.14 21.2 21.3 5.62 15.6 47.5 31.8 1.86 3.76 1.46
## ------------------------------------------------------------
## estado_nutricional_crib_eval: RIESGO
## vars n mean sd median trimmed mad min max range skew kurtosis se
## imc 1 40 29 7.46 27.5 27.9 4.73 19.4 64.9 45.5 2.79 10.9 1.18
## ------------------------------------------------------------
## estado_nutricional_crib_eval: NORMAL
## vars n mean sd median trimmed mad min max range skew kurtosis se
## imc 1 37 28.3 4.44 27.5 28.1 4.45 19.5 38.1 18.5 0.36 -0.53 0.73Base_de_datos_ampliada <- Base_de_datos_ampliada %>%
mutate(across(c(sexo,imc_cat, cir_panto, estado_nutricional_crib_eval, alta_obito, clasi_dina, cir_panto),as.factor))
catvars = c( "sexo","imc_cat", "alta_obito", "clasi_dina", "cir_panto")
vars = c("sexo", "cir_panto", "imc_cat", "alta_obito", "clasi_dina","edad","imc","punt_panto", "punt_braquial")
tabla_1 <- CreateTableOne(
vars = vars,
strata = "estado_nutricional_crib_eval",
factorVars = catvars,
data = Base_de_datos_ampliada,
addOverall = TRUE
)
print(tabla_1, nonnormal = c("punt_panto", "punt_braquial", "imc"))## Stratified by estado_nutricional_crib_eval
## Overall MALNUTRICION
## n 101 24
## sexo = 2 (%) 66 (65.3) 15 (62.5)
## cir_panto = 1 (%) 78 (77.2) 11 (45.8)
## imc_cat (%)
## 0 10 ( 9.9) 10 (41.7)
## 1 5 ( 5.0) 2 ( 8.3)
## 2 8 ( 7.9) 3 (12.5)
## 3 78 (77.2) 9 (37.5)
## alta_obito = 1 (%) 10 ( 9.9) 5 (20.8)
## clasi_dina = NORMAL (%) 43 (42.6) 3 (12.5)
## edad (mean (SD)) 68.02 (7.03) 71.04 (9.05)
## imc (median [IQR]) 26.43 [23.45, 30.86] 21.24 [17.51, 24.67]
## punt_panto (median [IQR]) 34.00 [31.00, 38.00] 29.50 [26.38, 31.62]
## punt_braquial (median [IQR]) 29.00 [25.00, 31.00] 23.75 [20.88, 25.88]
## Stratified by estado_nutricional_crib_eval
## RIESGO NORMAL p
## n 40 37
## sexo = 2 (%) 25 (62.5) 26 (70.3) 0.732
## cir_panto = 1 (%) 32 (80.0) 35 (94.6) <0.001
## imc_cat (%) <0.001
## 0 0 ( 0.0) 0 ( 0.0)
## 1 2 ( 5.0) 1 ( 2.7)
## 2 3 ( 7.5) 2 ( 5.4)
## 3 35 (87.5) 34 (91.9)
## alta_obito = 1 (%) 5 (12.5) 0 ( 0.0) 0.023
## clasi_dina = NORMAL (%) 17 (42.5) 23 (62.2) 0.001
## edad (mean (SD)) 68.20 (6.36) 65.86 (5.50) 0.017
## imc (median [IQR]) 27.52 [24.63, 31.12] 27.48 [24.97, 31.19] <0.001
## punt_panto (median [IQR]) 34.00 [32.40, 36.88] 36.00 [34.00, 38.50] <0.001
## punt_braquial (median [IQR]) 29.00 [26.75, 30.12] 30.00 [27.00, 32.90] <0.001
## Stratified by estado_nutricional_crib_eval
## test
## n
## sexo = 2 (%)
## cir_panto = 1 (%)
## imc_cat (%)
## 0
## 1
## 2
## 3
## alta_obito = 1 (%)
## clasi_dina = NORMAL (%)
## edad (mean (SD))
## imc (median [IQR]) nonnorm
## punt_panto (median [IQR]) nonnorm
## punt_braquial (median [IQR]) nonnormkableone(tabla_1, nonnormal = c("punt_panto", "punt_braquial", "imc")) #se visualiza mejor| Overall | MALNUTRICION | RIESGO | NORMAL | p | test | |
|---|---|---|---|---|---|---|
| n | 101 | 24 | 40 | 37 | ||
| sexo = 2 (%) | 66 (65.3) | 15 (62.5) | 25 (62.5) | 26 (70.3) | 0.732 | |
