AngelAnalisys
Loranda_Calderon
01-24-2025
#install.packages("conflicted")
#install.packages("dplyr")
#install.packages("FSA")library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(gtsummary)
library(gt)
library(readxl)
library(conflicted)
library(dplyr)
library(FSA)## ## FSA v0.9.6. See citation('FSA') if used in publication.
## ## Run fishR() for related website and fishR('IFAR') for related book.setwd(dir = "/Users/lorandacalderonzamora/Downloads/")
data <- read_excel("Copia_de_Base_de_datos_Angel-laura.xlsx")
knitr::opts_knit$set(root.dir = "/Users/lorandacalderonzamora/Downloads")
class(data)## [1] "tbl_df" "tbl" "data.frame"names(data)## [1] "Registro" "Nombre"
## [3] "GRUPO" "Sexo"
## [5] "Edad" "Peso"
## [7] "Talla" "IMC"
## [9] "Cintura" "Cadera"
## [11] "ICC" "Glucosa"
## [13] "HBA1c" "Colesterol_total"
## [15] "VLDL" "LDL"
## [17] "HDL" "Trigliceridos"
## [19] "Creatinina" "Urea"
## [21] "BUN" "TFGe"
## [23] "DISFUNCIÓN_RENAL" "RIESGO_CARDIO-VASCULAR"
## [25] "RIESGO_CV_Dx_Framingham" "Fuma"
## [27] "Consume_alcohol" "HTA"
## [29] "TX_HTA" "Diabetes_Mellitus"
## [31] "Dislipidemia" "Tratamiento_Lipemiante"
## [33] "IL-6" "ApoE"
## [35] "Riesgo_ApoE"data <- as.data.frame(data)
data <- data %>%
mutate(
Grupos_de_estudio = dplyr::recode(GRUPO, `0` = "Control", `1` = "DM2", `2` = "ND"),
Disfunción_renal = dplyr::recode(DISFUNCIÓN_RENAL, `0` = "Sin disfunción renal", `1` = "Con disfunción renal"),
Sexo = dplyr::recode(Sexo, `0` = "Hombre", `1` = "Mujer"),
Consumo_de_alcohol = dplyr::recode(Consume_alcohol, `0` = "No", `1` = "Sí"),
Dislipidemia = dplyr::recode(Dislipidemia, `0` = "No", `1` = "Sí"),
Hipertensión = dplyr::recode(HTA, `0` = "No", `1` = "Sí"),
Diabetes_mellitus = dplyr::recode(Diabetes_Mellitus, `0` = "No", `1` = "Sí"),
Tratamiento_antihipertensivo = dplyr::recode(TX_HTA, `0` = "No", `1` = "Sí"),
Tratamiento_antilipemiante = dplyr::recode(Tratamiento_Lipemiante, `0` = "No", `1` = "Sí"),
Fuma = dplyr::recode(Fuma, `0`= "No", `1` = "Sí"),
Edad = as.numeric(Edad) # Crear o convertir Age a numérico
)class(data)## [1] "data.frame"view(data)
str(data)## 'data.frame': 323 obs. of 42 variables:
## $ Registro : chr "MIDE001" "MIDE002" "MIDE006" "MIDE009" ...
## $ Nombre : chr "OSUNA GONZÁLEZ LAURA ALICIA" "CASTILLO GONZÁLEZ MARTHA ALICIA" "SÁNCHEZ MORALES JAVIER" "AYÓN ALVARADO JOSÉ LUIS" ...
## $ GRUPO : num 2 2 2 1 2 1 1 2 2 1 ...
## $ Sexo : chr "F" "F" "M" "M" ...
## $ Edad : num 65 57 68 62 62 59 66 68 71 76 ...
## $ Peso : num 94.2 84.5 82.3 88.8 65.4 89.7 75.4 98.2 54.7 55.2 ...
## $ Talla : num 1.59 1.62 1.6 1.67 1.68 1.67 1.52 1.63 1.59 1.63 ...
## $ IMC : num 37.3 32.2 32.1 31.8 23.2 ...
## $ Cintura : num 113 105 109 103 87 ...
## $ Cadera : num 123 115 107 100 94 ...
## $ ICC : num 0.919 0.913 1.019 1.025 0.926 ...
## $ Glucosa : num 118.5 188 94.8 115.1 134.4 ...
## $ HBA1c : num 9.1 7.9 5.7 7.4 5.8 9.5 8 7.9 6.5 8.1 ...
## $ Colesterol_total : num 158 129 912 237 220 ...
## $ VLDL : num 26.7 29.5 15.9 35.1 18 ...
## $ LDL : chr "94.23" "77.540000000000006" "863.52" "205.39" ...
## $ HDL : num 36.7 22.4 32.3 24.8 24.9 ...
## $ Trigliceridos : num 133.5 147.4 79.6 171.8 90.2 ...
## $ Creatinina : num 1.2 1.2 2.5 1.2 1.5 1 0.9 1.2 1.4 0.8 ...
## $ Urea : num 70.9 28.7 143.2 46.7 62.1 ...
## $ BUN : num 33.1 13.4 66.9 21.8 29 ...
## $ TFGe : num 47.4 50.1 25.4 64 49.2 61.6 66.6 46.4 37.7 86.8 ...
## $ DISFUNCIÓN_RENAL : num 1 1 1 0 1 0 0 1 1 0 ...
## $ RIESGO_CARDIO-VASCULAR : chr "21.5" "10" "30" "24.8" ...
## $ RIESGO_CV_Dx_Framingham : chr "ALTO" "MODERADO" "ALTO" "ALTO" ...
## $ Fuma : chr "No" "No" "No" "No" ...
## $ Consume_alcohol : num 0 0 0 0 0 0 1 1 1 0 ...
## $ HTA : num 1 1 1 0 1 1 1 1 1 1 ...
## $ TX_HTA : num 1 1 1 0 0 1 1 0 1 0 ...
## $ Diabetes_Mellitus : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Dislipidemia : chr "No" "No" "Sí" "No" ...
## $ Tratamiento_Lipemiante : num 0 0 0 0 0 0 0 0 0 0 ...
## $ IL-6 : chr "G/G" "G/G" "G/G" "G/C" ...
## $ ApoE : chr "E3/E3" "E3/E4" "E3/E3" "E3/E4" ...
## $ Riesgo_ApoE : num 0 2 0 2 3 0 0 2 0 0 ...
## $ Grupos_de_estudio : chr "ND" "ND" "ND" "DM2" ...
## $ Disfunción_renal : chr "Con disfunción renal" "Con disfunción renal" "Con disfunción renal" "Sin disfunción renal" ...
