#LIBRERIAS
library(funModeling)
## Loading required package: Hmisc
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
## Loading required package: ggplot2
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
##
## format.pval, units
## funModeling v.1.9.4 :)
## Examples and tutorials at livebook.datascienceheroes.com
## / Now in Spanish: librovivodecienciadedatos.ai
library(tidyverse)
## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.5.0
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ✔ purrr 1.0.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ dplyr::src() masks Hmisc::src()
## ✖ dplyr::summarize() masks Hmisc::summarize()
library(Hmisc)
library(tidyverse)
library(ggpubr)
library(gtable)
library(gt)
##
## Attaching package: 'gt'
##
## The following object is masked from 'package:Hmisc':
##
## html
library(survival)
library(ggpubr)
library(survminer)
##
## Attaching package: 'survminer'
##
## The following object is masked from 'package:survival':
##
## myeloma
library(readr)
library(gtsummary)
Análisis Viruela del Mono en Sinaloa
library(readxl)
Viruela <- read_excel("C:/Users/fidel/OneDrive - CINVESTAV/Proyecto colaboración SSA/base de datos/Viruelasimica/Viruela.xlsx")
dbvirsim <- Viruela
dbvirsim <- dbvirsim %>% filter(!is.na(RESDEFVS))
glimpse(dbvirsim)
## Rows: 38
## Columns: 246
## $ NUM_AFILIA_EXPED <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FOLIO <dbl> 90, 155, 311, 312, 460, 676, 835, 928, 1092…
## $ PRIMER_AP <chr> "GARCIA", "CASILLAS", "MEDINA", "PIÑA", "LE…
## $ SEGUNDO_AP <chr> "ESPINOZA", "MERCADO", "URIARTE", "GONZALEZ…
## $ NOMBRE <chr> "ARTURO", "SOPHIA ISABEL", "CARLOS EDEL", "…
## $ FEC_NAC <dttm> 1977-12-18, 2014-04-22, 2014-12-25, 2009-0…
## $ EDAD_A <dbl> 44, 8, 7, 13, 43, 27, 67, 42, 28, 32, 39, 3…
## $ EDAD_M <dbl> 9, 6, 10, 4, 9, 7, 7, 0, 0, 8, 4, 0, 10, 5,…
## $ EDAD_D <dbl> 13, 18, 13, 15, 11, 10, 13, 0, 0, 0, 16, 29…
## $ SEXO <dbl> 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ GENERO <chr> "Hombre", "Mujer", "Hombre", "Mujer", "Homb…
## $ ORIENT_SEX <chr> "Gay", "Heterosexual", "Heterosexual", "Het…
## $ ESTADO_NAC <dbl> 25, 25, 25, 25, 25, 25, 25, NA, NA, 25, 30,…
## $ DES_ESTADO_NAC <chr> "Sinaloa", "Sinaloa", "Sinaloa", "Sinaloa",…
## $ MUNIC_NAC <dbl> 6, 6, 6, 6, 6, 12, 6, NA, NA, 6, 69, 12, 12…
## $ DES_MUNIC_NAC <chr> "CULIACAN", "CULIACAN", "CULIACAN", "CULIAC…
## $ CURP <chr> "GAEA771218HSLRSR08", "CAMS140422MSLSRPA9",…
## $ CALLE <chr> "ESTERO DE CHAMETLA", "LOS TULES", "MONTE D…
## $ NUM_EX <chr> "3217", "1996", "5003", "94", "S/N", "SIN N…
## $ NUM_IN <chr> "S/N", "S/N", "S/N", "S/N", "S/N", "SIN NUM…
## $ COLONIA <chr> "PRADERA DORADA", NA, NA, NA, NA, NA, NA, N…
## $ ESTADO <dbl> 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,…
## $ DES_ESTADO <chr> "Sinaloa", "Sinaloa", "Sinaloa", "Sinaloa",…
## $ JURISDIC <dbl> 4, 4, 4, 4, 4, 5, 4, 6, 6, 4, 4, 5, 5, 5, 5…
## $ DES_JURISDIC <chr> "JURISDICCION SANITARIA IV CULIACAN", "JURI…
## $ MUNICIPIO <dbl> 6, 6, 6, 6, 6, 12, 6, 12, 12, 6, 6, 12, 12,…
## $ DES_MUNICIPIO <chr> "CULIACAN", "CULIACAN", "CULIACAN", "CULIAC…
## $ LOCALIDAD <dbl> 1, 1, 1, 1, 968, 1, 1, NA, NA, 1, 1, 1, 1, …
## $ DES_LOCALIDAD <chr> "CULIACÁN ROSALES", "CULIACÁN ROSALES", "CU…
## $ ENTRE_CALL <chr> "S/R", "S/R", "CIMA EVEREST Y CIMA ORIZABA"…
## $ Y_CALLE <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ C_P <dbl> 80058, 80058, 80295, 80190, 80450, 82089, 8…
## $ TEL <chr> "6672444166", "6673387097|", "6673503153", …
## $ RECON_INDIGENA <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ HABLA_LENG_IND <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ LENG_IND <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ DES_LENG_IND <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ OCUPACION <dbl> 10, 33, 12, 12, 33, 20, 9, NA, NA, 29, 33, …
## $ DES_OCUPACION <chr> "Empleados(as)", "Otros", "Estudiantes", "E…
## $ DIR_LABORAL <chr> NA, NA, NA, NA, NA, "EGA INDUSTRIAL ZONA NO…
## $ MIGRANTE <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
## $ NACIONALIDAD <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ PAIS_ORIGEN <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ PAIS_TRANSITO1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ PAIS_TRANSITO2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ PAIS_TRANSITO3 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_INGRESO_MEX <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ NOM_UNIDAD <chr> "UMF 36 CULIACAN", "HOSPITAL PEDIÁTRICO DE …
## $ ESTADO_UNIDAD <dbl> 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,…
## $ DES_ESTADO_UNIDAD <chr> "Sinaloa", "Sinaloa", "Sinaloa", "Sinaloa",…
## $ JURISD_UNIDAD <dbl> 4, 4, 4, 4, 4, 5, 4, 5, 5, 4, 4, 5, 5, 5, 5…
## $ DES_JURISD_UNIDAD <chr> "JURISDICCION SANITARIA IV CULIACAN", "JURI…
## $ CLUES <chr> "SLIMS000155", "SLSSA002556", "SLSSA002556"…
## $ MUN_UNIDAD <dbl> 6, 6, 6, 6, 6, 12, 6, 12, 12, 6, 6, 12, 12,…
## $ DES_MUN_UNIDAD <chr> "CULIACAN", "CULIACAN", "CULIACAN", "CULIAC…
## $ LOC_UNIDAD <dbl> 1, 1, 1, 1, 1194, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ DES_LOC_UNIDAD <chr> "CULIACÁN ROSALES", "CULIACÁN ROSALES", "CU…
## $ INST_UNIDAD <dbl> 2, 1, 1, 3, 1, 2, 1, 3, 3, 3, 1, 1, 2, 3, 1…
## $ DES_INST_UNIDAD <chr> "IMSS", "SSA", "SSA", "ISSSTE", "SSA", "IMS…
## $ FEC_PRIM_CONTACTO_SS <dttm> 2022-07-13, 2022-07-23, 2022-08-02, 2022-0…
## $ FEC_NOT_JUR <dttm> 2022-07-13, 2022-07-23, 2022-08-02, 2022-0…
## $ FEC_NOT_EST <dttm> 2022-07-13, 2022-07-27, 2022-08-04, 2022-0…
## $ FEC_NOT_DGE <dttm> 2022-07-13, 2022-07-27, 2022-08-04, 2022-0…
## $ FEC_INI_EST_EPI <dttm> 2022-07-13, 2022-07-23, 2022-08-02, 2022-0…
## $ VIAJE <dbl> 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2…
## $ PAIS_VIAJE1 <chr> "México", NA, NA, NA, NA, NA, NA, NA, NA, "…
## $ ESTADO_VIAJE1 <chr> "CIUDAD DE MEXICO", NA, NA, NA, NA, NA, NA,…
## $ MUN_VIAJE1 <chr> "CIUDAD DE MEXICO", NA, NA, NA, NA, NA, NA,…
## $ LOC_VIAJE1 <chr> "CIUDAD DE MEXICO", NA, NA, NA, NA, NA, NA,…
## $ FEC_ENT_VIAJE1 <dttm> 2022-07-23, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FEC_SAL_VIAJE1 <dttm> 2022-07-27, NA, NA, NA, NA, NA, NA, NA, NA…
## $ TIEMP_ESTANCIA1 <dbl> 4, 0, 0, 0, 0, 0, 0, NA, NA, 7, 1, 14, 1, 0…
## $ PAIS_VIAJE2 <chr> "México", NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ ESTADO_VIAJE2 <chr> "JALISCO", NA, NA, NA, NA, NA, NA, NA, NA, …
## $ MUN_VIAJE2 <chr> "GUADALAJARA", NA, NA, NA, NA, NA, NA, NA, …
## $ LOC_VIAJE2 <chr> "GUADALAJARA", NA, NA, NA, NA, NA, NA, NA, …
## $ FEC_ENT_VIAJE2 <dttm> 2022-06-27, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FEC_SAL_VIAJE2 <dttm> 2022-07-04, NA, NA, NA, NA, NA, NA, NA, NA…
## $ TIEMP_ESTANCIA2 <dbl> 8, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 6, 0,…
## $ PROCEDENCIA <chr> "De la Jurisdicción", "De la Jurisdicción",…
## $ ESTUVO <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 1, 1, 2,…
## $ CONFIRM_LABOR <dbl> NA, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2…
## $ FOLIO_CONFIRM <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CUAL_ENTORNO <chr> "Evento Masivo Sin Contacto Sexual", "Casa"…
## $ ESPECIFIQUE_LU <chr> NA, NA, "VISITA CASA ZONA RURAL", "VIA PUBL…
## $ ESPECIFIQUE_FE <dttm> NA, NA, 2022-07-22, NA, NA, NA, NA, NA, NA…
## $ CONT_MASC_DOMEST <dbl> 0, 0, 0, 0, 0, 1, 0, NA, NA, 0, 0, 0, 1, 1,…
## $ CONT_ROEDORES <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ CONT_ROED_SALV <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ CONT_ANIM_SALV <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ CONT_OTROS <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ CONT_ESP_OTROS <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ MEC_TRANSM <chr> "Persona a persona (Excepto las opciones an…
## $ ESPEC_MEC_TRANS <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FECHA_INI_SIGNOS <dttm> 2022-07-10, 2022-07-19, 2022-07-29, 2022-0…
## $ FECHA_INI_EXANT <dttm> 2022-07-10, 2022-07-19, 2022-07-29, 2022-0…
## $ EXA_MACULA <dbl> 1, 0, 0, 1, 0, 0, 0, NA, NA, 0, 0, 0, 1, 0,…
## $ EXA_PAPULA <dbl> 1, 1, 0, 1, 1, 1, 0, NA, NA, 0, 0, 1, 1, 1,…
## $ EXA_VESICULA <dbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1…
## $ EXA_PUSTULA <dbl> 0, 0, 0, 0, 1, 1, 0, NA, NA, 0, 0, 0, 1, 1,…
## $ EXA_COSTRA <dbl> 1, 1, 0, 0, 0, 1, 1, NA, NA, 0, 1, 0, 1, 1,…
## $ DISTRIB_EXA <chr> "Cefalocaudal", "Cefalocaudal", "Centrífuga…
## $ LEXAN_CABEZA <dbl> 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0…
## $ LEXAN_CARA <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0…
## $ LEXAN_CUELLO <dbl> 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0…
## $ LEXAN_TORAX <dbl> 1, 1, 0, 0, 1, 1, 1, NA, NA, 0, 1, 0, 0, 1,…
## $ LEXAN_MIEM_SUP <dbl> 0, 0, 1, 1, 1, 0, 0, 1, NA, 1, 1, 1, 1, 1, …
## $ LEXAN_MIEM_INF <dbl> 0, 0, 1, 1, 1, 1, 0, NA, 1, 0, 1, 0, 1, 1, …
## $ LEXAN_MUC_ORAL <dbl> 0, 0, 0, 0, 1, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ LEXAN_GENITALES <dbl> 0, 1, 0, 0, 1, 1, 0, 1, NA, 0, 0, 0, 1, 1, …
## $ LEXAN_ABDOMEN <dbl> 0, 0, 0, 0, 1, 0, 0, NA, NA, 0, 0, 1, 0, 1,…
## $ LEXAN_ESPALDA <dbl> 1, 0, 0, 0, 1, 0, 0, NA, NA, 1, 1, 0, 0, 1,…
## $ LEXAN_REG_PERI <dbl> 0, 0, 0, 0, 1, 0, 0, NA, NA, 0, 0, 0, 0, 1,…
## $ LEXAN_PLANTAS <dbl> 0, 0, 0, 0, 1, 0, 0, NA, NA, 0, 0, 0, 0, 1,…
## $ LEXAN_PALMAS <dbl> 0, 0, 0, 0, 1, 0, 0, NA, NA, 0, 0, 0, 1, 0,…
## $ FIEBRE <dbl> 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ CUANT_FIEBRE <dbl> 38.0, 38.7, 40.0, 0.0, 38.0, 38.0, 38.0, NA…
## $ FEC_INI_FIEBRE <dttm> 2022-07-10, 2022-07-18, 2022-07-29, NA, 20…
## $ CEFALEA <dbl> 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2…
## $ ARTRALGIAS <dbl> 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2…
## $ NAUSEA <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 1, 2, 2, 1, 2,…
## $ MIALGIAS <dbl> 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ LUMBALGIA <dbl> 2, 2, 2, 2, 2, 1, 2, NA, NA, 2, 2, 1, 1, 1,…
## $ VOMITO <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ ASTENIA <dbl> 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1…
## $ TOS <dbl> 2, 2, 2, 2, 2, 1, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ ODINOFAGIA <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 1, 2,…
## $ ESCALOFRIOS <dbl> 2, 2, 2, 2, 2, 1, 2, NA, 1, 2, 1, 2, 1, 1, …
## $ DIAFORESIS <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ CONJUNTIVITIS <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ SANGRANTES <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ DOLOROSAS <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ OTRAS_COMOR <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ LINF_AXILAR <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ LINF_CERVICAL <dbl> 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0…
## $ LINF_INGUINAL <dbl> 0, 0, 0, 0, 0, 0, 1, NA, NA, 0, 0, 1, 1, 0,…
## $ LINF_OTROS <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 1, 0,…
## $ LINF_ESP_OTROS <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ COMOR_DIABETES <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_NEOPLASIAS <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_HEPATITISC <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_GONORREA <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_CLAMIDIA <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_HERPES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_SIFILIS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_VERRUGAS <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_MICOPLASMA <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_TRICOMONIASIS <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_LINFOGRA <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_VIH <dbl> 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0…
## $ CD4 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ COMOR_OTRAS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_NINGUNA <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 1, 1,…
## $ COMOR_ESP_OTRAS1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ COMOR_ESP_OTRAS2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ COMOR_ESP_OTRAS3 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ EMBARAZO <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
## $ SDG <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ PUERPERIO <dbl> 0, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ FEC_TOMA_LES_CUT1 <dttm> 2022-07-13, 2022-07-26, 2022-08-02, 2022-0…
## $ FEC_ENV_LESP_LES_CUT1 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20…
## $ FEC_RECEP_LESP_LES_CUT1 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2022-0…
## $ FEC_ENV_INDRE_LES_CUT1 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FEC_RECEP_INDRE_LES_CUT1 <dttm> 2022-07-15, 2022-07-26, 2022-08-05, 2022-0…
## $ CAL_MTRA_LESP_LES_CUT1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, N…
## $ MOTIV_RECH_LESP_LES_CUT1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_LESP_LES_CUT1 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ CAL_MTRA_INDRE_LES_CUT1 <dbl> 1, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ MOTIV_RECH_INDRE_LES_CUT1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_INDRE_LES_CUT1 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FEC_RESUL_LES_CUT1 <dttm> 2022-07-16, 2022-07-28, 2022-08-06, 2022-0…
## $ RESULTADO_LES_CUT11 <chr> "Positivo a virus de viruela símica", "Nega…
## $ RESDEFVS <chr> "POSITIVO", "NEGATIVO", "NEGATIVO", "NEGATI…
## $ `POSITIVO OTRO` <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "VHH3",…
## $ RESULTADO_VIRUEL1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Positi…
## $ RESULTADO_HERPES1 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CLADO_LES_CUT1 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TECNICA_LES_CUT11 <dbl> 1, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ FEC_TOMA_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_ENV_LESP_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_LESP_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_ENV_INDRE_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_INDRE_LES_CUT2 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ CAL_MTRA_LESP_LES_CUT2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, N…
## $ MOTIV_RECH_LESP_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_LESP_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RESUL_LES_CUT2 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ RESULTADO_LES_CUT21 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ RESULTADO_LES_CUT22 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ RESULTADO_LES_CUT23 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CLADO_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TECNICA_LES_CUT21 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_TOMA_EX_FAR <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FEC_ENV_LESP_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_LESP_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_ENV_INDRE_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_INDRE_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CAL_MTRA_LESP_EX_FAR <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, N…
## $ MOTIV_RECH_LESP_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_LESP_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CAL_MTRA_INDRE_EX_FAR <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ MOTIV_RECH_INDRE_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_INDRE_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RESUL_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ RESULTADO_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CLADO_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TECNICA_LES_EX_FAR <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TIPO_BIOPSIA <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_TOMA_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_ENV_LESP_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_LESP_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_ENV_INDRE_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_INDRE_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CAL_MTRA_LESP_LIQ_CEFAL <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, N…
## $ MOTIV_RECH_LESP_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_LESP_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CAL_MTRA_INDRE_LIQ_CEFAL <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ MOTIV_RECH_INDRE_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_INDRE_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RESUL_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ RESULTADO_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CLADO_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TECNICA_LIQ_CEFAL <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ DES_LAB_PROC <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ HOSPITALIZADO <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2…
## $ FEC_HOSPITAL <dttm> NA, NA, NA, NA, NA, NA, 2022-08-17, NA, NA…
## $ CUID_INTENSIV <dbl> 2, 2, 2, 2, 0, 2, 2, NA, NA, 0, 2, 2, 2, 2,…
## $ TRATAMIENTO <dbl> 2, 2, 1, 2, 2, 1, 1, NA, NA, 2, 2, 1, 2, 1,…
## $ FEC_INI_TRATAM <dttm> NA, NA, 2022-08-02, NA, NA, 2022-08-03, 20…
## $ TIPO_TRATAM <dbl> 0, 0, 2, 0, 0, 1, 1, NA, NA, 0, 0, 2, 0, 1,…
## $ OTRO_ESPECIF_TRAT <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ SITUACION_ACTUAL <dbl> 3, 3, 3, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 3…
## $ FEC_ALTA <dttm> 2022-07-13, 2022-07-28, 2022-08-06, 2022-0…
## $ DEFUNCION <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ FEC_DEFUN <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ NUM_CONTACT_D <dbl> 4, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0…
## $ FEC_CLASIFICACION <dttm> 2022-07-16, 2022-07-27, 2022-08-06, 2022-0…
## $ DIAG_FINAL <dbl> 2, 3, 3, 3, 3, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2…
## $ OTRO_DIAG <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ OBSERVACIONES <chr> "EN ESTUDIO", NA, NA, NA, NA, NA, NA, NA, N…
## $ NOM_ELAB_EST_EPI <chr> "CISNEROS AVENDAÑO FRANCISCO ENRIQUE", "ROS…
## $ NOM_VALID <chr> "CISNEROS AVENDAÑO FRANCISCO ENRIQUE", "EPI…
## $ CARGO_VALID <chr> "EPIDEMIOLÓGIA", "PAULINA HERNANDEZ CRUZ", …
## $ TEL_VALID <chr> "6671765692", "6672381056", "6672381056", "…
## $ CVE_USU <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "1US012…
## $ FEC_CAP <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2022-0…
#definir variables
dbvirsim <- dbvirsim %>% mutate(SEXO=recode(SEXO, `1` = "Hombre",
`2` = "Mujer"))
##CURVAS
library(lubridate)
## Loading required package: timechange
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(epiR); library(ggplot2); library(scales); library(zoo)
## Package epiR 2.0.54 is loaded
## Type help(epi.about) for summary information
## Type browseVignettes(package = 'epiR') to learn how to use epiR for applied epidemiological analyses
##
##
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
##
## discard
## The following object is masked from 'package:readr':
##
## col_factor
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:Hmisc':
##
## subplot
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
ggplot(data = dbvirsim, aes(x = as.Date(FEC_INI_FIEBRE))) +
theme_bw() +
geom_histogram(binwidth = 2, colour = "#3e78d6", fill = "#3e78d6", size = 0.1)+
geom_density(aes(y = ..density.. * (nrow(dbvirsim) * 2)), colour = "red", size=0.8)+
scale_x_date(date_breaks = "1 week",labels = date_format("%d %b"),
name = "Date") +
scale_y_continuous(breaks = seq(from = 0, to =50, by = 1), name = "Number of cases") + theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## Warning: Removed 5 rows containing non-finite values (`stat_bin()`).
## Warning: Removed 5 rows containing non-finite values (`stat_density()`).
ggplot(data = dbvirsim, aes(x = as.Date(FEC_INI_FIEBRE))) +
theme_bw() +
geom_histogram(binwidth = 2, colour = "#3e78d6", fill = "#3e78d6", size = 0.1)+
geom_density(aes(y = ..density.. * (nrow(dbvirsim) * 2)), colour = "red", size=0.8)+
scale_x_date(date_breaks = "1 week",labels = date_format("%d %b"),
name = "Date") +
scale_y_continuous(breaks = seq(from = 0, to =50, by = 1), name = "Number of cases") + theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
## Warning: Removed 5 rows containing non-finite values (`stat_bin()`).
## Warning: Removed 5 rows containing non-finite values (`stat_density()`).
ggplot(data = dbvirsim, aes(x = as.Date(FEC_INI_FIEBRE), group=RESDEFVS, fill=RESDEFVS)) +
theme_bw() +
geom_histogram(binwidth = 7, colour = "black", size = 0.1)+
scale_x_date(date_breaks = "1 week",labels = date_format("%d %b"),
name = "Date") +
scale_y_continuous(breaks = seq(from = 0, to =50, by = 1), name = "Number of cases") + theme_classic()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+scale_fill_manual(values = c("red", "skyblue"), name = "Diagnostico")