| cir_panto = 1 (%) | 78 (77.2) | 11 (45.8) | 32 (80.0) | 35 (94.6) | <0.001 | |
| imc_cat (%) | <0.001 | |||||
| 0 | 10 ( 9.9) | 10 (41.7) | 0 ( 0.0) | 0 ( 0.0) | ||
| 1 | 5 ( 5.0) | 2 ( 8.3) | 2 ( 5.0) | 1 ( 2.7) | ||
| 2 | 8 ( 7.9) | 3 (12.5) | 3 ( 7.5) | 2 ( 5.4) | ||
| 3 | 78 (77.2) | 9 (37.5) | 35 (87.5) | 34 (91.9) | ||
| alta_obito = 1 (%) | 10 ( 9.9) | 5 (20.8) | 5 (12.5) | 0 ( 0.0) | 0.023 | |
| clasi_dina = NORMAL (%) | 43 (42.6) | 3 (12.5) | 17 (42.5) | 23 (62.2) | 0.001 | |
| edad (mean (SD)) | 68.02 (7.03) | 71.04 (9.05) | 68.20 (6.36) | 65.86 (5.50) | 0.017 | |
| imc (median [IQR]) | 26.43 [23.45, 30.86] | 21.24 [17.51, 24.67] | 27.52 [24.63, 31.12] | 27.48 [24.97, 31.19] | <0.001 | nonnorm |
| punt_panto (median [IQR]) | 34.00 [31.00, 38.00] | 29.50 [26.38, 31.62] | 34.00 [32.40, 36.88] | 36.00 [34.00, 38.50] | <0.001 | nonnorm |
| punt_braquial (median [IQR]) | 29.00 [25.00, 31.00] | 23.75 [20.88, 25.88] | 29.00 [26.75, 30.12] | 30.00 [27.00, 32.90] | <0.001 | nonnorm |
pairwise.wilcox.test(
x = datos$imc,
g = datos$estado_nutricional_crib_eval,
p.adjust.method = "bonferroni",
exact = FALSE
)##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: datos$imc and datos$estado_nutricional_crib_eval
##
## MALNUTRICION NORMAL
## NORMAL 0.00007 -
## RIESGO 0.00008 1
##
## P value adjustment method: bonferroni#análisis descriptivo
#total de pacientes 101
table_s<-table(Base_de_datos_ampliada$sexo)
table_s##
## 1 2
## 35 66prop.table(table_s)##
## 1 2
## 0.347 0.653prop.test(
x = 65.3,
n = 101,
conf.level = 0.95,
correct = FALSE
)##
## 1-sample proportions test without continuity correction
##
## data: 65.3 out of 101, null probability 0.5
## X-squared = 9, df = 1, p-value = 0.003
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
## 0.550 0.733
## sample estimates:
## p
## 0.647describe(Base_de_datos_ampliada$edad)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 101 68 7.03 67 67.1 5.93 60 85 25 0.9 -0.11 0.7describe(Base_de_datos_ampliada$dias_int)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 101 16.2 16.2 10 13 5.93 2 91 89 2.39 6.82 1.61median(Base_de_datos_ampliada$dias_int, na.rm = TRUE)## [1] 10quantile(
Base_de_datos_ampliada$dias_int,
probs = c(0.25, 0.75),
na.rm = TRUE
)## 25% 75%
## 7 18table_2 <- table(datos$estado_nutricional_crib_eval)
prop.table(table_2)##
## MALNUTRICION NORMAL RIESGO
## 0.238 0.366 0.396kableone(table_2)| Var1 | Freq |
|---|---|
| MALNUTRICION | 24 |
| NORMAL | 37 |
| RIESGO | 40 |
datos$estado_nutricional_crib_eval <- factor(
datos$estado_nutricional_crib_eval,
levels = c("MALNUTRICION", "RIESGO", "NORMAL")
)
library(dplyr)
library(binom)## Warning: package 'binom' was built under R version 4.4.3tabla_2 <- datos %>%
dplyr::filter(!is.na(estado_nutricional_crib_eval)) %>%
dplyr::count(estado_nutricional_crib_eval, name = "n") %>%
dplyr::mutate(
total = sum(n),
frecuencia = n / total,