## $ Consumo_de_alcohol : chr "No" "No" "No" "No" ...
## $ Hipertensión : chr "Sí" "Sí" "Sí" "No" ...
## $ Diabetes_mellitus : chr "Sí" "Sí" "Sí" "Sí" ...
## $ Tratamiento_antihipertensivo: chr "Sí" "Sí" "Sí" "No" ...
## $ Tratamiento_antilipemiante : chr "No" "No" "No" "No" ...data <- as.data.frame(data)
selected_data <- data %>%
dplyr::select(
Grupos_de_estudio, Sexo, Edad, Disfunción_renal, Fuma, Consumo_de_alcohol, Hipertensión, Diabetes_mellitus, Dislipidemia, Tratamiento_antihipertensivo, Tratamiento_antilipemiante
)
summary_table <- selected_data %>%
tbl_summary(
by = Grupos_de_estudio,
missing = "no",
type = list(
Edad ~ "continuous",
Sexo ~ "categorical",
Disfunción_renal ~ "categorical",
Fuma ~ "categorical",
Consumo_de_alcohol ~ "categorical",
Hipertensión ~ "categorical",
Diabetes_mellitus ~ "categorical",
Dislipidemia ~ "categorical",
Tratamiento_antihipertensivo ~ "categorical",
Tratamiento_antilipemiante ~ "categorical"
),
statistic = list(
Edad ~ "{median} ({p25} - {p75})",
all_categorical() ~ "{n} ({p}%)"
)
) %>%
add_overall() %>%
add_p(
test = list(
Edad ~ "kruskal.test", # Prueba de Kruskal-Wallis para Edad
all_categorical() ~ "chisq.test" # Prueba de Chi-cuadrado para variables categóricas
),
test.args = list(
all_categorical() ~ list(simulate.p.value = TRUE) # Activar simulación para Chi-cuadrado
)
) %>%
modify_header(label = "**Variable**") %>%
modify_caption("**Table 1. Características sociodemográficas y estilo de vida en la población de estudio**") %>%
as_gt() %>%
gt::tab_style(
style = gt::cell_text(weight = "bold"),
locations = gt::cells_column_labels()
)
summary_table| Variable | Overall N = 3231 |
Control N = 691 |
DM2 N = 2011 |
ND N = 531 |
p-value2 |
|---|---|---|---|---|---|
| Sexo | 0.001 | ||||
| F | 204 (63%) | 49 (71%) | 133 (66%) | 22 (42%) | |
| M | 119 (37%) | 20 (29%) | 68 (34%) | 31 (58%) | |
| Edad | 60 (52 - 66) | 59 (46 - 65) | 60 (52 - 66) | 63 (60 - 68) | <0.001 |
| Disfunción_renal | <0.001 | ||||
| Con disfunción renal | 53 (16%) | 0 (0%) | 0 (0%) | 53 (100%) | |
| Sin disfunción renal | 270 (84%) | 69 (100%) | 201 (100%) | 0 (0%) | |
| Fuma | 0.054 | ||||
| No | 295 (91%) | 68 (99%) | 180 (90%) | 47 (89%) | |
| Sí | 28 (8.7%) | 1 (1.4%) | 21 (10%) | 6 (11%) | |
| Consumo_de_alcohol | 0.022 | ||||
| No | 196 (61%) | 35 (51%) | 121 (60%) | 40 (75%) | |
| Sí | 127 (39%) | 34 (49%) | 80 (40%) | 13 (25%) | |
| Hipertensión | <0.001 | ||||
| No | 153 (47%) | 50 (72%) | 90 (45%) | 13 (25%) | |
| Sí | 170 (53%) | 19 (28%) | 111 (55%) | 40 (75%) | |
| Diabetes_mellitus | <0.001 | ||||
| No | 76 (24%) | 69 (100%) | 7 (3.5%) | 0 (0%) | |
| Sí | 247 (76%) | 0 (0%) | 194 (97%) | 53 (100%) | |
| Dislipidemia | >0.9 | ||||
| No | 215 (67%) | 46 (67%) | 134 (67%) | 35 (66%) | |
| Sí | 108 (33%) | 23 (33%) | 67 (33%) | 18 (34%) | |
| Tratamiento_antihipertensivo | <0.001 | ||||
| No | 184 (57%) | 51 (74%) | 114 (57%) | 19 (36%) | |
| Sí | 139 (43%) | 18 (26%) | 87 (43%) | 34 (64%) | |
| Tratamiento_antilipemiante | 0.4 | ||||
| No | 267 (83%) | 56 (81%) | 170 (85%) | 41 (77%) | |
| Sí | 56 (17%) | 13 (19%) | 31 (15%) | 12 (23%) | |
| 1 n (%); Median (Q1 - Q3) | |||||
| 2 Pearson’s Chi-squared test with simulated p-value (based on 2000 replicates); Kruskal-Wallis rank sum test | |||||
selected_data <- data %>%
mutate(
Peso = as.numeric(Peso),
IMC = as.numeric(IMC),
Cintura = as.numeric(Cintura),
Cadera = as.numeric(Cadera),
ICC = as.numeric(ICC),
Glucosa = as.numeric(Glucosa),
HBA1c = as.numeric(HBA1c),
Colesterol_total = as.numeric(Colesterol_total),
VLDL = as.numeric(VLDL),
LDL = as.numeric(LDL),
HDL = as.numeric(HDL),
Trigliceridos = as.numeric(Trigliceridos),
Creatinina = as.numeric(Creatinina),
Urea = as.numeric(Urea),
BUN = as.numeric(BUN),
TFGe = as.numeric(TFGe)
) %>%
dplyr::select(Peso, IMC, Cintura, Cadera, ICC, Glucosa, HBA1c, Colesterol_total, VLDL, LDL, HDL, Trigliceridos, Creatinina, Urea, BUN, TFGe, Grupos_de_estudio)## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `LDL = as.numeric(LDL)`.
## Caused by warning:
## ! NAs introduced by coercion# 1. Análisis de normalidad
# Prueba de Shapiro-Wilk para cada variable dentro de los grupos
normality_tests <- selected_data %>%
group_by(Grupos_de_estudio) %>%
summarise(
Peso_p = shapiro.test(Peso)$p.value,
IMC_p = shapiro.test(IMC)$p.value,
Cintura_p = shapiro.test(Cintura)$p.value,
Cadera_p = shapiro.test(Cadera)$p.value,
ICC_p = shapiro.test(ICC)$p.value,
Glucosa_p = shapiro.test(Glucosa)$p.value,
HBA1c_p = shapiro.test(HBA1c)$p.value,
Colesterol_total_p = shapiro.test(Colesterol_total)$p.value,
VLDL_p = shapiro.test(VLDL)$p.value,
LDL_p = shapiro.test(LDL)$p.value,
HDL_p = shapiro.test(HDL)$p.value,
Trigliceridos_p = shapiro.test(Trigliceridos)$p.value,
Creatinina_p = shapiro.test(Creatinina)$p.value,
Urea_p = shapiro.test(Urea)$p.value,
BUN_p = shapiro.test(BUN)$p.value,
TFGe_p = shapiro.test(TFGe)$p.value,
.groups = "drop"
)
print(normality_tests)## # A tibble: 3 × 17
## Grupos_de_estudio Peso_p IMC_p Cintura_p Cadera_p ICC_p Glucosa_p HBA1c_p
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Control 4.37e-1 0.186 0.196 0.705 0.704 1.48e- 6 4.62e-7
## 2 DM2 5.85e-4 0.00347 0.544 0.00119 0.00193 4.23e-13 1.04e-8
## 3 ND 3.48e-1 0.993 0.541 0.187 0.608 1.65e- 3 7.58e-1
## # ℹ 9 more variables: Colesterol_total_p <dbl>, VLDL_p <dbl>, LDL_p <dbl>,
## # HDL_p <dbl>, Trigliceridos_p <dbl>, Creatinina_p <dbl>, Urea_p <dbl>,
## # BUN_p <dbl>, TFGe_p <dbl># 2. Análisis de homocedasticidad
# Prueba de Levene para cada variable
library(car) ## Loading required package: carData## Registered S3 methods overwritten by 'car':
## method from
## hist.boot FSA
## confint.boot FSAhomocedasticity_tests <- list(
Peso = leveneTest(Peso ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
IMC = leveneTest(IMC ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
Cintura = leveneTest(Cintura ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
Cadera = leveneTest(Cadera ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
ICC = leveneTest(ICC ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
Glucosa = leveneTest(Glucosa ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
HBA1c = leveneTest(HBA1c ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
Colesterol_total = leveneTest(Colesterol_total ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
VLDL = leveneTest(VLDL ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
LDL = leveneTest(LDL ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
HDL = leveneTest(HDL ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
Trigliceridos = leveneTest(Trigliceridos ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
Creatinina = leveneTest(Creatinina ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
Urea = leveneTest(Urea ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
BUN = leveneTest(BUN ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1],
TFGe = leveneTest(TFGe ~ Grupos_de_estudio, data = selected_data)$`Pr(>F)`[1]
)## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
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## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.homocedasticity_results <- data.frame(
Variable = names(homocedasticity_tests),
p_value = unlist(homocedasticity_tests)
)
print(homocedasticity_results)## Variable p_value
## Peso Peso 3.401467e-01
## IMC IMC 2.148653e-02
## Cintura Cintura 9.938146e-01
## Cadera Cadera 1.435644e-02
## ICC ICC 4.443850e-01
## Glucosa Glucosa 7.311333e-12
## HBA1c HBA1c 1.525227e-06
## Colesterol_total Colesterol_total 1.314301e-01
## VLDL VLDL 7.158737e-01
## LDL LDL 1.105762e-01
## HDL HDL 4.171676e-02
## Trigliceridos Trigliceridos 8.390450e-01
## Creatinina Creatinina 2.848371e-31
## Urea Urea 1.831490e-36
## BUN BUN 5.296303e-24
## TFGe TFGe 1.033284e-12### Casi todas las variables son no parametricas a excepción de peso, cintura y HDL. selected_data <- data %>%
mutate(
IMC = as.numeric(IMC),
Cadera = as.numeric(Cadera),
ICC = as.numeric(ICC),
Grupos_de_estudio = as.factor(Grupos_de_estudio)
) %>%
dplyr::select(IMC, Cadera, ICC, Grupos_de_estudio)
# 2. Crear la tabla de resumen con Kruskal-Wallis
summary_table <- selected_data %>%
tbl_summary(