## Warning: Removed 5 rows containing non-finite values (`stat_bin()`).
DEMOGRAFICOS
dbvirsim %>% select(RESDEFVS,SEXO,EDAD_A,ORIENT_SEX,MEC_TRANSM,HOSPITALIZADO,`POSITIVO OTRO`,RESULTADO_HERPES1,RESULTADO_VIRUEL1) %>% tbl_summary(by=RESDEFVS) %>% add_p() %>%
add_overall()
## Warning for variable 'EDAD_A':
## simpleWarning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot compute exact p-value with ties
## There was an error in 'add_p()/add_difference()' for variable 'POSITIVO OTRO', p-value omitted:
## Error in stats::fisher.test(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, "VHH3", : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'RESULTADO_HERPES1', p-value omitted:
## Error in stats::chisq.test(x = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, : 'x' and 'y' must have at least 2 levels
| Characteristic |
Overall, N = 38 |
NEGATIVO, N = 19 |
POSITIVO, N = 19 |
p-value |
| SEXO |
|
|
|
0.008 |
| Hombre |
31 (82%) |
12 (63%) |
19 (100%) |
|
| Mujer |
7 (18%) |
7 (37%) |
0 (0%) |
|
| EDAD_A |
34 (27, 40) |
32 (16, 42) |
35 (30, 40) |
0.3 |
| ORIENT_SEX |
|
|
|
<0.001 |
| Bisexual |
3 (7.9%) |
0 (0%) |
3 (16%) |
|
| Gay |
13 (34%) |
2 (11%) |
11 (58%) |
|
| Heterosexual |
20 (53%) |
17 (89%) |
3 (16%) |
|
| Hombres que tienen sexo con hombres |
2 (5.3%) |
0 (0%) |
2 (11%) |
|
| MEC_TRANSM |
|
|
|
0.016 |
| Desconocida |
14 (37%) |
9 (47%) |
5 (26%) |
|
| En los Servicios Salud |
1 (2.6%) |
1 (5.3%) |
0 (0%) |
|
| Persona a persona (Excepto las opciones anteriores) |
13 (34%) |
8 (42%) |
5 (26%) |
|
| Sexual |
10 (26%) |
1 (5.3%) |
9 (47%) |
|
| HOSPITALIZADO |
|
|
|
0.7 |
| 1 |
6 (16%) |
2 (11%) |
4 (21%) |
|
| 2 |
32 (84%) |
17 (89%) |
15 (79%) |
|
| POSITIVO OTRO |
|
|
|
|
| VHH3 |
3 (100%) |
3 (100%) |
0 (NA%) |
|
| Unknown |
35 |
16 |
19 |
|
| RESULTADO_HERPES1 |
0 (NA%) |
0 (NA%) |
0 (NA%) |
|
| Unknown |
38 |
19 |
19 |
|
| RESULTADO_VIRUEL1 |
|
|
|
>0.9 |
| Negativo a herpes virus 3 humano (varicela zóster) |
7 (70%) |
7 (70%) |
0 (NA%) |
|
| Positivo a herpes virus humano 3 (varicela zóster) |
3 (30%) |
3 (30%) |
0 (NA%) |
|
| Unknown |
28 |
9 |
19 |
|
dbvirsim %>% select(EXA_MACULA:FEC_TOMA_LES_CUT1,RESDEFVS) %>% tbl_summary(by=RESDEFVS) %>% add_p() %>%
add_overall()
## Warning for variable 'FEC_INI_FIEBRE':
## simpleWarning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot compute exact p-value with ties
## Warning for variable 'TOS':
## simpleWarning in stats::chisq.test(x = c(2, 2, 2, 2, 2, 1, 2, NA, NA, 2, 2, 2, : Chi-squared approximation may be incorrect
## There was an error in 'add_p()/add_difference()' for variable 'CONJUNTIVITIS', p-value omitted:
## Error in stats::chisq.test(x = c(2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_HEPATITISC', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_GONORREA', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_CLAMIDIA', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_HERPES', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_VERRUGAS', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_MICOPLASMA', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_TRICOMONIASIS', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_LINFOGRA', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_ESP_OTRAS1', p-value omitted:
## Error in stats::fisher.test(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_ESP_OTRAS2', p-value omitted:
## Error in stats::chisq.test(x = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_ESP_OTRAS3', p-value omitted:
## Error in stats::chisq.test(x = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'EMBARAZO', p-value omitted:
## Error in stats::chisq.test(x = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'SDG', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## Warning for variable 'FEC_TOMA_LES_CUT1':
## simpleWarning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot compute exact p-value with ties
| Characteristic |
Overall, N = 38 |
NEGATIVO, N = 19 |
POSITIVO, N = 19 |
p-value |
| EXA_MACULA |
19 (53%) |
9 (47%) |
10 (59%) |
0.5 |
| Unknown |
2 |
0 |
2 |
|
| EXA_PAPULA |
25 (69%) |
10 (53%) |
15 (88%) |
0.021 |
| Unknown |
2 |
0 |
2 |
|
| EXA_VESICULA |
33 (87%) |
16 (84%) |
17 (89%) |
>0.9 |
| EXA_PUSTULA |
20 (56%) |
8 (42%) |
12 (71%) |
0.086 |
| Unknown |
2 |
0 |
2 |
|
| EXA_COSTRA |
22 (61%) |
9 (47%) |
13 (76%) |
0.074 |
| Unknown |
2 |
0 |
2 |
|
| DISTRIB_EXA |
|
|
|
0.069 |
| Cefalocaudal |
18 (50%) |
12 (63%) |
6 (35%) |
|
| Centrífuga |
9 (25%) |
6 (32%) |
3 (18%) |
|
| Centrípeta |
2 (5.6%) |
0 (0%) |
2 (12%) |
|
| No Especificada |
3 (8.3%) |
0 (0%) |
3 (18%) |
|
| Otra |
1 (2.8%) |
0 (0%) |
1 (5.9%) |
|
| Simultánea |
3 (8.3%) |
1 (5.3%) |
2 (12%) |
|
| Unknown |
2 |
0 |
2 |
|
| LEXAN_CABEZA |
22 (58%) |
10 (53%) |
12 (63%) |
0.5 |
| LEXAN_CARA |
26 (68%) |
12 (63%) |
14 (74%) |
0.5 |
| LEXAN_CUELLO |
20 (53%) |
11 (58%) |
9 (47%) |
0.5 |
| LEXAN_TORAX |
23 (64%) |
10 (53%) |
13 (76%) |
0.14 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_MIEM_SUP |
30 (81%) |
17 (89%) |
13 (72%) |
0.2 |
| Unknown |
1 |
0 |
1 |
|
| LEXAN_MIEM_INF |
25 (68%) |
13 (68%) |
12 (67%) |
>0.9 |
| Unknown |
1 |
0 |
1 |
|
| LEXAN_MUC_ORAL |
3 (8.3%) |
2 (11%) |
1 (5.9%) |
>0.9 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_GENITALES |
13 (35%) |
5 (26%) |
8 (44%) |
0.2 |
| Unknown |
1 |
0 |
1 |
|
| LEXAN_ABDOMEN |
13 (36%) |
7 (37%) |
6 (35%) |
>0.9 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_ESPALDA |
18 (50%) |
11 (58%) |
7 (41%) |
0.3 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_REG_PERI |
4 (11%) |
2 (11%) |
2 (12%) |
>0.9 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_PLANTAS |
6 (17%) |
4 (21%) |
2 (12%) |
0.7 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_PALMAS |
13 (36%) |
6 (32%) |
7 (41%) |
0.5 |
| Unknown |
2 |
0 |
2 |
|
| FIEBRE |
|
|
|
0.046 |
| 1 |
33 (87%) |
14 (74%) |
19 (100%) |
|
| 2 |
5 (13%) |
5 (26%) |
0 (0%) |
|
| CUANT_FIEBRE |
|
|
|
0.053 |
| 0 |
5 (14%) |
5 (26%) |
0 (0%) |
|
| 38 |
21 (58%) |
8 (42%) |
13 (76%) |
|
| 38.3 |
1 (2.8%) |
1 (5.3%) |
0 (0%) |
|
| 38.5 |
1 (2.8%) |
0 (0%) |
1 (5.9%) |
|
| 38.7 |
1 (2.8%) |
1 (5.3%) |
0 (0%) |
|
| 39 |
6 (17%) |
3 (16%) |
3 (18%) |
|
| 40 |
1 (2.8%) |
1 (5.3%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| FEC_INI_FIEBRE |
2022-05-24 to 2022-11-23 |
2022-07-18 to 2022-11-12 |
2022-05-24 to 2022-11-23 |
>0.9 |
| Unknown |
5 |
5 |
0 |
|
| CEFALEA |
|
|
|
>0.9 |
| 1 |
29 (76%) |
14 (74%) |
15 (79%) |
|
| 2 |
9 (24%) |
5 (26%) |
4 (21%) |
|
| ARTRALGIAS |
|
|
|
0.10 |
| 1 |
21 (55%) |
8 (42%) |
13 (68%) |
|
| 2 |
17 (45%) |
11 (58%) |
6 (32%) |
|
| NAUSEA |
|
|
|
0.2 |
| 1 |
7 (19%) |
2 (11%) |
5 (29%) |
|
| 2 |
29 (81%) |
17 (89%) |
12 (71%) |
|
| Unknown |
2 |
0 |
2 |
|
| MIALGIAS |
|
|
|
0.2 |
| 1 |
26 (68%) |
11 (58%) |
15 (79%) |
|
| 2 |
12 (32%) |
8 (42%) |
4 (21%) |
|
| LUMBALGIA |
|
|
|
0.048 |
| 1 |
15 (42%) |
5 (26%) |
10 (59%) |
|
| 2 |
21 (58%) |
14 (74%) |
7 (41%) |
|
| Unknown |
2 |
0 |
2 |
|
| VOMITO |
|
|
|
0.5 |
| 1 |
1 (2.8%) |
0 (0%) |
1 (5.9%) |
|
| 2 |
35 (97%) |
19 (100%) |
16 (94%) |
|
| Unknown |
2 |
0 |
2 |
|
| ASTENIA |
|
|
|
0.009 |
| 1 |
22 (58%) |
7 (37%) |
15 (79%) |
|
| 2 |
16 (42%) |
12 (63%) |
4 (21%) |
|
| TOS |
|
|
|
0.3 |
| 1 |
10 (28%) |
4 (21%) |
6 (35%) |
|
| 2 |
26 (72%) |
15 (79%) |
11 (65%) |
|
| Unknown |
2 |
0 |
2 |
|
| ODINOFAGIA |
|
|
|
0.4 |
| 1 |
8 (22%) |
3 (16%) |
5 (29%) |
|
| 2 |
28 (78%) |
16 (84%) |
12 (71%) |
|
| Unknown |
2 |
0 |
2 |
|
| ESCALOFRIOS |
|
|
|
0.070 |
| 1 |
15 (41%) |
5 (26%) |
10 (56%) |
|
| 2 |
22 (59%) |
14 (74%) |
8 (44%) |
|
| Unknown |
1 |
0 |
1 |
|
| DIAFORESIS |
|
|
|
0.2 |
| 1 |
7 (19%) |
2 (11%) |
5 (29%) |
|
| 2 |
29 (81%) |
17 (89%) |
12 (71%) |
|
| Unknown |
2 |
0 |
2 |
|
| CONJUNTIVITIS |
|
|
|
|
| 2 |
36 (100%) |
19 (100%) |
17 (100%) |
|
| Unknown |
2 |
0 |
2 |
|
| SANGRANTES |
|
|
|
0.2 |
| 1 |
2 (5.6%) |
0 (0%) |
2 (12%) |
|
| 2 |
34 (94%) |
19 (100%) |
15 (88%) |
|
| Unknown |
2 |
0 |
2 |
|
| DOLOROSAS |
|
|
|
0.040 |
| 1 |
4 (11%) |
0 (0%) |
4 (24%) |
|
| 2 |
32 (89%) |
19 (100%) |
13 (76%) |
|
| Unknown |
2 |
0 |
2 |
|
| OTRAS_COMOR |
|
|
|
>0.9 |
| DOLOR ABDOMINAL |
1 (50%) |
0 (NA%) |
1 (50%) |
|
| NO |
1 (50%) |
0 (NA%) |
1 (50%) |
|
| Unknown |
36 |
19 |
17 |
|
| LINF_AXILAR |
4 (11%) |
0 (0%) |
4 (24%) |
0.040 |
| Unknown |
2 |
0 |
2 |
|
| LINF_CERVICAL |
12 (32%) |
4 (21%) |
8 (42%) |
0.2 |