# IC95% de Wilson
IC_inf = binom::binom.confint(
n, total,
methods = "wilson"
)$lower,
IC_sup = binom::binom.confint(
n, total,
methods = "wilson"
)$upper
) %>%
dplyr::mutate(
`Frecuencia (%)` = sprintf("%.1f", frecuencia * 100),
`IC95%` = sprintf(
"%.1f–%.1f",
IC_inf * 100,
IC_sup * 100
)
) %>%
dplyr::select(
Estado_nutricional = estado_nutricional_crib_eval,
n,
`Frecuencia (%)`,
`IC95%`
)
tabla_2## # A tibble: 3 × 4
## Estado_nutricional n `Frecuencia (%)` `IC95%`
## <fct> <int> <chr> <chr>
## 1 MALNUTRICION 24 23.8 16.5–32.9
## 2 RIESGO 40 39.6 30.6–49.4
## 3 NORMAL 37 36.6 27.9–46.4knitr::kable(tabla_2)| Estado_nutricional | n | Frecuencia (%) | IC95% |
|---|---|---|---|
| MALNUTRICION | 24 | 23.8 | 16.5–32.9 |
| RIESGO | 40 | 39.6 | 30.6–49.4 |
| NORMAL | 37 | 36.6 | 27.9–46.4 |
#análisis de días de internación
#creo subset de vivos
datos_vivos <- subset(datos, alta_obito == "0")
describe(datos_vivos$dias_int)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 91 16.2 16.6 9 12.9 5.93 2 91 89 2.41 6.83 1.73median(datos_vivos$dias_int, na.rm = TRUE)## [1] 9quantile(
datos_vivos$dias_int,
probs = c(0.25, 0.75),
na.rm = TRUE
)## 25% 75%
## 7 18describeBy(datos_vivos$dias_int, datos_vivos$estado_nutricional_crib_eval)##
## Descriptive statistics by group
## group: MALNUTRICION
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 19 19.7 16.1 13 18.5 8.9 5 55 50 0.92 -0.64 3.68
## ------------------------------------------------------------
## group: RIESGO
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 35 13.9 10.7 10 12.1 5.93 2 45 43 1.46 1.41 1.82
## ------------------------------------------------------------
## group: NORMAL
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 37 16.5 20.9 8 12.1 4.45 3 91 88 2.44 5.57 3.43tabla_dias <- datos_vivos %>%
dplyr::group_by(estado_nutricional_crib_eval) %>%
dplyr::summarise(
N = sum(!is.na(dias_int)),
Mediana_RIC = paste0(
median(dias_int, na.rm = TRUE),
" [",
quantile(dias_int, 0.25, na.rm = TRUE),
"–",
quantile(dias_int, 0.75, na.rm = TRUE),
"]"
)
)
View(tabla_dias)
knitr::kable(
tabla_dias,
digits = 2,
caption = "Tabla dias de internacion segun estado de nutricion"
)| estado_nutricional_crib_eval | N | Mediana_RIC |
|---|---|---|
| MALNUTRICION | 19 | 13 [8–33] |
| RIESGO | 35 | 10 [7–17.5] |
| NORMAL | 37 | 8 [6–17] |
#dias de internación según estado nutricional al ingreso: kruskal
kruskal.test(datos_vivos$dias_int, datos_vivos$estado_nutricional_crib_eval)##
## Kruskal-Wallis rank sum test
##
## data: datos_vivos$dias_int and datos_vivos$estado_nutricional_crib_eval
## Kruskal-Wallis chi-squared = 4, df = 2, p-value = 0.2pairwise.wilcox.test(
x = datos_vivos$dias_int,
g = datos_vivos$estado_nutricional_crib_eval,
p.adjust.method = "bonferroni",
exact = FALSE
)##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: datos_vivos$dias_int and datos_vivos$estado_nutricional_crib_eval