by = Grupos_de_estudio,
missing = "no",
type = all_continuous() ~ "continuous",
statistic = all_continuous() ~ "{median} ({p25}, {p75})"
) %>%
add_overall() %>%
add_p(
test = all_continuous() ~ "kruskal.test",
pvalue_fun = function(x) style_pvalue(x, digits = 3)
) %>%
modify_header(label = "**Variable**") %>%
as_gt() %>%
gt::tab_header(
title = gt::md("**Tabla 2. Parámetros antropométricos**")
)
# Mostrar la tabla
print(summary_table)## <div id="cmytocarud" style="padding-left:0px;padding-right:0px;padding-top:10px;padding-bottom:10px;overflow-x:auto;overflow-y:auto;width:auto;height:auto;">
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## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_heading">
## <td colspan="6" class="gt_heading gt_title gt_font_normal gt_bottom_border" style><span class='gt_from_md'><strong>Tabla 2. Parámetros antropométricos</strong></span></td>
## </tr>
##
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="label"><span class='gt_from_md'><strong>Variable</strong></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_0"><span class='gt_from_md'><strong>Overall</strong><br />
## N = 323</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_1"><span class='gt_from_md'><strong>Control</strong><br />
## N = 69</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_2"><span class='gt_from_md'><strong>DM2</strong><br />
## N = 201</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="stat_3"><span class='gt_from_md'><strong>ND</strong><br />
## N = 53</span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span></th>
## <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="p.value"><span class='gt_from_md'><strong>p-value</strong></span><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>2</sup></span></th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="label" class="gt_row gt_left">IMC</td>
## <td headers="stat_0" class="gt_row gt_center">29.8 (25.9, 33.7)</td>
## <td headers="stat_1" class="gt_row gt_center">27.6 (25.3, 31.3)</td>
## <td headers="stat_2" class="gt_row gt_center">30.4 (26.7, 34.9)</td>
## <td headers="stat_3" class="gt_row gt_center">29.2 (25.3, 32.2)</td>
## <td headers="p.value" class="gt_row gt_center">0.004</td></tr>
## <tr><td headers="label" class="gt_row gt_left">Cadera</td>
## <td headers="stat_0" class="gt_row gt_center">105 (99, 111)</td>
## <td headers="stat_1" class="gt_row gt_center">106 (99, 111)</td>
## <td headers="stat_2" class="gt_row gt_center">106 (99, 113)</td>
## <td headers="stat_3" class="gt_row gt_center">102 (95, 108)</td>
## <td headers="p.value" class="gt_row gt_center">0.035</td></tr>
## <tr><td headers="label" class="gt_row gt_left">ICC</td>
## <td headers="stat_0" class="gt_row gt_center">0.93 (0.87, 1.00)</td>
## <td headers="stat_1" class="gt_row gt_center">0.87 (0.79, 0.91)</td>
## <td headers="stat_2" class="gt_row gt_center">0.94 (0.89, 1.01)</td>
## <td headers="stat_3" class="gt_row gt_center">1.00 (0.92, 1.04)</td>
## <td headers="p.value" class="gt_row gt_center"><0.001</td></tr>
## </tbody>
##
## <tfoot class="gt_footnotes">
## <tr>
## <td class="gt_footnote" colspan="6"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>1</sup></span> <span class='gt_from_md'>Median (Q1, Q3)</span></td>
## </tr>
## <tr>
## <td class="gt_footnote" colspan="6"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;line-height:0;"><sup>2</sup></span> <span class='gt_from_md'>Kruskal-Wallis rank sum test</span></td>
## </tr>
## </tfoot>
## </table>
## </div># 3. Realizar prueba post hoc
dunnTest(IMC ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 -3.1703813 0.00152239 0.00456717
## 2 Control - ND -0.8918574 0.37246937 1.00000000
## 3 DM2 - ND 1.8098208 0.07032358 0.21097073dunnTest(Cadera ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Warning: Some rows deleted from 'x' and 'g' because missing data.## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 -0.386659 0.699008694 1.00000000
## 2 Control - ND 1.897204 0.057801025 0.17340308
## 3 DM2 - ND 2.584991 0.009738146 0.02921444dunnTest(ICC ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Warning: Some rows deleted from 'x' and 'g' because missing data.## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 -6.160043 7.272536e-10 2.181761e-09
## 2 Control - ND -7.427849 1.103779e-13 3.311337e-13
## 3 DM2 - ND -3.237805 1.204532e-03 3.613596e-03#install.packages(c("ggpubr", "cowplot"))library(ggpubr)
library(cowplot)
library(ggplot2)data <- data %>%
mutate(
Peso = as.numeric(Peso),
Cintura = as.numeric(Cintura),
HDL = as.numeric(HDL),
Grupos_de_estudio = as.factor(Grupos_de_estudio) # Convertir en factor
)
custom_colors <- c("Control" = "#1f77b4", "DM2" = "#ff7f0e", "ND" = "red")
theme_custom <- theme_pubr() +
theme(
axis.title.x = element_blank(),
axis.text = element_text(size = 10),
legend.position = "none"
)
create_boxplot_with_error <- function(data, variable, y_label, y_lim) {
summary_stats <- data %>%
group_by(Grupos_de_estudio) %>%
summarise(
mean = mean(!!sym(variable), na.rm = TRUE),
sd = sd(!!sym(variable), na.rm = TRUE),
.groups = "drop"
)
ggplot(data, aes(x = Grupos_de_estudio, y = !!sym(variable), fill = Grupos_de_estudio)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = 0.2, alpha = 0.5, color = "black") +
stat_summary(fun = mean, geom = "point", shape = 18, size = 3, color = "black") +
geom_errorbar(
data = summary_stats,
aes(x = Grupos_de_estudio, ymin = mean - sd, ymax = mean + sd),
width = 0.2, inherit.aes = FALSE, color = "black"
) +
labs(
title = paste(variable, ""),
y = y_label,
fill = "Grupos"
) +
scale_fill_manual(values = custom_colors) +
ylim(0, y_lim) +
theme_custom
}
peso_plot <- create_boxplot_with_error(data, "Peso", "Peso (kg)", 120)
cintura_plot <- create_boxplot_with_error(data, "Cintura", "Cintura (cm)", 150)
HDL_plot <- create_boxplot_with_error(data, "HDL", "HDL (mg/dL)", 150)
print(peso_plot)## Warning: Removed 4 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 4 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_point()`).
print(cintura_plot)## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`geom_point()`).
print(HDL_plot)
run_anova_tukey <- function(data, variable) {
formula <- as.formula(paste(variable, "~ Grupos_de_estudio"))
anova_result <- aov(formula, data = data)
print(summary(anova_result))
tukey_result <- TukeyHSD(anova_result)
print(tukey_result)
}
run_anova_tukey(data, "Peso")## Df Sum Sq Mean Sq F value Pr(>F)
## Grupos_de_estudio 2 2487 1243.4 4.64 0.0103 *
## Residuals 320 85743 267.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = data)
##
## $Grupos_de_estudio
## diff lwr upr p adj
## DM2-Control 6.669100 1.291175 12.047025 0.0104482
## ND-Control 2.828698 -4.211305 9.868702 0.6115058
## ND-DM2 -3.840402 -9.792037 2.111234 0.2831524run_anova_tukey(data, "Cintura")## Df Sum Sq Mean Sq F value Pr(>F)
## Grupos_de_estudio 2 5865 2932.7 16.92 1.04e-07 ***
## Residuals 318 55105 173.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = data)
##
## $Grupos_de_estudio
## diff lwr upr p adj
## DM2-Control 10.2644493 5.936693 14.592206 0.0000002
## ND-Control 10.8822185 5.189854 16.574583 0.0000282
## ND-DM2 0.6177692 -4.207370 5.442908 0.9511409run_anova_tukey(data, "HDL")## Df Sum Sq Mean Sq F value Pr(>F)
## Grupos_de_estudio 2 923 461.7 2.314 0.1
## Residuals 320 63843 199.5
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = data)
##
## $Grupos_de_estudio
## diff lwr upr p adj
## DM2-Control -4.0770798 -8.717659 0.563499 0.0980206
## ND-Control -4.2887066 -10.363483 1.786070 0.2213772
## ND-DM2 -0.2116268 -5.347257 4.924003 0.9948229selected_data <- data %>%
mutate(
Glucosa = as.numeric(Glucosa),
HBA1c = as.numeric(HBA1c),
Colesterol_total = as.numeric(Colesterol_total),
VLDL = as.numeric(VLDL),
LDL = as.numeric(LDL),
HDL = as.numeric(HDL),
Trigliceridos = as.numeric(Trigliceridos),
Creatinina = as.numeric(Creatinina),
Urea = as.numeric(Urea),
BUN = as.numeric(BUN),
TFGe = as.numeric(TFGe),
Grupos_de_estudio = as.factor(Grupos_de_estudio)
) %>%
dplyr::select(Glucosa, HBA1c, Colesterol_total, VLDL, LDL, HDL, Trigliceridos, Creatinina, Urea, BUN, TFGe, Grupos_de_estudio
)## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `LDL = as.numeric(LDL)`.