| LINF_INGUINAL |
6 (17%) |
1 (5.3%) |
5 (29%) |
0.081 |
| Unknown |
2 |
0 |
2 |
|
| LINF_OTROS |
6 (17%) |
1 (5.3%) |
5 (29%) |
0.081 |
| Unknown |
2 |
0 |
2 |
|
| LINF_ESP_OTROS |
|
|
|
>0.9 |
| RETROAURICULAR |
1 (17%) |
0 (0%) |
1 (20%) |
|
| SUBMANDIBULAR |
5 (83%) |
1 (100%) |
4 (80%) |
|
| Unknown |
32 |
18 |
14 |
|
| COMOR_DIABETES |
2 (5.3%) |
1 (5.3%) |
1 (5.3%) |
>0.9 |
| COMOR_NEOPLASIAS |
1 (2.8%) |
0 (0%) |
1 (5.9%) |
0.5 |
| Unknown |
2 |
0 |
2 |
|
| COMOR_HEPATITISC |
0 (0%) |
0 (0%) |
0 (0%) |
|
| COMOR_GONORREA |
0 (0%) |
0 (0%) |
0 (0%) |
|
| COMOR_CLAMIDIA |
0 (0%) |
0 (0%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| COMOR_HERPES |
0 (0%) |
0 (0%) |
0 (0%) |
|
| COMOR_SIFILIS |
1 (2.6%) |
0 (0%) |
1 (5.3%) |
>0.9 |
| COMOR_VERRUGAS |
0 (0%) |
0 (0%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| COMOR_MICOPLASMA |
0 (0%) |
0 (0%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| COMOR_TRICOMONIASIS |
0 (0%) |
0 (0%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| COMOR_LINFOGRA |
0 (0%) |
0 (0%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| COMOR_VIH |
11 (29%) |
1 (5.3%) |
10 (53%) |
0.001 |
| CD4 |
|
|
|
>0.9 |
| 104 |
1 (25%) |
0 (NA%) |
1 (25%) |
|
| 119 |
1 (25%) |
0 (NA%) |
1 (25%) |
|
| SIN CONTEO |
1 (25%) |
0 (NA%) |
1 (25%) |
|
| SIN DATOS |
1 (25%) |
0 (NA%) |
1 (25%) |
|
| Unknown |
34 |
19 |
15 |
|
| COMOR_OTRAS |
2 (5.3%) |
2 (11%) |
0 (0%) |
0.5 |
| COMOR_NINGUNA |
6 (17%) |
2 (11%) |
4 (24%) |
0.4 |
| Unknown |
2 |
0 |
2 |
|
| COMOR_ESP_OTRAS1 |
|
|
|
|
| HIPERPLASI PROSTATICA BENIGNA |
1 (100%) |
1 (100%) |
0 (NA%) |
|
| Unknown |
37 |
18 |
19 |
|
| COMOR_ESP_OTRAS2 |
0 (NA%) |
0 (NA%) |
0 (NA%) |
|
| Unknown |
38 |
19 |
19 |
|
| COMOR_ESP_OTRAS3 |
0 (NA%) |
0 (NA%) |
0 (NA%) |
|
| Unknown |
38 |
19 |
19 |
|
| EMBARAZO |
|
|
|
|
| 2 |
38 (100%) |
19 (100%) |
19 (100%) |
|
| SDG |
0 (0%) |
0 (0%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| PUERPERIO |
|
|
|
0.6 |
| 0 |
3 (8.3%) |
1 (5.3%) |
2 (12%) |
|
| 2 |
33 (92%) |
18 (95%) |
15 (88%) |
|
| Unknown |
2 |
0 |
2 |
|
| FEC_TOMA_LES_CUT1 |
2022-07-13 to 2022-12-06 |
2022-07-26 to 2022-12-06 |
2022-07-13 to 2022-11-25 |
0.7 |
| Unknown |
2 |
0 |
2 |
|
dbvirsim
## # A tibble: 38 × 246
## NUM_A…¹ FOLIO PRIME…² SEGUN…³ NOMBRE FEC_NAC EDAD_A EDAD_M EDAD_D
## <chr> <dbl> <chr> <chr> <chr> <dttm> <dbl> <dbl> <dbl>
## 1 <NA> 90 GARCIA ESPINO… ARTURO 1977-12-18 00:00:00 44 9 13
## 2 <NA> 155 CASILL… MERCADO SOPHI… 2014-04-22 00:00:00 8 6 18
## 3 <NA> 311 MEDINA URIARTE CARLO… 2014-12-25 00:00:00 7 10 13
## 4 <NA> 312 PIÑA GONZAL… SOFIA… 2009-06-25 00:00:00 13 4 15
## 5 <NA> 460 LEON LOPEZ JOSE … 1979-01-27 00:00:00 43 9 11
## 6 <NA> 676 PUGA TORRES HECTO… 1995-03-30 00:00:00 27 7 10
## 7 <NA> 835 BALTAZ… LOPEZ FRANC… 1955-04-12 00:00:00 67 7 13
## 8 <NA> 928 PORTIL… CRUZ ALFON… NA 42 0 0
## 9 <NA> 1092 VALADEZ BENITEZ CARLO… NA 28 0 0
## 10 <NA> 1261 BEJARA… PEREZ GABRI… 1990-03-09 00:00:00 32 8 0
## # … with 28 more rows, 237 more variables: SEXO <chr>, GENERO <chr>,
## # ORIENT_SEX <chr>, ESTADO_NAC <dbl>, DES_ESTADO_NAC <chr>, MUNIC_NAC <dbl>,
## # DES_MUNIC_NAC <chr>, CURP <chr>, CALLE <chr>, NUM_EX <chr>, NUM_IN <chr>,
## # COLONIA <chr>, ESTADO <dbl>, DES_ESTADO <chr>, JURISDIC <dbl>,
## # DES_JURISDIC <chr>, MUNICIPIO <dbl>, DES_MUNICIPIO <chr>, LOCALIDAD <dbl>,
## # DES_LOCALIDAD <chr>, ENTRE_CALL <chr>, Y_CALLE <lgl>, C_P <dbl>, TEL <chr>,
## # RECON_INDIGENA <dbl>, HABLA_LENG_IND <dbl>, LENG_IND <dbl>, …
dbvirsim <- dbvirsim %>% mutate(status = ifelse(RESDEFVS == "POSITIVO", 1, 0))
dbvirsim$status
## [1] 1 0 0 0 0 1 0 1 1 0 0 1 1 1 1 0 0 0 1 1 1 1 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0
## EXANTEMA
dbvirsim %>%
select(EXA_MACULA:EXA_COSTRA, DISTRIB_EXA, RESDEFVS) %>% tbl_summary(by=RESDEFVS) %>% add_p() %>% add_overall()
| Characteristic |
Overall, N = 38 |
NEGATIVO, N = 19 |
POSITIVO, N = 19 |
p-value |
| EXA_MACULA |
19 (53%) |
9 (47%) |
10 (59%) |
0.5 |
| Unknown |
2 |
0 |
2 |
|
| EXA_PAPULA |
25 (69%) |
10 (53%) |
15 (88%) |
0.021 |
| Unknown |
2 |
0 |
2 |
|
| EXA_VESICULA |
33 (87%) |
16 (84%) |
17 (89%) |
>0.9 |
| EXA_PUSTULA |
20 (56%) |
8 (42%) |
12 (71%) |
0.086 |
| Unknown |
2 |
0 |
2 |
|
| EXA_COSTRA |
22 (61%) |
9 (47%) |
13 (76%) |
0.074 |
| Unknown |
2 |
0 |
2 |
|
| DISTRIB_EXA |
|
|
|
0.069 |
| Cefalocaudal |
18 (50%) |
12 (63%) |
6 (35%) |
|
| Centrífuga |
9 (25%) |
6 (32%) |
3 (18%) |
|
| Centrípeta |
2 (5.6%) |
0 (0%) |
2 (12%) |
|
| No Especificada |
3 (8.3%) |
0 (0%) |
3 (18%) |
|
| Otra |
1 (2.8%) |
0 (0%) |
1 (5.9%) |
|
| Simultánea |
3 (8.3%) |
1 (5.3%) |
2 (12%) |
|
| Unknown |
2 |
0 |
2 |
|
uvlm_tableEXA <- dbvirsim %>%
select(EXA_MACULA:EXA_COSTRA, DISTRIB_EXA, status) %>%
tbl_uvregression(
method = glm,
y = status,
method.args = list(family = binomial),
exponentiate = TRUE
)
uvlm_tableEXA
| Characteristic |
N |
OR |
95% CI |
p-value |
| EXA_MACULA |
36 |
1.59 |
0.43, 6.13 |
0.5 |
| EXA_PAPULA |
36 |
6.75 |
1.38, 50.8 |
0.030 |
| EXA_VESICULA |
38 |
1.59 |
0.23, 13.3 |
0.6 |
| EXA_PUSTULA |
36 |
3.30 |
0.86, 14.1 |
0.091 |
| EXA_COSTRA |
36 |
3.61 |
0.90, 16.7 |
0.080 |
| DISTRIB_EXA |
36 |
|
|
|
| Cefalocaudal |
|
— |
— |
|
| Centrífuga |
|
1.00 |
0.17, 5.37 |
>0.9 |
| Centrípeta |
|
231,297,586 |
0.00, NA |
>0.9 |
| No Especificada |
|
231,297,585 |
0.00, NA |
>0.9 |
| Otra |
|
231,297,585 |
0.00, NA |
>0.9 |
| Simultánea |
|
4.00 |
0.32, 96.9 |
0.3 |
## LOCALIZACICIÓN DE LA LESIO
dbvirsim %>%
select(LEXAN_CABEZA:LEXAN_PALMAS, RESDEFVS) %>% tbl_summary(by=RESDEFVS) %>% add_p() %>% add_overall()
| Characteristic |
Overall, N = 38 |
NEGATIVO, N = 19 |
POSITIVO, N = 19 |
p-value |
| LEXAN_CABEZA |
22 (58%) |
10 (53%) |
12 (63%) |
0.5 |
| LEXAN_CARA |
26 (68%) |
12 (63%) |
14 (74%) |
0.5 |
| LEXAN_CUELLO |
20 (53%) |
11 (58%) |
9 (47%) |
0.5 |
| LEXAN_TORAX |
23 (64%) |
10 (53%) |
13 (76%) |
0.14 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_MIEM_SUP |
30 (81%) |
17 (89%) |
13 (72%) |
0.2 |
| Unknown |
1 |
0 |
1 |
|
| LEXAN_MIEM_INF |
25 (68%) |
13 (68%) |
12 (67%) |
>0.9 |
| Unknown |
1 |
0 |
1 |
|
| LEXAN_MUC_ORAL |
3 (8.3%) |
2 (11%) |
1 (5.9%) |
>0.9 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_GENITALES |
13 (35%) |
5 (26%) |
8 (44%) |
0.2 |
| Unknown |
1 |
0 |
1 |
|
| LEXAN_ABDOMEN |
13 (36%) |
7 (37%) |
6 (35%) |
>0.9 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_ESPALDA |
18 (50%) |
11 (58%) |
7 (41%) |
0.3 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_REG_PERI |
4 (11%) |
2 (11%) |
2 (12%) |
>0.9 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_PLANTAS |
6 (17%) |
4 (21%) |
2 (12%) |
0.7 |
| Unknown |
2 |
0 |
2 |
|
| LEXAN_PALMAS |
13 (36%) |
6 (32%) |
7 (41%) |
0.5 |
| Unknown |
2 |
0 |
2 |
|
uvlm_tableLEXA <- dbvirsim %>%
select(LEXAN_CABEZA:LEXAN_PALMAS, status) %>%
tbl_uvregression(
method = glm,
y = status,
method.args = list(family = binomial),
exponentiate = TRUE
)
uvlm_tableLEXA
| Characteristic |
N |
OR |
95% CI |
p-value |
| LEXAN_CABEZA |
38 |
1.54 |
0.42, 5.81 |
0.5 |
| LEXAN_CARA |
38 |
1.63 |
0.41, 6.84 |
0.5 |
| LEXAN_CUELLO |
38 |
0.65 |
0.18, 2.35 |
0.5 |
| LEXAN_TORAX |
36 |
2.92 |
0.73, 13.5 |
0.14 |
| LEXAN_MIEM_SUP |
37 |
0.31 |
0.04, 1.67 |
0.2 |
| LEXAN_MIEM_INF |
37 |
0.92 |
0.23, 3.72 |
>0.9 |
| LEXAN_MUC_ORAL |
36 |
0.53 |
0.02, 6.08 |
0.6 |
| LEXAN_GENITALES |
37 |
2.24 |
0.57, 9.44 |
0.3 |
| LEXAN_ABDOMEN |
36 |
0.94 |
0.23, 3.69 |
>0.9 |
| LEXAN_ESPALDA |
36 |
0.51 |
0.13, 1.90 |
0.3 |
| LEXAN_REG_PERI |
36 |
1.13 |
0.12, 10.4 |
>0.9 |
| LEXAN_PLANTAS |
36 |
0.50 |
0.06, 2.97 |
0.5 |
| LEXAN_PALMAS |
36 |
1.52 |
0.39, 6.14 |
0.6 |
## CLINICA
dbvirsim %>%
select(FIEBRE:DOLOROSAS, RESDEFVS) %>% tbl_summary(by=RESDEFVS) %>% add_p() %>% add_overall()
## Warning for variable 'FEC_INI_FIEBRE':
## simpleWarning in wilcox.test.default(x = DATA[[1L]], y = DATA[[2L]], ...): cannot compute exact p-value with ties
## Warning for variable 'TOS':
## simpleWarning in stats::chisq.test(x = c(2, 2, 2, 2, 2, 1, 2, NA, NA, 2, 2, 2, : Chi-squared approximation may be incorrect
## There was an error in 'add_p()/add_difference()' for variable 'CONJUNTIVITIS', p-value omitted:
## Error in stats::chisq.test(x = c(2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, : 'x' and 'y' must have at least 2 levels
| Characteristic |
Overall, N = 38 |
NEGATIVO, N = 19 |
POSITIVO, N = 19 |
p-value |
| FIEBRE |
|
|
|
0.046 |
| 1 |
33 (87%) |