##
## MALNUTRICION RIESGO
## RIESGO 0.7 -
## NORMAL 0.2 1.0
##
## P value adjustment method: bonferroni#regresion lineal dias en funcion de estado nutricional ajustando por edad
#ajustando por la edad
mod_dias<-lm(dias_int ~ estado_nutricional_crib_eval , data=datos_vivos)
summary(mod_dias)##
## Call:
## lm(formula = dias_int ~ estado_nutricional_crib_eval, data = datos_vivos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.74 -10.19 -5.86 3.31 74.49
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.74 3.81 5.19 0.0000014 ***
## estado_nutricional_crib_evalRIESGO -5.88 4.73 -1.24 0.22
## estado_nutricional_crib_evalNORMAL -3.22 4.68 -0.69 0.49
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.6 on 88 degrees of freedom
## Multiple R-squared: 0.0176, Adjusted R-squared: -0.00475
## F-statistic: 0.787 on 2 and 88 DF, p-value: 0.458confint(mod_dias)## 2.5 % 97.5 %
## (Intercept) 12.2 27.30
## estado_nutricional_crib_evalRIESGO -15.3 3.51
## estado_nutricional_crib_evalNORMAL -12.5 6.08tab_model(mod_dias)| dias_int | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 19.74 | 12.17 – 27.30 | <0.001 |
|
estado nutricional crib eval [RIESGO] |
-5.88 | -15.27 – 3.51 | 0.217 |
|
estado nutricional crib eval [NORMAL] |
-3.22 | -12.53 – 6.08 | 0.493 |
| Observations | 91 | ||
| R2 / R2 adjusted | 0.018 / -0.005 | ||
#ajustando por edad
mod_dias_e<-lm(dias_int ~ estado_nutricional_crib_eval + edad , data=datos_vivos)
summary(mod_dias_e)##
## Call:
## lm(formula = dias_int ~ estado_nutricional_crib_eval + edad,
## data = datos_vivos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.33 -9.57 -5.35 2.76 74.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.727 19.907 2.30 0.024 *
## estado_nutricional_crib_evalRIESGO -7.635 4.888 -1.56 0.122
## estado_nutricional_crib_evalNORMAL -5.730 5.028 -1.14 0.258
## edad -0.357 0.268 -1.33 0.187
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.5 on 87 degrees of freedom
## Multiple R-squared: 0.0372, Adjusted R-squared: 0.00395
## F-statistic: 1.12 on 3 and 87 DF, p-value: 0.346confint(mod_dias_e)## 2.5 % 97.5 %
## (Intercept) 6.159 85.295
## estado_nutricional_crib_evalRIESGO -17.351 2.081
## estado_nutricional_crib_evalNORMAL -15.724 4.264
## edad -0.889 0.176tab_model(mod_dias_e)| dias_int | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 45.73 | 6.16 – 85.29 | 0.024 |
|
estado nutricional crib eval [RIESGO] |
-7.64 | -17.35 – 2.08 | 0.122 |
|
estado nutricional crib eval [NORMAL] |
-5.73 | -15.72 – 4.26 | 0.258 |
| edad | -0.36 | -0.89 – 0.18 | 0.187 |
| Observations | 91 | ||
| R2 / R2 adjusted | 0.037 / 0.004 | ||
anova(mod_dias, mod_dias_e)## Analysis of Variance Table
##
## Model 1: dias_int ~ estado_nutricional_crib_eval
## Model 2: dias_int ~ estado_nutricional_crib_eval + edad
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 88 24215
## 2 87 23733 1 482 1.77 0.19##Dentro de MALNUTRICIÓN, RIESGO y NORMAL, ¿qué ítems aportaron mayor puntaje a la sumatoria?