## Caused by warning:
## ! NAs introduced by coercionsummary_table <- selected_data %>%
tbl_summary(
by = Grupos_de_estudio,
missing = "no",
type = all_continuous() ~ "continuous",
statistic = all_continuous() ~ "{median} ({p25}, {p75})"
) %>%
add_overall() %>%
add_p(
test = all_continuous() ~ "kruskal.test",
pvalue_fun = function(x) style_pvalue(x, digits = 3)
) %>%
modify_header(label = "**Variable**") %>%
as_gt() %>%
gt::tab_header(
title = gt::md("**Tabla 3. Parámetros bioquímicos**")
)
summary_table| Tabla 3. Parámetros bioquímicos | |||||
| Variable | Overall N = 3231 |
Control N = 691 |
DM2 N = 2011 |
ND N = 531 |
p-value2 |
|---|---|---|---|---|---|
| Glucosa | 125 (94, 185) | 88 (83, 94) | 154 (110, 216) | 140 (102, 188) | <0.001 |
| HBA1c | 6.90 (5.90, 8.20) | 5.70 (5.20, 6.10) | 7.40 (6.50, 8.90) | 7.01 (5.82, 7.90) | <0.001 |
| Colesterol_total | 186 (151, 222) | 185 (162, 214) | 190 (159, 226) | 151 (124, 204) | 0.006 |
| VLDL | 28 (19, 35) | 23 (17, 33) | 30 (20, 36) | 26 (17, 36) | 0.021 |
| LDL | 111 (83, 152) | 118 (96, 147) | 115 (85, 156) | 88 (60, 130) | 0.011 |
| HDL | 41 (31, 51) | 45 (38, 52) | 40 (30, 50) | 36 (31, 51) | 0.024 |
| Trigliceridos | 140 (93, 178) | 114 (84, 163) | 148 (99, 181) | 130 (85, 181) | 0.020 |
| Creatinina | 0.70 (0.60, 0.90) | 0.70 (0.60, 0.70) | 0.70 (0.60, 0.80) | 1.77 (1.20, 5.81) | <0.001 |
| Urea | 34 (26, 48) | 28 (24, 33) | 34 (26, 45) | 83 (50, 134) | <0.001 |
| BUN | 16 (12, 23) | 13 (11, 15) | 16 (13, 21) | 39 (23, 63) | <0.001 |
| TFGe | 95 (74, 104) | 99 (94, 106) | 98 (86, 105) | 38 (7, 54) | <0.001 |
| 1 Median (Q1, Q3) | |||||
| 2 Kruskal-Wallis rank sum test | |||||
dunnTest(Glucosa ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 -10.225649 1.522055e-24 4.566166e-24
## 2 Control - ND -6.783889 1.169828e-11 3.509485e-11
## 3 DM2 - ND 1.215492 2.241784e-01 6.725353e-01dunnTest(HBA1c ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Warning: Some rows deleted from 'x' and 'g' because missing data.## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 -9.362907 7.757228e-21 2.327168e-20
## 2 Control - ND -4.908771 9.164878e-07 2.749463e-06
## 3 DM2 - ND 2.602713 9.248929e-03 2.774679e-02dunnTest(Colesterol_total ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 -0.4315202 0.666090202 1.00000000
## 2 Control - ND 2.3608790 0.018231680 0.05469504
## 3 DM2 - ND 3.1825334 0.001459927 0.00437978dunnTest(VLDL ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 -2.4407374 0.01465731 0.04397192
## 2 Control - ND -0.2949572 0.76802657 1.00000000
## 3 DM2 - ND 1.8565658 0.06337295 0.19011885dunnTest(LDL ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Warning: Some rows deleted from 'x' and 'g' because missing data.## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 0.2199792 0.825887366 1.00000000
## 2 Control - ND 2.5927483 0.009521242 0.02856373
## 3 DM2 - ND 2.8630372 0.004196013 0.01258804dunnTest(HDL ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 2.5514810 0.01072662 0.03217985
## 2 Control - ND 2.2423563 0.02493835 0.07481506
## 3 DM2 - ND 0.3468832 0.72867903 1.00000000dunnTest(Trigliceridos ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 -2.4433355 0.01455220 0.04365661
## 2 Control - ND -0.2693067 0.78769371 1.00000000
## 3 DM2 - ND 1.8892547 0.05885771 0.17657313dunnTest(Creatinina ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 -0.7585522 4.481205e-01 1.000000e+00
## 2 Control - ND -9.3628289 7.762984e-21 2.328895e-20
## 3 DM2 - ND -10.3895660 2.766089e-25 8.298266e-25dunnTest(Urea ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 -4.187849 2.816113e-05 8.448338e-05
## 2 Control - ND -9.574491 1.023601e-21 3.070802e-21
## 3 DM2 - ND -7.541207 4.656418e-14 1.396925e-13dunnTest(BUN ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 -4.587600 4.483714e-06 1.345114e-05
## 2 Control - ND -9.638377 5.505135e-22 1.651540e-21
## 3 DM2 - ND -7.255559 4.000071e-13 1.200021e-12dunnTest(TFGe ~ Grupos_de_estudio, data = selected_data, method = "bonferroni")## Dunn (1964) Kruskal-Wallis multiple comparison
## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Control - DM2 1.83224 6.691565e-02 2.007469e-01
## 2 Control - ND 10.46743 1.219105e-25 3.657315e-25
## 3 DM2 - ND 10.72597 7.687432e-27 2.306230e-26data$RIESGO_CV_Dx_Framingham <- factor(data$RIESGO_CV_Dx_Framingham,
levels = c("BAJO", "MODERADO", "ALTO"))
tabla <- table(data$Grupos_de_estudio, data$RIESGO_CV_Dx_Framingham)
tabla_con_totales <- addmargins(tabla)
print(tabla_con_totales)##
## BAJO MODERADO ALTO Sum
## Control 47 18 4 69
## DM2 40 81 80 201
## ND 3 15 35 53
## Sum 90 114 119 323frecuencias_relativas <- prop.table(tabla, margin = 1) * 100
print(round(frecuencias_relativas, 1))##
## BAJO MODERADO ALTO
## Control 68.1 26.1 5.8
## DM2 19.9 40.3 39.8
## ND 5.7 28.3 66.0tabla_df <- as.data.frame(tabla)
# Graficar frecuencias absolutas
ggplot(tabla_df, aes(x = Var1, y = Freq, fill = Var2)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
labs(
x = "Grupos de Estudio",
y = "Proporción",
fill = "Riesgo Cardiovascular",
title = "Distribución del Riesgo Cardiovascular por Grupo de Estudio"
) +
theme_minimal()
chisq_result <- chisq.test(tabla)
print(chisq_result)##
## Pearson's Chi-squared test
##
## data: tabla
## X-squared = 88.682, df = 4, p-value < 2.2e-16library(DescTools)
v_cramer <- CramerV(tabla)
print(paste("V de Cramer:", v_cramer))## [1] "V de Cramer: 0.370511636468487"###Valores de referencia para el V de Cramer:
# 0.00 - 0.10 Asociación débil o despreciable
# 0.10 - 0.30 Asociación moderada
# 0.30 - 0.50 Asociación fuerte
# > 0.50 :Asociación muy fuertedata <- data %>%
mutate(
Riesgo_ApoE = factor(
case_when(
ApoE %in% c("E2/E2", "E2/E3") ~ "Renal",
ApoE %in% c("E3/E4", "E4/E4") ~ "Cardiovascular",
ApoE == "E2/E4" ~ "Renal y Cardiovascular",
ApoE == "E3/E3" ~ "Referencia"
),
levels = c("Renal", "Cardiovascular", "Renal y Cardiovascular", "Referencia")
)
)
tabla <- table(data$Grupos_de_estudio, data$Riesgo_ApoE)
tabla_con_totales <- addmargins(tabla)
print(tabla_con_totales)##
## Renal Cardiovascular Renal y Cardiovascular Referencia Sum
## Control 5 14 1 49 69
## DM2 15 76 8 102 201
## ND 4 24 3 22 53
## Sum 24 114 12 173 323frecuencias_relativas <- prop.table(tabla, margin = 1) * 100
print(round(frecuencias_relativas, 1))##
## Renal Cardiovascular Renal y Cardiovascular Referencia
## Control 7.2 20.3 1.4 71.0
## DM2 7.5 37.8 4.0 50.7
## ND 7.5 45.3 5.7 41.5tabla_df <- as.data.frame(tabla)
ggplot(tabla_df, aes(x = Var1, y = Freq, fill = Var2)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
labs(
x = "Grupos de estudio",
y = "Proporción",
fill = "Riesgo ApoE",
title = "Distribución del riesgo ApoE por grupo de estudio"
) +
theme_minimal()
fisher_result <- fisher.test(tabla)
print(fisher_result)##
## Fisher's Exact Test for Count Data
##
## data: tabla
## p-value = 0.02522
## alternative hypothesis: two.sided## Aunque no es estadísticamente significativa, la tabla de frecuencias relativas muestra tendencias ND tiene un porcentaje más alto en la categoría “Cardiovascular” (44.3%) comparado con DM2 (32.2%) y Control (22.8%) esto puede ser relevante clínicamente, incluso si no es estadísticamente significativodata <- data %>%
mutate(
Riesgo_ApoE = factor(
case_when(
ApoE %in% c("E2/E2", "E2/E3", "E2/E4") ~ "Renal y Cardiovascular",
ApoE %in% c("E3/E4", "E4/E4") ~ "Cardiovascular",
ApoE == "E3/E3" ~ "Referencia"
),
levels = c("Renal y Cardiovascular", "Cardiovascular", "Referencia")
)
)
tabla <- table(data$Grupos_de_estudio, data$Riesgo_ApoE)
tabla_con_totales <- addmargins(tabla)
print(tabla_con_totales)##