14 (74%) |
19 (100%) |
|
| 2 |
5 (13%) |
5 (26%) |
0 (0%) |
|
| CUANT_FIEBRE |
|
|
|
0.053 |
| 0 |
5 (14%) |
5 (26%) |
0 (0%) |
|
| 38 |
21 (58%) |
8 (42%) |
13 (76%) |
|
| 38.3 |
1 (2.8%) |
1 (5.3%) |
0 (0%) |
|
| 38.5 |
1 (2.8%) |
0 (0%) |
1 (5.9%) |
|
| 38.7 |
1 (2.8%) |
1 (5.3%) |
0 (0%) |
|
| 39 |
6 (17%) |
3 (16%) |
3 (18%) |
|
| 40 |
1 (2.8%) |
1 (5.3%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| FEC_INI_FIEBRE |
2022-05-24 to 2022-11-23 |
2022-07-18 to 2022-11-12 |
2022-05-24 to 2022-11-23 |
>0.9 |
| Unknown |
5 |
5 |
0 |
|
| CEFALEA |
|
|
|
>0.9 |
| 1 |
29 (76%) |
14 (74%) |
15 (79%) |
|
| 2 |
9 (24%) |
5 (26%) |
4 (21%) |
|
| ARTRALGIAS |
|
|
|
0.10 |
| 1 |
21 (55%) |
8 (42%) |
13 (68%) |
|
| 2 |
17 (45%) |
11 (58%) |
6 (32%) |
|
| NAUSEA |
|
|
|
0.2 |
| 1 |
7 (19%) |
2 (11%) |
5 (29%) |
|
| 2 |
29 (81%) |
17 (89%) |
12 (71%) |
|
| Unknown |
2 |
0 |
2 |
|
| MIALGIAS |
|
|
|
0.2 |
| 1 |
26 (68%) |
11 (58%) |
15 (79%) |
|
| 2 |
12 (32%) |
8 (42%) |
4 (21%) |
|
| LUMBALGIA |
|
|
|
0.048 |
| 1 |
15 (42%) |
5 (26%) |
10 (59%) |
|
| 2 |
21 (58%) |
14 (74%) |
7 (41%) |
|
| Unknown |
2 |
0 |
2 |
|
| VOMITO |
|
|
|
0.5 |
| 1 |
1 (2.8%) |
0 (0%) |
1 (5.9%) |
|
| 2 |
35 (97%) |
19 (100%) |
16 (94%) |
|
| Unknown |
2 |
0 |
2 |
|
| ASTENIA |
|
|
|
0.009 |
| 1 |
22 (58%) |
7 (37%) |
15 (79%) |
|
| 2 |
16 (42%) |
12 (63%) |
4 (21%) |
|
| TOS |
|
|
|
0.3 |
| 1 |
10 (28%) |
4 (21%) |
6 (35%) |
|
| 2 |
26 (72%) |
15 (79%) |
11 (65%) |
|
| Unknown |
2 |
0 |
2 |
|
| ODINOFAGIA |
|
|
|
0.4 |
| 1 |
8 (22%) |
3 (16%) |
5 (29%) |
|
| 2 |
28 (78%) |
16 (84%) |
12 (71%) |
|
| Unknown |
2 |
0 |
2 |
|
| ESCALOFRIOS |
|
|
|
0.070 |
| 1 |
15 (41%) |
5 (26%) |
10 (56%) |
|
| 2 |
22 (59%) |
14 (74%) |
8 (44%) |
|
| Unknown |
1 |
0 |
1 |
|
| DIAFORESIS |
|
|
|
0.2 |
| 1 |
7 (19%) |
2 (11%) |
5 (29%) |
|
| 2 |
29 (81%) |
17 (89%) |
12 (71%) |
|
| Unknown |
2 |
0 |
2 |
|
| CONJUNTIVITIS |
|
|
|
|
| 2 |
36 (100%) |
19 (100%) |
17 (100%) |
|
| Unknown |
2 |
0 |
2 |
|
| SANGRANTES |
|
|
|
0.2 |
| 1 |
2 (5.6%) |
0 (0%) |
2 (12%) |
|
| 2 |
34 (94%) |
19 (100%) |
15 (88%) |
|
| Unknown |
2 |
0 |
2 |
|
| DOLOROSAS |
|
|
|
0.040 |
| 1 |
4 (11%) |
0 (0%) |
4 (24%) |
|
| 2 |
32 (89%) |
19 (100%) |
13 (76%) |
|
| Unknown |
2 |
0 |
2 |
|
uvlm_tableCLIN <- dbvirsim %>%
select(FIEBRE:DOLOROSAS, status) %>%
tbl_uvregression(
method = glm,
y = status,
method.args = list(family = binomial),
exponentiate = TRUE
)
uvlm_tableCLIN
| Characteristic |
N |
OR |
95% CI |
p-value |
| FIEBRE |
38 |
0.00 |
|
>0.9 |
| CUANT_FIEBRE |
36 |
1.13 |
1.02, NA |
0.3 |
| FEC_INI_FIEBRE |
33 |
1.00 |
1.00, 1.00 |
>0.9 |
| CEFALEA |
38 |
0.75 |
0.16, 3.38 |
0.7 |
| ARTRALGIAS |
38 |
0.34 |
0.08, 1.23 |
0.11 |
| NAUSEA |
36 |
0.28 |
0.04, 1.55 |
0.2 |
| MIALGIAS |
38 |
0.37 |
0.08, 1.48 |
0.2 |
| LUMBALGIA |
36 |
0.25 |
0.06, 0.98 |
0.053 |
| VOMITO |
36 |
0.00 |
|
>0.9 |
| ASTENIA |
38 |
0.16 |
0.03, 0.62 |
0.012 |
| TOS |
36 |
0.49 |
0.10, 2.12 |
0.3 |
| ODINOFAGIA |
36 |
0.45 |
0.08, 2.20 |
0.3 |
| ESCALOFRIOS |
37 |
0.29 |
0.07, 1.10 |
0.075 |
| DIAFORESIS |
36 |
0.28 |
0.04, 1.55 |
0.2 |
| CONJUNTIVITIS |
36 |
|
|
|
| SANGRANTES |
36 |
0.00 |
|
>0.9 |
| DOLOROSAS |
36 |
0.00 |
|
>0.9 |
#LINFADENITIS
dbvirsim %>%
select(LINF_AXILAR:LINF_ESP_OTROS, RESDEFVS) %>% tbl_summary(by=RESDEFVS) %>% add_p() %>% add_overall()
| Characteristic |
Overall, N = 38 |
NEGATIVO, N = 19 |
POSITIVO, N = 19 |
p-value |
| LINF_AXILAR |
4 (11%) |
0 (0%) |
4 (24%) |
0.040 |
| Unknown |
2 |
0 |
2 |
|
| LINF_CERVICAL |
12 (32%) |
4 (21%) |
8 (42%) |
0.2 |
| LINF_INGUINAL |
6 (17%) |
1 (5.3%) |
5 (29%) |
0.081 |
| Unknown |
2 |
0 |
2 |
|
| LINF_OTROS |
6 (17%) |
1 (5.3%) |
5 (29%) |
0.081 |
| Unknown |
2 |
0 |
2 |
|
| LINF_ESP_OTROS |
|
|
|
>0.9 |
| RETROAURICULAR |
1 (17%) |
0 (0%) |
1 (20%) |
|
| SUBMANDIBULAR |
5 (83%) |
1 (100%) |
4 (80%) |
|
| Unknown |
32 |
18 |
14 |
|
uvlm_tableLINF <- dbvirsim %>%
select(LINF_AXILAR:LINF_ESP_OTROS, status) %>%
tbl_uvregression(
method = glm,
y = status,
method.args = list(family = binomial),
exponentiate = TRUE
)
uvlm_tableLINF
| Characteristic |
N |
OR |
95% CI |
p-value |
| LINF_AXILAR |
36 |
62,180,880 |
0.00, NA |
>0.9 |
| LINF_CERVICAL |
38 |
2.73 |
0.68, 12.5 |
0.2 |
| LINF_INGUINAL |
36 |
7.50 |
1.04, 153 |
0.082 |
| LINF_OTROS |
36 |
7.50 |
1.04, 153 |
0.082 |
| LINF_ESP_OTROS |
6 |
|
|
|
| RETROAURICULAR |
|
— |
— |
|
| SUBMANDIBULAR |
|
0.00 |
|
>0.9 |
#COMORBILIDADES
dbvirsim %>%
select(COMOR_DIABETES:COMOR_VIH,RESDEFVS) %>% tbl_summary(by=RESDEFVS) %>% add_p() %>% add_overall()
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_HEPATITISC', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_GONORREA', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_CLAMIDIA', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_HERPES', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_VERRUGAS', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_MICOPLASMA', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_TRICOMONIASIS', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
## There was an error in 'add_p()/add_difference()' for variable 'COMOR_LINFOGRA', p-value omitted:
## Error in stats::chisq.test(x = c(0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, : 'x' and 'y' must have at least 2 levels
| Characteristic |
Overall, N = 38 |
NEGATIVO, N = 19 |
POSITIVO, N = 19 |
p-value |
| COMOR_DIABETES |
2 (5.3%) |
1 (5.3%) |
1 (5.3%) |
>0.9 |
| COMOR_NEOPLASIAS |
1 (2.8%) |
0 (0%) |
1 (5.9%) |
0.5 |
| Unknown |
2 |
0 |
2 |
|
| COMOR_HEPATITISC |
0 (0%) |
0 (0%) |
0 (0%) |
|
| COMOR_GONORREA |
0 (0%) |
0 (0%) |
0 (0%) |
|
| COMOR_CLAMIDIA |
0 (0%) |
0 (0%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| COMOR_HERPES |
0 (0%) |
0 (0%) |
0 (0%) |
|
| COMOR_SIFILIS |
1 (2.6%) |
0 (0%) |
1 (5.3%) |
>0.9 |
| COMOR_VERRUGAS |
0 (0%) |
0 (0%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| COMOR_MICOPLASMA |
0 (0%) |
0 (0%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| COMOR_TRICOMONIASIS |
0 (0%) |
0 (0%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| COMOR_LINFOGRA |
0 (0%) |
0 (0%) |
0 (0%) |
|
| Unknown |
2 |
0 |
2 |
|
| COMOR_VIH |
11 (29%) |
1 (5.3%) |
10 (53%) |
0.001 |
vlm_tableCOMORBO <- dbvirsim %>%
select(COMOR_DIABETES:COMOR_VIH, status) %>%
tbl_uvregression(
method = glm,
y = status,
method.args = list(family = binomial),
exponentiate = TRUE
)
vlm_tableCOMORBO
| Characteristic |
N |
OR |
95% CI |
p-value |
| COMOR_DIABETES |
38 |
1.00 |
0.04, 26.6 |
>0.9 |
| COMOR_NEOPLASIAS |
36 |
18,585,991 |
0.00, NA |
>0.9 |
| COMOR_HEPATITISC |
38 |
|
|
|
| COMOR_GONORREA |
38 |
|
|
|
| COMOR_CLAMIDIA |
36 |
|
|
|
| COMOR_HERPES |
38 |
|
|
|
| COMOR_SIFILIS |
38 |
16,520,881 |
0.00, NA |
>0.9 |
| COMOR_VERRUGAS |
36 |
|
|
|
| COMOR_MICOPLASMA |
36 |
|
|
|
| COMOR_TRICOMONIASIS |
36 |
|
|
|
| COMOR_LINFOGRA |
36 |
|
|
|
| COMOR_VIH |
38 |
20.0 |
3.12, 398 |
0.008 |
glimpse(dbvirsim)
## Rows: 38
## Columns: 247
## $ NUM_AFILIA_EXPED <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FOLIO <dbl> 90, 155, 311, 312, 460, 676, 835, 928, 1092…
## $ PRIMER_AP <chr> "GARCIA", "CASILLAS", "MEDINA", "PIÑA", "LE…
## $ SEGUNDO_AP <chr> "ESPINOZA", "MERCADO", "URIARTE", "GONZALEZ…
## $ NOMBRE <chr> "ARTURO", "SOPHIA ISABEL", "CARLOS EDEL", "…
## $ FEC_NAC <dttm> 1977-12-18, 2014-04-22, 2014-12-25, 2009-0…
## $ EDAD_A <dbl> 44, 8, 7, 13, 43, 27, 67, 42, 28, 32, 39, 3…
## $ EDAD_M <dbl> 9, 6, 10, 4, 9, 7, 7, 0, 0, 8, 4, 0, 10, 5,…
## $ EDAD_D <dbl> 13, 18, 13, 15, 11, 10, 13, 0, 0, 0, 16, 29…
## $ SEXO <chr> "Hombre", "Mujer", "Hombre", "Mujer", "Homb…
## $ GENERO <chr> "Hombre", "Mujer", "Hombre", "Mujer", "Homb…
## $ ORIENT_SEX <chr> "Gay", "Heterosexual", "Heterosexual", "Het…
## $ ESTADO_NAC <dbl> 25, 25, 25, 25, 25, 25, 25, NA, NA, 25, 30,…
## $ DES_ESTADO_NAC <chr> "Sinaloa", "Sinaloa", "Sinaloa", "Sinaloa",…
## $ MUNIC_NAC <dbl> 6, 6, 6, 6, 6, 12, 6, NA, NA, 6, 69, 12, 12…
## $ DES_MUNIC_NAC <chr> "CULIACAN", "CULIACAN", "CULIACAN", "CULIAC…
## $ CURP <chr> "GAEA771218HSLRSR08", "CAMS140422MSLSRPA9",…
## $ CALLE <chr> "ESTERO DE CHAMETLA", "LOS TULES", "MONTE D…
## $ NUM_EX <chr> "3217", "1996", "5003", "94", "S/N", "SIN N…
## $ NUM_IN <chr> "S/N", "S/N", "S/N", "S/N", "S/N", "SIN NUM…
## $ COLONIA <chr> "PRADERA DORADA", NA, NA, NA, NA, NA, NA, N…
## $ ESTADO <dbl> 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,…
## $ DES_ESTADO <chr> "Sinaloa", "Sinaloa", "Sinaloa", "Sinaloa",…
## $ JURISDIC <dbl> 4, 4, 4, 4, 4, 5, 4, 6, 6, 4, 4, 5, 5, 5, 5…
## $ DES_JURISDIC <chr> "JURISDICCION SANITARIA IV CULIACAN", "JURI…
## $ MUNICIPIO <dbl> 6, 6, 6, 6, 6, 12, 6, 12, 12, 6, 6, 12, 12,…
## $ DES_MUNICIPIO <chr> "CULIACAN", "CULIACAN", "CULIACAN", "CULIAC…
## $ LOCALIDAD <dbl> 1, 1, 1, 1, 968, 1, 1, NA, NA, 1, 1, 1, 1, …
## $ DES_LOCALIDAD <chr> "CULIACÁN ROSALES", "CULIACÁN ROSALES", "CU…
## $ ENTRE_CALL <chr> "S/R", "S/R", "CIMA EVEREST Y CIMA ORIZABA"…
## $ Y_CALLE <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ C_P <dbl> 80058, 80058, 80295, 80190, 80450, 82089, 8…
## $ TEL <chr> "6672444166", "6673387097|", "6673503153", …
## $ RECON_INDIGENA <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ HABLA_LENG_IND <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ LENG_IND <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ DES_LENG_IND <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ OCUPACION <dbl> 10, 33, 12, 12, 33, 20, 9, NA, NA, 29, 33, …
## $ DES_OCUPACION <chr> "Empleados(as)", "Otros", "Estudiantes", "E…
## $ DIR_LABORAL <chr> NA, NA, NA, NA, NA, "EGA INDUSTRIAL ZONA NO…
## $ MIGRANTE <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
## $ NACIONALIDAD <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ PAIS_ORIGEN <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ PAIS_TRANSITO1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ PAIS_TRANSITO2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ PAIS_TRANSITO3 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_INGRESO_MEX <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ NOM_UNIDAD <chr> "UMF 36 CULIACAN", "HOSPITAL PEDIÁTRICO DE …
## $ ESTADO_UNIDAD <dbl> 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,…
## $ DES_ESTADO_UNIDAD <chr> "Sinaloa", "Sinaloa", "Sinaloa", "Sinaloa",…
## $ JURISD_UNIDAD <dbl> 4, 4, 4, 4, 4, 5, 4, 5, 5, 4, 4, 5, 5, 5, 5…
## $ DES_JURISD_UNIDAD <chr> "JURISDICCION SANITARIA IV CULIACAN", "JURI…
## $ CLUES <chr> "SLIMS000155", "SLSSA002556", "SLSSA002556"…
## $ MUN_UNIDAD <dbl> 6, 6, 6, 6, 6, 12, 6, 12, 12, 6, 6, 12, 12,…
## $ DES_MUN_UNIDAD <chr> "CULIACAN", "CULIACAN", "CULIACAN", "CULIAC…
## $ LOC_UNIDAD <dbl> 1, 1, 1, 1, 1194, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ DES_LOC_UNIDAD <chr> "CULIACÁN ROSALES", "CULIACÁN ROSALES", "CU…
## $ INST_UNIDAD <dbl> 2, 1, 1, 3, 1, 2, 1, 3, 3, 3, 1, 1, 2, 3, 1…
## $ DES_INST_UNIDAD <chr> "IMSS", "SSA", "SSA", "ISSSTE", "SSA", "IMS…
## $ FEC_PRIM_CONTACTO_SS <dttm> 2022-07-13, 2022-07-23, 2022-08-02, 2022-0…
## $ FEC_NOT_JUR <dttm> 2022-07-13, 2022-07-23, 2022-08-02, 2022-0…
## $ FEC_NOT_EST <dttm> 2022-07-13, 2022-07-27, 2022-08-04, 2022-0…
## $ FEC_NOT_DGE <dttm> 2022-07-13, 2022-07-27, 2022-08-04, 2022-0…
## $ FEC_INI_EST_EPI <dttm> 2022-07-13, 2022-07-23, 2022-08-02, 2022-0…
## $ VIAJE <dbl> 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2…
## $ PAIS_VIAJE1 <chr> "México", NA, NA, NA, NA, NA, NA, NA, NA, "…
## $ ESTADO_VIAJE1 <chr> "CIUDAD DE MEXICO", NA, NA, NA, NA, NA, NA,…
## $ MUN_VIAJE1 <chr> "CIUDAD DE MEXICO", NA, NA, NA, NA, NA, NA,…
## $ LOC_VIAJE1 <chr> "CIUDAD DE MEXICO", NA, NA, NA, NA, NA, NA,…
## $ FEC_ENT_VIAJE1 <dttm> 2022-07-23, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FEC_SAL_VIAJE1 <dttm> 2022-07-27, NA, NA, NA, NA, NA, NA, NA, NA…
## $ TIEMP_ESTANCIA1 <dbl> 4, 0, 0, 0, 0, 0, 0, NA, NA, 7, 1, 14, 1, 0…
## $ PAIS_VIAJE2 <chr> "México", NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ ESTADO_VIAJE2 <chr> "JALISCO", NA, NA, NA, NA, NA, NA, NA, NA, …
## $ MUN_VIAJE2 <chr> "GUADALAJARA", NA, NA, NA, NA, NA, NA, NA, …
## $ LOC_VIAJE2 <chr> "GUADALAJARA", NA, NA, NA, NA, NA, NA, NA, …
## $ FEC_ENT_VIAJE2 <dttm> 2022-06-27, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FEC_SAL_VIAJE2 <dttm> 2022-07-04, NA, NA, NA, NA, NA, NA, NA, NA…
## $ TIEMP_ESTANCIA2 <dbl> 8, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 6, 0,…
## $ PROCEDENCIA <chr> "De la Jurisdicción", "De la Jurisdicción",…
## $ ESTUVO <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 1, 1, 2,…
## $ CONFIRM_LABOR <dbl> NA, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2…
## $ FOLIO_CONFIRM <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CUAL_ENTORNO <chr> "Evento Masivo Sin Contacto Sexual", "Casa"…
## $ ESPECIFIQUE_LU <chr> NA, NA, "VISITA CASA ZONA RURAL", "VIA PUBL…
## $ ESPECIFIQUE_FE <dttm> NA, NA, 2022-07-22, NA, NA, NA, NA, NA, NA…
## $ CONT_MASC_DOMEST <dbl> 0, 0, 0, 0, 0, 1, 0, NA, NA, 0, 0, 0, 1, 1,…
## $ CONT_ROEDORES <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ CONT_ROED_SALV <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ CONT_ANIM_SALV <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ CONT_OTROS <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ CONT_ESP_OTROS <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ MEC_TRANSM <chr> "Persona a persona (Excepto las opciones an…
## $ ESPEC_MEC_TRANS <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FECHA_INI_SIGNOS <dttm> 2022-07-10, 2022-07-19, 2022-07-29, 2022-0…
## $ FECHA_INI_EXANT <dttm> 2022-07-10, 2022-07-19, 2022-07-29, 2022-0…
## $ EXA_MACULA <dbl> 1, 0, 0, 1, 0, 0, 0, NA, NA, 0, 0, 0, 1, 0,…
## $ EXA_PAPULA <dbl> 1, 1, 0, 1, 1, 1, 0, NA, NA, 0, 0, 1, 1, 1,…
## $ EXA_VESICULA <dbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1…
## $ EXA_PUSTULA <dbl> 0, 0, 0, 0, 1, 1, 0, NA, NA, 0, 0, 0, 1, 1,…
## $ EXA_COSTRA <dbl> 1, 1, 0, 0, 0, 1, 1, NA, NA, 0, 1, 0, 1, 1,…
## $ DISTRIB_EXA <chr> "Cefalocaudal", "Cefalocaudal", "Centrífuga…
## $ LEXAN_CABEZA <dbl> 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0…
## $ LEXAN_CARA <dbl> 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0…
## $ LEXAN_CUELLO <dbl> 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0…
## $ LEXAN_TORAX <dbl> 1, 1, 0, 0, 1, 1, 1, NA, NA, 0, 1, 0, 0, 1,…
## $ LEXAN_MIEM_SUP <dbl> 0, 0, 1, 1, 1, 0, 0, 1, NA, 1, 1, 1, 1, 1, …
## $ LEXAN_MIEM_INF <dbl> 0, 0, 1, 1, 1, 1, 0, NA, 1, 0, 1, 0, 1, 1, …
## $ LEXAN_MUC_ORAL <dbl> 0, 0, 0, 0, 1, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ LEXAN_GENITALES <dbl> 0, 1, 0, 0, 1, 1, 0, 1, NA, 0, 0, 0, 1, 1, …
## $ LEXAN_ABDOMEN <dbl> 0, 0, 0, 0, 1, 0, 0, NA, NA, 0, 0, 1, 0, 1,…
## $ LEXAN_ESPALDA <dbl> 1, 0, 0, 0, 1, 0, 0, NA, NA, 1, 1, 0, 0, 1,…
## $ LEXAN_REG_PERI <dbl> 0, 0, 0, 0, 1, 0, 0, NA, NA, 0, 0, 0, 0, 1,…
## $ LEXAN_PLANTAS <dbl> 0, 0, 0, 0, 1, 0, 0, NA, NA, 0, 0, 0, 0, 1,…
## $ LEXAN_PALMAS <dbl> 0, 0, 0, 0, 1, 0, 0, NA, NA, 0, 0, 0, 1, 0,…
## $ FIEBRE <dbl> 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ CUANT_FIEBRE <dbl> 38.0, 38.7, 40.0, 0.0, 38.0, 38.0, 38.0, NA…
## $ FEC_INI_FIEBRE <dttm> 2022-07-10, 2022-07-18, 2022-07-29, NA, 20…
## $ CEFALEA <dbl> 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2…
## $ ARTRALGIAS <dbl> 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2…
## $ NAUSEA <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 1, 2, 2, 1, 2,…
## $ MIALGIAS <dbl> 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ LUMBALGIA <dbl> 2, 2, 2, 2, 2, 1, 2, NA, NA, 2, 2, 1, 1, 1,…
## $ VOMITO <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ ASTENIA <dbl> 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1…
## $ TOS <dbl> 2, 2, 2, 2, 2, 1, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ ODINOFAGIA <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 1, 2,…
## $ ESCALOFRIOS <dbl> 2, 2, 2, 2, 2, 1, 2, NA, 1, 2, 1, 2, 1, 1, …
## $ DIAFORESIS <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ CONJUNTIVITIS <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ SANGRANTES <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ DOLOROSAS <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ OTRAS_COMOR <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ LINF_AXILAR <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ LINF_CERVICAL <dbl> 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0…
## $ LINF_INGUINAL <dbl> 0, 0, 0, 0, 0, 0, 1, NA, NA, 0, 0, 1, 1, 0,…
## $ LINF_OTROS <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 1, 0,…
## $ LINF_ESP_OTROS <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ COMOR_DIABETES <dbl> 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_NEOPLASIAS <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_HEPATITISC <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_GONORREA <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_CLAMIDIA <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_HERPES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_SIFILIS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_VERRUGAS <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_MICOPLASMA <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_TRICOMONIASIS <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_LINFOGRA <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ COMOR_VIH <dbl> 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0…
## $ CD4 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ COMOR_OTRAS <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ COMOR_NINGUNA <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 1, 1,…
## $ COMOR_ESP_OTRAS1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ COMOR_ESP_OTRAS2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ COMOR_ESP_OTRAS3 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ EMBARAZO <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
## $ SDG <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ PUERPERIO <dbl> 0, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ FEC_TOMA_LES_CUT1 <dttm> 2022-07-13, 2022-07-26, 2022-08-02, 2022-0…
## $ FEC_ENV_LESP_LES_CUT1 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20…
## $ FEC_RECEP_LESP_LES_CUT1 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2022-0…
## $ FEC_ENV_INDRE_LES_CUT1 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FEC_RECEP_INDRE_LES_CUT1 <dttm> 2022-07-15, 2022-07-26, 2022-08-05, 2022-0…
## $ CAL_MTRA_LESP_LES_CUT1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, N…
## $ MOTIV_RECH_LESP_LES_CUT1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_LESP_LES_CUT1 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ CAL_MTRA_INDRE_LES_CUT1 <dbl> 1, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ MOTIV_RECH_INDRE_LES_CUT1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_INDRE_LES_CUT1 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FEC_RESUL_LES_CUT1 <dttm> 2022-07-16, 2022-07-28, 2022-08-06, 2022-0…
## $ RESULTADO_LES_CUT11 <chr> "Positivo a virus de viruela símica", "Nega…
## $ RESDEFVS <chr> "POSITIVO", "NEGATIVO", "NEGATIVO", "NEGATI…
## $ `POSITIVO OTRO` <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "VHH3",…
## $ RESULTADO_VIRUEL1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Positi…
## $ RESULTADO_HERPES1 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CLADO_LES_CUT1 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TECNICA_LES_CUT11 <dbl> 1, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ FEC_TOMA_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_ENV_LESP_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_LESP_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_ENV_INDRE_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_INDRE_LES_CUT2 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ CAL_MTRA_LESP_LES_CUT2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, N…
## $ MOTIV_RECH_LESP_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_LESP_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RESUL_LES_CUT2 <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ RESULTADO_LES_CUT21 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ RESULTADO_LES_CUT22 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ RESULTADO_LES_CUT23 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CLADO_LES_CUT2 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TECNICA_LES_CUT21 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_TOMA_EX_FAR <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ FEC_ENV_LESP_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_LESP_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_ENV_INDRE_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_INDRE_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CAL_MTRA_LESP_EX_FAR <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, N…
## $ MOTIV_RECH_LESP_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_LESP_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CAL_MTRA_INDRE_EX_FAR <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ MOTIV_RECH_INDRE_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_INDRE_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RESUL_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ RESULTADO_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CLADO_EX_FAR <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TECNICA_LES_EX_FAR <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TIPO_BIOPSIA <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_TOMA_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_ENV_LESP_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_LESP_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_ENV_INDRE_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECEP_INDRE_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CAL_MTRA_LESP_LIQ_CEFAL <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, N…
## $ MOTIV_RECH_LESP_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_LESP_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CAL_MTRA_INDRE_LIQ_CEFAL <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ MOTIV_RECH_INDRE_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RECH_INDRE_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ FEC_RESUL_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ RESULTADO_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CLADO_LIQ_CEFAL <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TECNICA_LIQ_CEFAL <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ DES_LAB_PROC <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ HOSPITALIZADO <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2…
## $ FEC_HOSPITAL <dttm> NA, NA, NA, NA, NA, NA, 2022-08-17, NA, NA…
## $ CUID_INTENSIV <dbl> 2, 2, 2, 2, 0, 2, 2, NA, NA, 0, 2, 2, 2, 2,…
## $ TRATAMIENTO <dbl> 2, 2, 1, 2, 2, 1, 1, NA, NA, 2, 2, 1, 2, 1,…
## $ FEC_INI_TRATAM <dttm> NA, NA, 2022-08-02, NA, NA, 2022-08-03, 20…
## $ TIPO_TRATAM <dbl> 0, 0, 2, 0, 0, 1, 1, NA, NA, 0, 0, 2, 0, 1,…
## $ OTRO_ESPECIF_TRAT <dbl> 0, 0, 0, 0, 0, 0, 0, NA, NA, 0, 0, 0, 0, 0,…
## $ SITUACION_ACTUAL <dbl> 3, 3, 3, 3, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 3…
## $ FEC_ALTA <dttm> 2022-07-13, 2022-07-28, 2022-08-06, 2022-0…
## $ DEFUNCION <dbl> 2, 2, 2, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, 2,…
## $ FEC_DEFUN <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ NUM_CONTACT_D <dbl> 4, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0…
## $ FEC_CLASIFICACION <dttm> 2022-07-16, 2022-07-27, 2022-08-06, 2022-0…
## $ DIAG_FINAL <dbl> 2, 3, 3, 3, 3, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2…
## $ OTRO_DIAG <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ OBSERVACIONES <chr> "EN ESTUDIO", NA, NA, NA, NA, NA, NA, NA, N…
## $ NOM_ELAB_EST_EPI <chr> "CISNEROS AVENDAÑO FRANCISCO ENRIQUE", "ROS…
## $ NOM_VALID <chr> "CISNEROS AVENDAÑO FRANCISCO ENRIQUE", "EPI…
## $ CARGO_VALID <chr> "EPIDEMIOLÓGIA", "PAULINA HERNANDEZ CRUZ", …
## $ TEL_VALID <chr> "6671765692", "6672381056", "6672381056", "…
## $ CVE_USU <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, "1US012…
## $ FEC_CAP <dttm> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2022-0…
## $ status <dbl> 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1…
MODELO CONSTRUIDO CON VARIABLES SIGNIFICATIVAS
dbvirsim %>% select(GENERO, ORIENT_SEX, MEC_TRANSM,FIEBRE,LUMBALGIA,ASTENIA,DOLOROSAS,COMOR_VIH, EXA_PAPULA,LINF_AXILAR,RESDEFVS) %>% tbl_summary(by=RESDEFVS) %>% add_p() %>% add_overall()
| Characteristic |
Overall, N = 38 |
NEGATIVO, N = 19 |
POSITIVO, N = 19 |
p-value |
| GENERO |
|
|
|
0.008 |
| Hombre |
31 (82%) |
12 (63%) |
19 (100%) |
|
| Mujer |
7 (18%) |
7 (37%) |
0 (0%) |
|
| ORIENT_SEX |
|
|
|
<0.001 |
| Bisexual |
3 (7.9%) |
0 (0%) |
3 (16%) |
|
| Gay |
13 (34%) |
2 (11%) |
11 (58%) |
|
| Heterosexual |
20 (53%) |
17 (89%) |
3 (16%) |
|
| Hombres que tienen sexo con hombres |
2 (5.3%) |
0 (0%) |
2 (11%) |
|
| MEC_TRANSM |
|
|
|
0.016 |
| Desconocida |
14 (37%) |
9 (47%) |
5 (26%) |
|
| En los Servicios Salud |
1 (2.6%) |
1 (5.3%) |
0 (0%) |
|
| Persona a persona (Excepto las opciones anteriores) |
13 (34%) |
8 (42%) |
5 (26%) |
|
| Sexual |
10 (26%) |