#evaluar qué variable contribuye más al puntaje. Correlación item total corregida
maximos <- c(
per_peso = 3,
movi = 2,
enf_ag = 2,
apetito = 2,
neuro = 2,
imc_cat = 3,
vive_ind = 1,
polim = 1,
ulceras = 1,
comidas = 2,
lacteos = 1,
frutas = 1,
agua = 1,
forma = 2,
bien_nutrido = 2,
estado_salud = 2,
cir_braquial = 1,
cir_panto = 1
)
library(dplyr)
library(tidyr)
tabla_aporte <- datos %>%
dplyr::select(
estado_nutricional_crib_eval,
dplyr::all_of(variables_1)
) %>%
tidyr::pivot_longer(
cols = dplyr::all_of(variables_1),
names_to = "Variable",
values_to = "Puntaje"
) %>%
dplyr::group_by(
estado_nutricional_crib_eval,
Variable
) %>%
dplyr::summarise(
N = sum(!is.na(Puntaje)),
Media = mean(Puntaje, na.rm = TRUE),
Mediana = median(Puntaje, na.rm = TRUE),
.groups = "drop"
) %>%
dplyr::mutate(
Maximo = maximos[Variable],
Aporte_porcentaje = Media / Maximo * 100
) %>%
dplyr::arrange(
estado_nutricional_crib_eval,
dplyr::desc(Aporte_porcentaje)
)
View(tabla_aporte)
#malnutricion
tabla_malnutricion <- tabla_aporte %>%
filter(estado_nutricional_crib_eval == "MALNUTRICION") %>%
arrange(desc(Aporte_porcentaje))
View(tabla_malnutricion)
#riesgo
tabla_riesgo <- tabla_aporte %>%
filter(estado_nutricional_crib_eval == "RIESGO") %>%
arrange(desc(Aporte_porcentaje))
View(tabla_riesgo)
#normal
tabla_normal <- tabla_aporte %>%
filter(estado_nutricional_crib_eval == "NORMAL") %>%
arrange(desc(Aporte_porcentaje))
View(tabla_normal)
knitr::kable(
tabla_malnutricion,
digits = 2,
caption = "Aporte relativo de los componentes en pacientes con malnutrición"
)| estado_nutricional_crib_eval | Variable | N | Media | Mediana | Maximo | Aporte_porcentaje |
|---|---|---|---|---|---|---|
| MALNUTRICION | neuro | 24 | 1.58 | 2.0 | 2 | 79.17 |
| MALNUTRICION | cir_braquial | 24 | 0.73 | 1.0 | 1 | 72.92 |
| MALNUTRICION | forma | 24 | 1.33 | 2.0 | 2 | 66.67 |
| MALNUTRICION | comidas | 24 | 1.17 | 2.0 | 2 | 58.33 |
| MALNUTRICION | ulceras | 24 | 0.54 | 1.0 | 1 | 54.17 |
| MALNUTRICION | agua | 24 | 0.52 | 0.5 | 1 | 52.08 |
| MALNUTRICION | imc_cat | 24 | 1.46 | 1.5 | 3 | 48.61 |
| MALNUTRICION | bien_nutrido | 24 | 0.92 | 1.0 | 2 | 45.83 |
| MALNUTRICION | cir_panto | 24 | 0.46 | 0.0 | 1 | 45.83 |
| MALNUTRICION | enf_ag | 24 | 0.83 | 0.0 | 2 | 41.67 |
| MALNUTRICION | lacteos | 24 | 0.38 | 0.5 | 1 | 37.50 |
| MALNUTRICION | frutas | 24 | 0.33 | 0.0 | 1 | 33.33 |
| MALNUTRICION | estado_salud | 24 | 0.56 | 0.5 | 2 | 28.12 |
| MALNUTRICION | movi | 24 | 0.50 | 0.0 | 2 | 25.00 |