## Renal y Cardiovascular Cardiovascular Referencia Sum
## Control 6 14 49 69
## DM2 23 76 102 201
## ND 7 24 22 53
## Sum 36 114 173 323frecuencias_relativas <- prop.table(tabla, margin = 1) * 100
print(round(frecuencias_relativas, 1))##
## Renal y Cardiovascular Cardiovascular Referencia
## Control 8.7 20.3 71.0
## DM2 11.4 37.8 50.7
## ND 13.2 45.3 41.5tabla_df <- as.data.frame(tabla)
ggplot(tabla_df, aes(x = Var1, y = Freq, fill = Var2)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
labs(
x = "Grupos de estudio",
y = "Proporción",
fill = "Riesgo ApoE",
title = "Distribución del riesgo ApoE por grupo de estudio"
) +
theme_minimal()
fisher_result <- fisher.test(tabla)
print(fisher_result)##
## Fisher's Exact Test for Count Data
##
## data: tabla
## p-value = 0.01173
## alternative hypothesis: two.sided### Se utilizo fisher porque no tiene restricciones sobre las frecuencias esperadas en las celdascolnames(data)[colnames(data) == "IL-6"] <- "IL6"
colnames(data)[colnames(data) == "IL-6"] <- "IL6"
data$IL6 <- factor(data$IL6, levels = c("G/G", "G/C", "C/C"))
tabla_IL6 <- table(data$Grupos_de_estudio, data$IL6)
tabla_IL6_con_totales <- addmargins(tabla_IL6)
print(tabla_IL6_con_totales)##
## G/G G/C C/C Sum
## Control 44 21 4 69
## DM2 136 55 10 201
## ND 33 20 0 53
## Sum 213 96 14 323frecuencias_relativas_IL6 <- prop.table(tabla_IL6, margin = 1) * 100
print(round(frecuencias_relativas_IL6, 1))##
## G/G G/C C/C
## Control 63.8 30.4 5.8
## DM2 67.7 27.4 5.0
## ND 62.3 37.7 0.0tabla_IL6_df <- as.data.frame(tabla_IL6)
library(ggplot2)
ggplot(tabla_IL6_df, aes(x = Var1, y = Freq, fill = Var2)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
labs(
x = "Grupos de Estudio",
y = "Proporción",
fill = "Genotipos de IL6",
title = "Distribución de los Genotipos de IL6 por Grupo de Estudio"
) +
theme_minimal()
fisher_result <- fisher.test(tabla)
print(fisher_result)##
## Fisher's Exact Test for Count Data
##
## data: tabla
## p-value = 0.01173
## alternative hypothesis: two.sided###El genotipo G/C esta más representado en el grupo ND, mientras que el genotipo C/C es raro en todos los grupos, pero más presente en Control#install.packages("https://cran.r-project.org/bin/macosx/big-sur-arm64/contrib/4.3/rms_6.8-1.tgz", repos = NULL, type = "binary")
library(rms)## Loading required package: Hmisc##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:DescTools':
##
## %nin%, Label, Mean, Quantile## The following object is masked from 'package:gt':
##
## html## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, unitssnp_indices <- which(colnames(data) == "IL6")
library(SNPassoc)## Registered S3 method overwritten by 'SNPassoc':
## method from
## summary.haplo.glm haplo.statsSNPs <- setupSNP(data, colSNPs = snp_indices, sep = "/")plot(SNPs$IL6)
plot(SNPs$IL6, type = pie)
names(SNPs)## [1] "Registro" "Nombre"
## [3] "GRUPO" "Sexo"
## [5] "Edad" "Peso"
## [7] "Talla" "IMC"
## [9] "Cintura" "Cadera"
## [11] "ICC" "Glucosa"
## [13] "HBA1c" "Colesterol_total"
## [15] "VLDL" "LDL"
## [17] "HDL" "Trigliceridos"
## [19] "Creatinina" "Urea"
## [21] "BUN" "TFGe"
## [23] "DISFUNCIÓN_RENAL" "RIESGO_CARDIO-VASCULAR"
## [25] "RIESGO_CV_Dx_Framingham" "Fuma"
## [27] "Consume_alcohol" "HTA"
## [29] "TX_HTA" "Diabetes_Mellitus"
## [31] "Dislipidemia" "Tratamiento_Lipemiante"
## [33] "ApoE" "Riesgo_ApoE"
## [35] "Grupos_de_estudio" "Disfunción_renal"
## [37] "Consumo_de_alcohol" "Hipertensión"
## [39] "Diabetes_mellitus" "Tratamiento_antihipertensivo"
## [41] "Tratamiento_antilipemiante" "IL6"#Perform association analysis for each SNP
association(TFGe ~ IL6, data = SNPs)##
## SNP: IL6 adjusted by:
## n me se dif lower upper p-value AIC
## Codominant
## G/G 213 87.45 2.014 0.0000 0.1308 3101
## G/C 96 82.88 3.108 -4.5755 -11.611 2.460
## C/C 14 98.57 3.027 11.1188 -4.672 26.910
## Dominant
## G/G 213 87.45 2.014 0.0000 0.4544 3103
## G/C-C/C 110 84.87 2.782 -2.5781 -9.324 4.168
## Recessive
## G/G-G/C 309 86.03 1.692 0.0000 0.1174 3101
## C/C 14 98.57 3.027 12.5403 -3.114 28.194
## Overdominant
## G/G-C/C 227 88.14 1.906 0.0000 0.1404 3101
## G/C 96 82.88 3.108 -5.2613 -12.239 1.716
## log-Additive
## 0,1,2 -0.1825 -5.811 5.446 0.9494 3103# Reemplazar "NA" por NA en las variables numéricas
data$Cintura[data$Cintura == "NA"] <- NA
data$ICC[data$ICC== "NA"] <- NA
data$Cadera[data$Cadera == "NA"] <- NA
data$HBA1c[data$HBA1c == "NA"] <- NA# Convertir columnas a numéricas
data$Cintura <- as.numeric(as.character(data$Cintura))
data$Cadera <- as.numeric(as.character(data$Cadera))
data$ICC <- as.numeric(as.character(data$ICC))
data$HBA1c <- as.numeric(as.character(data$HBA1c))# Verificar qué filas tienen valores no numéricos en cada columna
str(data$Cintura) ## num [1:323] 113 105 109 103 87 ...str(data$Cadera)## num [1:323] 123 115 107 100 94 ...str(data$HBA1c) ## num [1:323] 9.1 7.9 5.7 7.4 5.8 9.5 8 7.9 6.5 8.1 ...str(data$ICC)## num [1:323] 0.919 0.913 1.019 1.025 0.926 ...# Verificar cuántos NA hay en cada columna
sum(is.na(data$Cintura))## [1] 2sum(is.na(data$Cadera))## [1] 2sum(is.na(data$HBA1c))## [1] 1sum(is.na(data$ICC))## [1] 2#install.packages("car")
library(car)# Identificar las variables colineales
modelo <- lm(IMC ~ IL6 + Glucosa + HBA1c + Sexo + Edad + Urea + BUN + TFGe +
Creatinina + Colesterol_total + HDL + LDL + VLDL + Trigliceridos +
Consume_alcohol + HTA + Fuma, data = data)
alias_info <- alias(modelo)
colineales <- rownames(alias_info$Complete)
print("Variables colineales:")## [1] "Variables colineales:"print(colineales)## [1] "VLDL" "Trigliceridos" "Consume_alcohol" "HTA"
## [5] "FumaSí"colSums(is.na(data))## Registro Nombre
## 0 0
## GRUPO Sexo
## 0 0
## Edad Peso
## 0 0
## Talla IMC
## 0 0
## Cintura Cadera
## 2 2
## ICC Glucosa
## 2 0
## HBA1c Colesterol_total
## 1 0
## VLDL LDL
## 0 0
## HDL Trigliceridos
## 0 0
## Creatinina Urea
## 0 0
## BUN TFGe
## 0 0
## DISFUNCIÓN_RENAL RIESGO_CARDIO-VASCULAR
## 0 0
## RIESGO_CV_Dx_Framingham Fuma
## 0 0
## Consume_alcohol HTA
## 0 0
## TX_HTA Diabetes_Mellitus
## 0 0
## Dislipidemia Tratamiento_Lipemiante
## 0 0
## IL6 ApoE
## 0 0
## Riesgo_ApoE Grupos_de_estudio
## 0 0
## Disfunción_renal Consumo_de_alcohol
## 0 0
## Hipertensión Diabetes_mellitus
## 0 0
## Tratamiento_antihipertensivo Tratamiento_antilipemiante
## 0 0data[!complete.cases(data), ]## Registro Nombre GRUPO Sexo Edad Peso Talla
## 26 MIDE087 RODRÍGUEZ MORALES JESÚS FRANCISCO 2 M 62 59.6 1.58
## 94 IRC5-ND031 María del Rosario Sarabia Zamora 2 F 57 60.0 1.66
## 239 IRC-ND043 Jesus Enrique Ramirez Vega 1 M 36 103.0 1.78
## IMC Cintura Cadera ICC Glucosa HBA1c Colesterol_total VLDL
## 26 23.87438 NA NA NA 210.61 6.9 224.33 36.134
## 94 21.77384 98 95 1.031579 278.00 NA 197.30 16.400
## 239 32.50852 NA NA NA 87.90 6.4 114.00 51.860
## LDL HDL Trigliceridos Creatinina Urea BUN TFGe
## 26 171.68 16.52 180.67 3.80 176.32 82.93000 16.0
## 94 116.9 64.00 82.00 5.81 134.20 62.62834 5.7
## 239 27.84 34.30 259.30 0.75 37.90 17.70000 118.0
## DISFUNCIÓN_RENAL RIESGO_CARDIO-VASCULAR RIESGO_CV_Dx_Framingham Fuma
## 26 1 30 ALTO Sí
## 94 1 8.6999999999999993 BAJO No
## 239 0 5.3 BAJO No
## Consume_alcohol HTA TX_HTA Diabetes_Mellitus Dislipidemia
## 26 0 0 0 1 No
## 94 1 1 1 1 No
## 239 0 0 0 1 Sí
## Tratamiento_Lipemiante IL6 ApoE Riesgo_ApoE Grupos_de_estudio
## 26 0 G/G E3/E4 Cardiovascular ND
## 94 0 G/G E4/E4 Cardiovascular ND
## 239 1 C/C E3/E3 Referencia DM2
## Disfunción_renal Consumo_de_alcohol Hipertensión Diabetes_mellitus
## 26 Con disfunción renal No No Sí
## 94 Con disfunción renal Sí Sí Sí
## 239 Sin disfunción renal No No Sí
## Tratamiento_antihipertensivo Tratamiento_antilipemiante
## 26 No No
## 94 Sí No
## 239 No Sídata <- na.omit(data)
colSums(is.na(data))## Registro Nombre
## 0 0
## GRUPO Sexo
## 0 0
## Edad Peso
## 0 0
## Talla IMC
## 0 0
## Cintura Cadera
## 0 0
## ICC Glucosa
## 0 0
## HBA1c Colesterol_total
## 0 0
## VLDL LDL
## 0 0
## HDL Trigliceridos
## 0 0
## Creatinina Urea
## 0 0
## BUN TFGe
## 0 0
## DISFUNCIÓN_RENAL RIESGO_CARDIO-VASCULAR
## 0 0
## RIESGO_CV_Dx_Framingham Fuma
## 0 0
## Consume_alcohol HTA
## 0 0
## TX_HTA Diabetes_Mellitus
## 0 0
## Dislipidemia Tratamiento_Lipemiante
## 0 0
## IL6 ApoE
## 0 0
## Riesgo_ApoE Grupos_de_estudio
## 0 0
## Disfunción_renal Consumo_de_alcohol
## 0 0
## Hipertensión Diabetes_mellitus
## 0 0
## Tratamiento_antihipertensivo Tratamiento_antilipemiante
## 0 0data$Sexo <- as.factor(data$Sexo)
levels(data$Sexo)## [1] "F" "M"data$Sexo <- relevel(data$Sexo, ref = "M")### Evaluar la asociación entre variantes genéticas (IL-6 y ApoE) y la presencia de disfunción renal
set.seed(123) # Para reproducibilidad
data <- data[sample(nrow(data)), ]
modelo_nulo <- glm(DISFUNCIÓN_RENAL ~ 1, data = data, family = binomial, na.action = na.exclude)
# Modelo completo
modelo_completo <- glm(DISFUNCIÓN_RENAL ~ IL6 + ApoE + Edad + Sexo + HBA1c,
family = binomial, data = data)
OR <- exp(coef(modelo_completo))
CI <- exp(confint(modelo_completo))## Waiting for profiling to be done...## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredp_values <- summary(modelo_completo)$coefficients[, 4]
OR_completo <- cbind(OR, CI, p_values)
print(OR_completo)## OR 2.5 % 97.5 % p_values
## (Intercept) 3.888603e-02 0.001441672 7.905513e-01 0.0418448514
## IL6G/C 1.141075e+00 0.563575042 2.254517e+00 0.7077647460
## IL6C/C 1.282622e-07 NA 5.785429e+24 0.9875715946
## ApoEE2/E3 5.947689e-01 0.052186999 6.813698e+00 0.6622100231
## ApoEE2/E4 1.212036e+00 0.129214172 1.350098e+01 0.8670946100
## ApoEE3/E3 5.635615e-01 0.103147069 4.541643e+00 0.5369128582
## ApoEE3/E4 8.969081e-01 0.159878873 7.285258e+00 0.9075755842
## ApoEE4/E4 3.654006e-01 0.031191633 4.213214e+00 0.4011679793
## Edad 1.057630e+00 1.025464300 1.093584e+00 0.0006054667
## SexoF 3.252070e-01 0.167104506 6.219443e-01 0.0007640348
## HBA1c 8.883826e-01 0.718721699 1.079144e+00 0.2517371543summary(modelo_completo)##
## Call:
## glm(formula = DISFUNCIÓN_RENAL ~ IL6 + ApoE + Edad + Sexo +
## HBA1c, family = binomial, data = data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.24712 1.59559 -2.035 0.041845 *
## IL6G/C 0.13197 0.35205 0.375 0.707765
## IL6C/C -15.86919 1018.73641 -0.016 0.987572
## ApoEE2/E3 -0.51958 1.18935 -0.437 0.662210
## ApoEE2/E4 0.19230 1.14910 0.167 0.867095
## ApoEE3/E3 -0.57348 0.92873 -0.617 0.536913
## ApoEE3/E4 -0.10880 0.93716 -0.116 0.907576
## ApoEE4/E4 -1.00676 1.19919 -0.840 0.401168
## Edad 0.05603 0.01634 3.429 0.000605 ***
## SexoF -1.12329 0.33377 -3.366 0.000764 ***
## HBA1c -0.11835 0.10326 -1.146 0.251737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 280.72 on 319 degrees of freedom
## Residual deviance: 243.15 on 309 degrees of freedom
## AIC: 265.15
##
## Number of Fisher Scoring iterations: 16##removimos del modelo la variable de TFGe porque esta perfectamente alineado con la disfunción renal###Evaluar el RCV por RLM
##Antes de ajustar el modelo, asegurémonos de que las variables están en el formato correcto
library(nnet)
data$RIESGO_CV_Dx_Framingham <- factor(data$RIESGO_CV_Dx_Framingham,
levels = c("BAJO", "MODERADO", "ALTO")) # "BAJO" como referencia
data$IL6 <- factor(data$IL6, levels = c("G/G", "G/C", "C/C"))
data$IL6 <- relevel(data$IL6, ref = "G/G") # Establecer referencia en "G/G"
data$ApoE <- as.factor(data$ApoE)
data$Sexo <- as.factor(data$Sexo)
data$DISFUNCIÓN_RENAL <- as.factor(data$DISFUNCIÓN_RENAL)
num_vars <- c("Edad", "HBA1c", "IMC", "TFGe", "Creatinina")
data[num_vars] <- lapply(data[num_vars], as.numeric)
data <- droplevels(data)# Ajustar el modelo
modelo_multinom <- multinom(RIESGO_CV_Dx_Framingham ~ IL6 + ApoE + Edad + Sexo + HBA1c + IMC + TFGe + Creatinina,
data = data)## # weights: 45 (28 variable)
## initial value 351.555932
## iter 10 value 294.557952
## iter 20 value 256.606337
## iter 30 value 250.444030
## iter 40 value 250.134863
## iter 40 value 250.134863
## iter 40 value 250.134863
## final value 250.134863
## convergedexp(cbind(OR = coef(modelo_multinom), confint(modelo_multinom)))## Warning in cbind(OR = coef(modelo_multinom), confint(modelo_multinom)): number
## of rows of result is not a multiple of vector length (arg 2)## (Intercept) IL6G/C IL6C/C ApoEE2/E3 ApoEE2/E4 ApoEE3/E3
## MODERADO 2.615571e-05 1.3512822 1.874396 0.3005051 1.3473818 0.5710123
## ALTO 1.254371e-07 0.6101471 0.734676 0.5484217 0.6031813 0.4568554
## ApoEE3/E4 ApoEE4/E4 Edad SexoF HBA1c IMC TFGe
## MODERADO 0.6128642 0.4854360 1.114979 0.4922020 1.446485 1.090690 1.0009310
## ALTO 0.2783592 0.6293414 1.238810 0.2588276 1.594076 1.107588 0.9843991
## Creatinina
## MODERADO 1.644636 4.022852e-08
## ALTO 1.616108 6.476752e-01z_values <- coef(modelo_multinom) / summary(modelo_multinom)$standard.errors
p_values <- 2 * (1 - pnorm(abs(z_values)))
OR_table <- exp(coef(modelo_multinom))
CI_lower <- exp(coef(modelo_multinom) - 1.96 * summary(modelo_multinom)$standard.errors)
CI_upper <- exp(coef(modelo_multinom) + 1.96 * summary(modelo_multinom)$standard.errors)
resultados <- data.frame(OR = OR_table, Lower95 = CI_lower, Upper95 = CI_upper, p_values)
print(resultados)## OR..Intercept. OR.IL6G.C OR.IL6C.C OR.ApoEE2.E3 OR.ApoEE2.E4
## MODERADO 2.615571e-05 1.3512822 1.874396 0.3005051 1.3473818
## ALTO 1.254371e-07 0.6101471 0.734676 0.5484217 0.6031813
## OR.ApoEE3.E3 OR.ApoEE3.E4 OR.ApoEE4.E4 OR.Edad OR.SexoF OR.HBA1c
## MODERADO 0.5710123 0.6128642 0.4854360 1.114979 0.4922020 1.446485
## ALTO 0.4568554 0.2783592 0.6293414 1.238810 0.2588276 1.594076
## OR.IMC OR.TFGe OR.Creatinina Lower95..Intercept. Lower95.IL6G.C
## MODERADO 1.090690 1.0009310 1.644636 4.022373e-08 0.6476665
## ALTO 1.107588 0.9843991 1.616108 8.408510e-11 0.2529850
## Lower95.IL6C.C Lower95.ApoEE2.E3 Lower95.ApoEE2.E4 Lower95.ApoEE3.E3
## MODERADO 0.36952126 0.01712973 0.07187028 0.05094031
## ALTO 0.09423943 0.02939918 0.02524783 0.03627083
## Lower95.ApoEE3.E4 Lower95.ApoEE4.E4 Lower95.Edad Lower95.SexoF
## MODERADO 0.05264534 0.03120928 1.065546 0.2192881
## ALTO 0.02083757 0.03445786 1.170001 0.1039754
## Lower95.HBA1c Lower95.IMC Lower95.TFGe Lower95.Creatinina
## MODERADO 1.179688 1.021376 0.9731968 0.5630997
## ALTO 1.258140 1.025672 0.9552741 0.5485378
## Upper95..Intercept. Upper95.IL6G.C Upper95.IL6C.C Upper95.ApoEE2.E3
## MODERADO 0.0170078952 2.819296 9.507875 5.271733
## ALTO 0.0001871255 1.471548 5.727420 10.230435
## Upper95.ApoEE2.E4 Upper95.ApoEE3.E3 Upper95.ApoEE3.E4
## MODERADO 25.25992 6.400727 7.134583
## ALTO 14.41025 5.754400 3.718467
## Upper95.ApoEE4.E4 Upper95.Edad Upper95.SexoF Upper95.HBA1c Upper95.IMC
## MODERADO 7.550577 1.166706 1.1047699 1.773621 1.164708
## ALTO 11.494348 1.311665 0.6443035 2.019712 1.196046
## Upper95.TFGe Upper95.Creatinina X.Intercept. IL6G.C IL6C.C
## MODERADO 1.029456 4.803461 1.409083e-03 0.4223578 0.4482387
## ALTO 1.014412 4.761393 2.023923e-05 0.2713612 0.7685486
## ApoEE2.E3 ApoEE2.E4 ApoEE3.E3 ApoEE3.E4 ApoEE4.E4 Edad
## MODERADO 0.4107298 0.8419639 0.6495108 0.6958261 0.6057444 2.551292e-06
## ALTO 0.6874049 0.7548661 0.5444547 0.3335597 0.7547007 2.058353e-13
## SexoF HBA1c IMC TFGe Creatinina
## MODERADO 0.085713386 0.0003873197 0.009559738 0.9482453 0.3629291
## ALTO 0.003675876 0.0001125500 0.009145035 0.3048168 0.3839019##Si el p-valor de la prueba es < 0.05, significa que al menos un predictor está asociado significativamente con el riesgo cardiovascular.