| MALNUTRICION | vive_ind | 24 | 0.25 | 0.0 | 1 | 25.00 |
| MALNUTRICION | polim | 24 | 0.21 | 0.0 | 1 | 20.83 |
| MALNUTRICION | apetito | 24 | 0.33 | 0.0 | 2 | 16.67 |
| MALNUTRICION | per_peso | 24 | 0.21 | 0.0 | 3 | 6.94 |
knitr::kable(
tabla_riesgo,
digits = 2,
caption = "Aporte relativo de los componentes en pacientes en riesgo nutricional"
)| estado_nutricional_crib_eval | Variable | N | Media | Mediana | Maximo | Aporte_porcentaje |
|---|---|---|---|---|---|---|
| RIESGO | cir_braquial | 40 | 1.00 | 1.0 | 1 | 100.0 |
| RIESGO | forma | 40 | 1.93 | 2.0 | 2 | 96.2 |
| RIESGO | imc_cat | 40 | 2.83 | 3.0 | 3 | 94.2 |
| RIESGO | comidas | 40 | 1.75 | 2.0 | 2 | 87.5 |
| RIESGO | neuro | 40 | 1.70 | 2.0 | 2 | 85.0 |
| RIESGO | cir_panto | 40 | 0.80 | 1.0 | 1 | 80.0 |
| RIESGO | ulceras | 40 | 0.78 | 1.0 | 1 | 77.5 |
| RIESGO | agua | 40 | 0.74 | 1.0 | 1 | 73.8 |
| RIESGO | bien_nutrido | 40 | 1.35 | 1.5 | 2 | 67.5 |
| RIESGO | movi | 40 | 1.30 | 1.5 | 2 | 65.0 |
| RIESGO | estado_salud | 40 | 1.26 | 1.0 | 2 | 63.1 |
| RIESGO | vive_ind | 40 | 0.60 | 1.0 | 1 | 60.0 |
| RIESGO | lacteos | 40 | 0.58 | 0.5 | 1 | 57.5 |
| RIESGO | enf_ag | 40 | 1.10 | 2.0 | 2 | 55.0 |
| RIESGO | frutas | 40 | 0.55 | 1.0 | 1 | 55.0 |
| RIESGO | polim | 40 | 0.50 | 0.5 | 1 | 50.0 |
| RIESGO | apetito | 40 | 0.92 | 1.0 | 2 | 46.2 |
| RIESGO | per_peso | 40 | 0.90 | 0.0 | 3 | 30.0 |
knitr::kable(
tabla_normal,
digits = 2,
caption = "Aporte relativo de los componentes en pacientes con estado nutricional normal"
)| estado_nutricional_crib_eval | Variable | N | Media | Mediana | Maximo | Aporte_porcentaje |
|---|---|---|---|---|---|---|
| NORMAL | forma | 37 | 1.97 | 2.0 | 2 | 98.7 |
| NORMAL | cir_braquial | 37 | 0.97 | 1.0 | 1 | 97.3 |
| NORMAL | neuro | 37 | 1.95 | 2.0 | 2 | 97.3 |
| NORMAL | imc_cat | 37 | 2.89 | 3.0 | 3 | 96.4 |
| NORMAL | cir_panto | 37 | 0.95 | 1.0 | 1 | 94.6 |
| NORMAL | movi | 37 | 1.84 | 2.0 | 2 | 91.9 |
| NORMAL | apetito | 37 | 1.81 | 2.0 | 2 | 90.5 |
| NORMAL | comidas | 37 | 1.81 | 2.0 | 2 | 90.5 |
| NORMAL | agua | 37 | 0.84 | 1.0 | 1 | 83.8 |
| NORMAL | enf_ag | 37 | 1.68 | 2.0 | 2 | 83.8 |
| NORMAL | per_peso | 37 | 2.51 | 3.0 | 3 | 83.8 |
| NORMAL | bien_nutrido | 37 | 1.62 | 2.0 | 2 | 81.1 |
| NORMAL | ulceras | 37 | 0.78 | 1.0 | 1 | 78.4 |
| NORMAL | estado_salud | 37 | 1.53 | 2.0 | 2 | 76.3 |
| NORMAL | vive_ind | 37 | 0.76 | 1.0 | 1 | 75.7 |
| NORMAL | frutas | 37 | 0.68 | 1.0 | 1 | 67.6 |
| NORMAL | polim | 37 | 0.65 | 1.0 | 1 | 64.9 |
| NORMAL | lacteos | 37 | 0.64 | 0.5 | 1 | 63.5 |