# Comparar con el modelo nulo (sin predictores)
modelo_nulo <- multinom(RIESGO_CV_Dx_Framingham ~ 1, data = data)## # weights: 6 (2 variable)
## initial value 351.555932
## final value 348.989670
## converged# Prueba de razón de verosimilitudes (LR Test)
anova(modelo_nulo, modelo_multinom, test = "Chisq")## Likelihood ratio tests of Multinomial Models
##
## Response: RIESGO_CV_Dx_Framingham
## Model Resid. df
## 1 1 638
## 2 IL6 + ApoE + Edad + Sexo + HBA1c + IMC + TFGe + Creatinina 612
## Resid. Dev Test Df LR stat. Pr(Chi)
## 1 697.9793
## 2 500.2697 1 vs 2 26 197.7096 0#### Pr(Chi) = 0 significa que el modelo completo es significativamente mejor que el modelo nulo, lo que indica que al menos una de las variables predictoras está asociada con el riesgo cardiovascular. No significa que todas las variables sean significativas, pero al menos una de ellas tiene un efecto importante.###Evaluar cómo IL6 y ApoE se asocian con variables clínicas continuas (HBA1c, IMC, TFGe, etc.), usamos modelos de regresión lineal
# Regresión lineal con TFGe como variable de respuesta
modelo_lineal_TFGe <- lm(TFGe ~ IL6 + ApoE + Edad + Sexo + IMC + HBA1c + Creatinina, data = data)
coef_table_TFGe <- summary(modelo_lineal_TFGe)$coefficients
CI_TFGe <- confint(modelo_lineal_TFGe)
OR_TFGe <- exp(coef_table_TFGe[, 1])
resultados_TFGe <- data.frame(
Variable = rownames(coef_table_TFGe),
OR = OR_TFGe,
Coef = coef_table_TFGe[,1],
Lower95 = CI_TFGe[,1],
Upper95 = CI_TFGe[,2],
p_values = coef_table_TFGe[,4]
)
print("Resultados del modelo con TFGe como variable de respuesta:")## [1] "Resultados del modelo con TFGe como variable de respuesta:"print(resultados_TFGe)## Variable OR Coef Lower95 Upper95
## (Intercept) (Intercept) 2.616844e+67 155.23517011 134.8667725 175.6035677
## IL6G/C IL6G/C 2.133877e+00 0.75794049 -3.2973278 4.8132088
## IL6C/C IL6C/C 2.159569e+01 3.07249373 -6.2316474 12.3766349
## ApoEE2/E3 ApoEE2/E3 3.402816e-01 -1.07798183 -14.9936773 12.8377137
## ApoEE2/E4 ApoEE2/E4 4.706831e-01 -0.75357020 -15.4106399 13.9034995
## ApoEE3/E3 ApoEE3/E3 2.824974e+00 1.03849912 -10.6026580 12.6796562
## ApoEE3/E4 ApoEE3/E4 1.330171e+00 0.28530744 -11.5345707 12.1051855
## ApoEE4/E4 ApoEE4/E4 2.932719e+00 1.07593000 -12.4935900 14.6454500
## Edad Edad 3.890993e-01 -0.94392070 -1.1079400 -0.7799014
## SexoF SexoF 3.904189e-01 -0.94053503 -4.9276833 3.0466132
## IMC IMC 1.066305e+00 0.06419921 -0.2850323 0.4134307
## HBA1c HBA1c 8.273930e-01 -0.18947546 -1.2068894 0.8279385
## Creatinina Creatinina 1.376999e-05 -11.19301885 -12.2125547 -10.1734830
## p_values
## (Intercept) 1.598089e-38
## IL6G/C 7.132964e-01
## IL6C/C 5.163087e-01
## ApoEE2/E3 8.789480e-01
## ApoEE2/E4 9.194837e-01
## ApoEE3/E3 8.607719e-01
## ApoEE3/E4 9.621482e-01
## ApoEE4/E4 8.761190e-01
## Edad 4.483717e-25
## SexoF 6.428556e-01
## IMC 7.178055e-01
## HBA1c 7.142791e-01
## Creatinina 1.408226e-63##El modelo esta desajustado por los valores de OR tan extremos es necesario utilizar otro tipo de regresion para minimar el desajuste por el tamaño de muestra en los grupos. Dado que muchas variables no son significativas, intentar con un modelo parsimoniosolibrary(MASS)
modelo_inicial <- lm(TFGe ~ IL6 + ApoE + Edad + Sexo + IMC + HBA1c + Creatinina, data = data)
# Modelo parsimonioso
modelo_parsimonioso <- stepAIC(modelo_inicial, direction = "both", trace = FALSE)
summary(modelo_parsimonioso)##
## Call:
## lm(formula = TFGe ~ Edad + Creatinina, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.014 -5.019 3.236 9.523 67.811
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 156.43656 4.81785 32.47 <2e-16 ***
## Edad -0.95016 0.08016 -11.85 <2e-16 ***
## Creatinina -11.10541 0.48284 -23.00 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.01 on 317 degrees of freedom
## Multiple R-squared: 0.6921, Adjusted R-squared: 0.6902
## F-statistic: 356.3 on 2 and 317 DF, p-value: < 2.2e-16coef_table <- summary(modelo_parsimonioso)$coefficients
CI <- confint(modelo_parsimonioso)
resultados <- data.frame(
Variable = rownames(coef_table),
Coef = coef_table[,1],
Lower95 = CI[,1],
Upper95 = CI[,2],
p_values = coef_table[,4]
)
print("Resultados del modelo parsimonioso con TFGe como variable de respuesta:")## [1] "Resultados del modelo parsimonioso con TFGe como variable de respuesta:"print(resultados)## Variable Coef Lower95 Upper95 p_values
## (Intercept) (Intercept) 156.4365606 146.957556 165.9155657 7.752900e-103
## Edad Edad -0.9501645 -1.107871 -0.7924584 4.448406e-27
## Creatinina Creatinina -11.1054074 -12.055389 -10.1554262 1.519506e-69###El modelo parsimonioso resultante incluye solo Edad y Creatinina como predictores significativos de TFGe, lo cual indica que las demás variables no aportaban información relevante para la predicción.unique(data$RIESGO_CV_Dx_Framingham)## [1] BAJO ALTO MODERADO
## Levels: BAJO MODERADO ALTO# Crear la nueva variable combinada
data <- data %>%
mutate(Riesgo_Combinado = case_when(
DISFUNCIÓN_RENAL == 0 & RIESGO_CV_Dx_Framingham %in% c("BAJO", "MODERADO") ~ "SinDisfuncion_SinRCV",
DISFUNCIÓN_RENAL == 0 & RIESGO_CV_Dx_Framingham == "ALTO" ~ "SinDisfuncion_ConRCV",
DISFUNCIÓN_RENAL == 1 & RIESGO_CV_Dx_Framingham %in% c("BAJO", "MODERADO") ~ "ConDisfuncion_SinRCV",
DISFUNCIÓN_RENAL == 1 & RIESGO_CV_Dx_Framingham == "ALTO" ~ "ConDisfuncion_ConRCV",
TRUE ~ NA_character_ # Para manejar valores inesperados
))
# Convertir en factor con niveles ordenados
data$Riesgo_Combinado <- factor(data$Riesgo_Combinado,
levels = c("SinDisfuncion_SinRCV", "SinDisfuncion_ConRCV",
"ConDisfuncion_SinRCV", "ConDisfuncion_ConRCV"))
data$Riesgo_Combinado <- relevel(data$Riesgo_Combinado, ref = "SinDisfuncion_SinRCV")
levels(data$Riesgo_Combinado)## [1] "SinDisfuncion_SinRCV" "SinDisfuncion_ConRCV" "ConDisfuncion_SinRCV"
## [4] "ConDisfuncion_ConRCV"# Verificar la distribución de la nueva variable
table(data$Riesgo_Combinado, useNA = "always")##
## SinDisfuncion_SinRCV SinDisfuncion_ConRCV ConDisfuncion_SinRCV
## 185 84 17
## ConDisfuncion_ConRCV <NA>
## 34 0modelo_nulo <- multinom(Riesgo_Combinado ~ 1, data = data)## # weights: 8 (3 variable)
## initial value 443.614196
## iter 10 value 339.847436
## final value 339.847395
## convergedmodelo_multinom <- multinom(Riesgo_Combinado ~ ApoE + Edad + Sexo + HBA1c + IMC + Creatinina,
data = data)## # weights: 48 (33 variable)
## initial value 443.614196
## iter 10 value 297.016124
## iter 20 value 220.960783
## iter 30 value 188.914323
## iter 40 value 173.846961
## iter 50 value 173.020945
## iter 60 value 173.002654
## final value 173.002530
## convergedsummary(modelo_nulo)## Call:
## multinom(formula = Riesgo_Combinado ~ 1, data = data)
##
## Coefficients:
## (Intercept)
## SinDisfuncion_ConRCV -0.789539
## ConDisfuncion_SinRCV -2.387142
## ConDisfuncion_ConRCV -1.693995
##
## Std. Errors:
## (Intercept)
## SinDisfuncion_ConRCV 0.1315681
## ConDisfuncion_SinRCV 0.2534343
## ConDisfuncion_ConRCV 0.1865936
##
## Residual Deviance: 679.6948
## AIC: 685.6948summary(modelo_multinom)## Call:
## multinom(formula = Riesgo_Combinado ~ ApoE + Edad + Sexo + HBA1c +
## IMC + Creatinina, data = data)
##
## Coefficients:
## (Intercept) ApoEE2/E3 ApoEE2/E4 ApoEE3/E3 ApoEE3/E4
## SinDisfuncion_ConRCV -11.27056 -0.3147825 -1.079277 -0.8527057 -1.404732
## ConDisfuncion_SinRCV -22.58366 -27.1546513 -3.873265 -4.3213133 -2.791523
## ConDisfuncion_ConRCV -29.01262 -4.2099319 -2.946158 -3.3391152 -3.064102
## ApoEE4/E4 Edad SexoF HBA1c IMC
## SinDisfuncion_ConRCV -0.5585813 0.14546730 -0.8567108 0.1793499 0.06157167
## ConDisfuncion_SinRCV -31.6652447 0.09012073 4.5767337 -0.1400128 -0.02975089
## ConDisfuncion_ConRCV -3.6368206 0.21365175 4.3627902 -0.1060410 -0.09050612
## Creatinina
## SinDisfuncion_ConRCV -0.06594493
## ConDisfuncion_SinRCV 17.92032485
## ConDisfuncion_ConRCV 18.03858554
##
## Std. Errors:
## (Intercept) ApoEE2/E3 ApoEE2/E4 ApoEE3/E3 ApoEE3/E4
## SinDisfuncion_ConRCV 2.193357 1.146217e+00 1.276492 0.9700548 0.9998134
## ConDisfuncion_SinRCV 7.461464 1.712930e-08 2.991415 2.1172734 2.0397215
## ConDisfuncion_ConRCV 7.377621 3.080385e+00 3.030837 2.2583120 2.2322331
## ApoEE4/E4 Edad SexoF HBA1c IMC
## SinDisfuncion_ConRCV 1.106930e+00 0.02129847 0.3891885 0.08703264 0.03142896
## ConDisfuncion_SinRCV 3.667593e-11 0.05680744 1.4940365 0.37628520 0.09868710
## ConDisfuncion_ConRCV 2.677192e+00 0.05495667 1.4500735 0.34947955 0.09737431
## Creatinina
## SinDisfuncion_ConRCV 0.9722161
## ConDisfuncion_SinRCV 3.5777450
## ConDisfuncion_ConRCV 3.5758173
##
## Residual Deviance: 346.0051
## AIC: 412.0051###educción en la deviance (691.08 a 347.14) indica que los predictores mejoran el modelo OR_table <- exp(coef(modelo_multinom))
CI_lower <- exp(coef(modelo_multinom) - 1.96 * summary(modelo_multinom)$standard.errors)
CI_upper <- exp(coef(modelo_multinom) + 1.96 * summary(modelo_multinom)$standard.errors)
z_values <- coef(modelo_multinom) / summary(modelo_multinom)$standard.errors
p_values <- 2 * (1 - pnorm(abs(z_values)))
resultados <- data.frame(
Variable = rownames(OR_table),
OR = OR_table,
Lower95 = CI_lower,
Upper95 = CI_upper,
p_values = p_values
)
print(resultados)## Variable OR..Intercept. OR.ApoEE2.E3
## SinDisfuncion_ConRCV SinDisfuncion_ConRCV 1.274265e-05 7.299476e-01
## ConDisfuncion_SinRCV ConDisfuncion_SinRCV 1.556107e-10 1.610218e-12
## ConDisfuncion_ConRCV ConDisfuncion_ConRCV 2.511768e-13 1.484738e-02
## OR.ApoEE2.E4 OR.ApoEE3.E3 OR.ApoEE3.E4 OR.ApoEE4.E4
## SinDisfuncion_ConRCV 0.33984115 0.42626005 0.24543270 5.720200e-01
## ConDisfuncion_SinRCV 0.02079038 0.01328243 0.06132775 1.769942e-14
## ConDisfuncion_ConRCV 0.05254119 0.03546833 0.04669574 2.633594e-02
## OR.Edad OR.SexoF OR.HBA1c OR.IMC OR.Creatinina
## SinDisfuncion_ConRCV 1.156580 0.4245562 1.1964393 1.0635067 9.361824e-01
## ConDisfuncion_SinRCV 1.094306 97.1963981 0.8693471 0.9706873 6.063148e+07
## ConDisfuncion_ConRCV 1.238191 78.4757912 0.8993878 0.9134687 6.824301e+07
## Lower95..Intercept. Lower95.ApoEE2.E3 Lower95.ApoEE2.E4
## SinDisfuncion_ConRCV 1.730757e-07 7.719907e-02 2.784223e-02
## ConDisfuncion_SinRCV 6.929673e-17 1.610218e-12 5.909066e-05
## ConDisfuncion_ConRCV 1.318323e-19 3.544645e-05 1.382289e-04
## Lower95.ApoEE3.E3 Lower95.ApoEE3.E4 Lower95.ApoEE4.E4
## SinDisfuncion_ConRCV 0.0636718250 0.0345839085 6.533912e-02
## ConDisfuncion_SinRCV 0.0002094199 0.0011256698 1.769942e-14
## ConDisfuncion_ConRCV 0.0004241571 0.0005877086 1.385725e-04
## Lower95.Edad Lower95.SexoF Lower95.HBA1c Lower95.IMC
## SinDisfuncion_ConRCV 1.1092924 0.1979936 1.0088044 0.9999709
## ConDisfuncion_SinRCV 0.9790018 5.1987701 0.4158079 0.7999728
## ConDisfuncion_ConRCV 1.1117515 4.5751804 0.4533817 0.7547568
## Lower95.Creatinina Upper95..Intercept. Upper95.ApoEE2.E3
## SinDisfuncion_ConRCV 1.392494e-01 9.381738e-04 6.901942e+00
## ConDisfuncion_SinRCV 5.460849e+04 3.494348e-04 1.610218e-12
## ConDisfuncion_ConRCV 6.169657e+04 4.785608e-07 6.219089e+00
## Upper95.ApoEE2.E4 Upper95.ApoEE3.E3 Upper95.ApoEE3.E4
## SinDisfuncion_ConRCV 4.148088 2.8536583 1.741770
## ConDisfuncion_SinRCV 7.314859 0.8424362 3.341204
## ConDisfuncion_ConRCV 19.971046 2.9658877 3.710158
## Upper95.ApoEE4.E4 Upper95.Edad Upper95.SexoF Upper95.HBA1c
## SinDisfuncion_ConRCV 5.007825e+00 1.205883 0.9103727 1.418974
## ConDisfuncion_SinRCV 1.769942e-14 1.223191 1817.1874715 1.817580
## ConDisfuncion_ConRCV 5.005193e+00 1.379011 1346.0561743 1.784144
## Upper95.IMC Upper95.Creatinina p_values..Intercept.
## SinDisfuncion_ConRCV 1.131079 6.294012e+00 2.769479e-07
## ConDisfuncion_SinRCV 1.177832 6.731878e+10 2.472338e-03
## ConDisfuncion_ConRCV 1.105555 7.548406e+10 8.406103e-05
## p_values.ApoEE2.E3 p_values.ApoEE2.E4 p_values.ApoEE3.E3
## SinDisfuncion_ConRCV 0.7836026 0.3978304 0.37938591
## ConDisfuncion_SinRCV 0.0000000 0.1953914 0.04125279
## ConDisfuncion_ConRCV 0.1717225 0.3310204 0.13925021
## p_values.ApoEE3.E4 p_values.ApoEE4.E4 p_values.Edad
## SinDisfuncion_ConRCV 0.1600229 0.6138243 8.494982e-12
## ConDisfuncion_SinRCV 0.1711305 0.0000000 1.126429e-01
## ConDisfuncion_ConRCV 0.1698573 0.1743222 1.012236e-04
## p_values.SexoF p_values.HBA1c p_values.IMC
## SinDisfuncion_ConRCV 0.027716607 0.03932981 0.05010406
## ConDisfuncion_SinRCV 0.002188852 0.70982415 0.76305853
## ConDisfuncion_ConRCV 0.002623954 0.76156562 0.35264760
## p_values.Creatinina
## SinDisfuncion_ConRCV 9.459214e-01
## ConDisfuncion_SinRCV 5.476125e-07
## ConDisfuncion_ConRCV 4.544593e-07