1 (5.3%) |
9 (47%) |
|
| FIEBRE |
|
|
|
0.046 |
| 1 |
33 (87%) |
14 (74%) |
19 (100%) |
|
| 2 |
5 (13%) |
5 (26%) |
0 (0%) |
|
| LUMBALGIA |
|
|
|
0.048 |
| 1 |
15 (42%) |
5 (26%) |
10 (59%) |
|
| 2 |
21 (58%) |
14 (74%) |
7 (41%) |
|
| Unknown |
2 |
0 |
2 |
|
| ASTENIA |
|
|
|
0.009 |
| 1 |
22 (58%) |
7 (37%) |
15 (79%) |
|
| 2 |
16 (42%) |
12 (63%) |
4 (21%) |
|
| DOLOROSAS |
|
|
|
0.040 |
| 1 |
4 (11%) |
0 (0%) |
4 (24%) |
|
| 2 |
32 (89%) |
19 (100%) |
13 (76%) |
|
| Unknown |
2 |
0 |
2 |
|
| COMOR_VIH |
11 (29%) |
1 (5.3%) |
10 (53%) |
0.001 |
| EXA_PAPULA |
25 (69%) |
10 (53%) |
15 (88%) |
0.021 |
| Unknown |
2 |
0 |
2 |
|
| LINF_AXILAR |
4 (11%) |
0 (0%) |
4 (24%) |
0.040 |
| Unknown |
2 |
0 |
2 |
|
#EXA_PAPULA, EXA_PUSTULA, EXA_COSTRA, LUMBALGIA, ASTENIA, ESCALOFRIOS, LINF_INGUINAL, LINF_OTROS, #COMOR_VIH
dbvirsim %>% select(GENERO, ORIENT_SEX, MEC_TRANSM,FIEBRE,LUMBALGIA,ASTENIA,DOLOROSAS,COMOR_VIH, EXA_PAPULA,LINF_AXILAR,status) %>%
tbl_uvregression(
method = glm,
y = status,
method.args = list(family = binomial),
exponentiate = TRUE
)
## 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 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 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 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 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 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 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 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 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 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 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 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 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 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 occurred
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## 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 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 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 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 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 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 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 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 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 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
| Characteristic |
N |
OR |
95% CI |
p-value |
| GENERO |
38 |
|
|
|
| Hombre |
|
— |
— |
|
| Mujer |
|
0.00 |
|
>0.9 |
| ORIENT_SEX |
38 |
|
|
|
| Bisexual |
|
— |
— |
|
| Gay |
|
0.00 |
|
>0.9 |
| Heterosexual |
|
0.00 |
|
>0.9 |
| Hombres que tienen sexo con hombres |
|
1.00 |
0.00, Inf |
>0.9 |
| MEC_TRANSM |
38 |
|
|
|
| Desconocida |
|
— |
— |
|
| En los Servicios Salud |
|
0.00 |
|
>0.9 |
| Persona a persona (Excepto las opciones anteriores) |
|
1.13 |
0.23, 5.52 |
0.9 |
| Sexual |
|
16.2 |
2.14, 347 |
0.020 |
| FIEBRE |
38 |
0.00 |
|
>0.9 |
| LUMBALGIA |
36 |
0.25 |
0.06, 0.98 |
0.053 |
| ASTENIA |
38 |
0.16 |
0.03, 0.62 |
0.012 |
| DOLOROSAS |
36 |
0.00 |
|
>0.9 |
| COMOR_VIH |
38 |
20.0 |
3.12, 398 |
0.008 |
| EXA_PAPULA |
36 |
6.75 |
1.38, 50.8 |
0.030 |
| LINF_AXILAR |
36 |
62,180,880 |
0.00, NA |
>0.9 |
significativos en el modelo
dbvirsim %>% select(EXA_PAPULA, LUMBALGIA, ASTENIA, LINF_INGUINAL, COMOR_VIH,status) %>%
tbl_uvregression(
method = glm,
y = status,
method.args = list(family = binomial),
exponentiate = TRUE
)
| Characteristic |
N |
OR |
95% CI |
p-value |
| EXA_PAPULA |
36 |
6.75 |
1.38, 50.8 |
0.030 |
| LUMBALGIA |
36 |
0.25 |
0.06, 0.98 |
0.053 |
| ASTENIA |
38 |
0.16 |
0.03, 0.62 |
0.012 |
| LINF_INGUINAL |
36 |
7.50 |
1.04, 153 |
0.082 |
| COMOR_VIH |
38 |
20.0 |
3.12, 398 |
0.008 |
#FOREST PLOTS
dbvirsimica<-dbvirsim %>% select(EXA_PAPULA,EXA_PUSTULA, EXA_COSTRA, LUMBALGIA, ASTENIA, ESCALOFRIOS, LINF_INGUINAL, LINF_OTROS, COMOR_VIH,status)
#write_xlsx(dbvirsimica,"C:/Users/fidel/OneDrive - CINVESTAV/Proyecto colaboración SSA/base de datos/Viruelasimica/dbvirsimica.xlsx")
dbvirsimica <- dbvirsimica %>% mutate(EXA_PAPULA=recode(EXA_PAPULA, `0` = "Negative",
`1` = "Positive"),
EXA_COSTRA=recode(EXA_COSTRA, `0` = "Negative",
`1` = "Positive"),
EXA_PUSTULA=recode(EXA_PUSTULA, `0` = "Negative",
`1` = "Positive"),
LUMBALGIA=recode(LUMBALGIA, `2` = "Negative",
`1` = "Positive"),
ASTENIA=recode(ASTENIA, `2` = "Negative",
`1` = "Positive"),
ESCALOFRIOS=recode(ESCALOFRIOS, `2` = "Negative",
`1` = "Positive"),
LINF_INGUINAL=recode(ESCALOFRIOS, `0` = "Negative",
`1` = "Positive"),
LINF_OTROS=recode(ESCALOFRIOS, `0` = "Negative",
`1` = "Positive"),
COMOR_VIH=recode(ESCALOFRIOS, `0` = "Negative",
`1` = "Positive"))
dbvirsimica<- dbvirsimica %>% drop_na()
library(finalfit)
explanatory <- c("EXA_PAPULA", "EXA_COSTRA")
dependent <- "status"
dbvirsimica %>%
or_plot(dependent, explanatory,
breaks = c(0.5, 1, 5, 10, 20, 30),
table_text_size = 3.5)
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Warning: Removed 2 rows containing missing values (`geom_errorbarh()`).
#write_xlsx(dbvirsimica,"C:/Users/fidel/OneDrive - CINVESTAV/Proyecto colaboración SSA/base de datos/Viruelasimica/dbvirsimica.xlsx")
str(dbvirsimica)
## tibble [36 × 10] (S3: tbl_df/tbl/data.frame)
## $ EXA_PAPULA : chr [1:36] "Positive" "Positive" "Negative" "Positive" ...
## $ EXA_PUSTULA : chr [1:36] "Negative" "Negative" "Negative" "Negative" ...
## $ EXA_COSTRA : chr [1:36] "Positive" "Positive" "Negative" "Negative" ...
## $ LUMBALGIA : chr [1:36] "Negative" "Negative" "Negative" "Negative" ...
## $ ASTENIA : chr [1:36] "Negative" "Negative" "Negative" "Negative" ...
## $ ESCALOFRIOS : chr [1:36] "Negative" "Negative" "Negative" "Negative" ...
## $ LINF_INGUINAL: chr [1:36] "Negative" "Negative" "Negative" "Negative" ...
## $ LINF_OTROS : chr [1:36] "Negative" "Negative" "Negative" "Negative" ...
## $ COMOR_VIH : chr [1:36] "Negative" "Negative" "Negative" "Negative" ...
## $ status : num [1:36] 1 0 0 0 0 1 0 0 0 1 ...
library(tidyverse)
library(finalfit)
library(gt)
dbvirsimica<-dbvirsim %>% select(GENERO, ORIENT_SEX, MEC_TRANSM,FIEBRE,LUMBALGIA,ASTENIA,DOLOROSAS,COMOR_VIH, EXA_PAPULA,LINF_AXILAR,status)
dbvirsimica <- dbvirsimica %>% mutate(FIEBRE=recode(FIEBRE, `2` = "Negative",
`1` = "Positive"),
LUMBALGIA=recode(LUMBALGIA, `2` = "Negative",
`1` = "Positive"),
ASTENIA=recode(ASTENIA, `2` = "Negative",
`1` = "Positive"),
LUMBALGIA=recode(LUMBALGIA, `2` = "Negative",
`1` = "Positive"),
DOLOROSAS=recode(DOLOROSAS, `2` = "Negative",
`1` = "Positive"),
COMOR_VIH=recode(COMOR_VIH, `0` = "Negative",
`1` = "Positive"),
EXA_PAPULA=recode(EXA_PAPULA, `0` = "Negative",
`1` = "Positive"),
LINF_AXILAR=recode(LINF_AXILAR, `0` = "Negative",
`1` = "Positive"))
dbvirsimica$FIEBRE = as.character(dbvirsimica$FIEBRE)
dbvirsimica$LUMBALGIA = as.character(dbvirsimica$LUMBALGIA)
dbvirsimica$ASTENIA = as.character(dbvirsimica$ASTENIA)
dbvirsimica$COMOR_VIH = as.character(dbvirsimica$COMOR_VIH)
dbvirsimica$EXA_PAPULA = as.character(dbvirsimica$EXA_PAPULA)
#dbvirsimica<- dbvirsimica %>% drop_na()
#library("writexl")
#write_xlsx(dbvirsimica,"C:/Users/fidel/OneDrive - CINVESTAV/Proyecto colaboración SSA/base de datos/Viruelasimica/dbvirsimica.xlsx")
# Specify explanatory variables of interest
explanatory <- c("GENERO", "ORIENT_SEX", "MEC_TRANSM","FIEBRE","LUMBALGIA","ASTENIA","DOLOROSAS","COMOR_VIH", "EXA_PAPULA","LINF_AXILAR")
dependent <- "status"
dbvirsimica %>%
summary_factorlist("status", explanatory,
p=TRUE, na_include=FALSE)
## label levels unit
## GENERO Hombre Mean (sd)
## Mujer Mean (sd)
## ORIENT_SEX Bisexual Mean (sd)
## Gay Mean (sd)
## Heterosexual Mean (sd)
## Hombres que tienen sexo con hombres Mean (sd)
## MEC_TRANSM Desconocida Mean (sd)
## Persona a persona (Excepto las opciones anteriores) Mean (sd)
## Sexual Mean (sd)
## FIEBRE Negative Mean (sd)
## Positive Mean (sd)
## LUMBALGIA Negative Mean (sd)
## Positive Mean (sd)
## ASTENIA Negative Mean (sd)
## Positive Mean (sd)
## DOLOROSAS Negative Mean (sd)
## Positive Mean (sd)
## COMOR_VIH Negative Mean (sd)
## Positive Mean (sd)
## EXA_PAPULA Negative Mean (sd)
## Positive Mean (sd)
## LINF_AXILAR Negative Mean (sd)
## Positive Mean (sd)
## value p
## 0.6 (0.5) 0.003
## 0.0 (0.0)
## 1.0 (0.0) <0.001
## 0.8 (0.4)
## 0.1 (0.4)
## 1.0 (0.0)
## 0.4 (0.5) 0.022
## 0.4 (0.5)
## 0.9 (0.3)
## 0.0 (0.0) 0.016
## 0.6 (0.5)
## 0.3 (0.5) 0.050
## 0.7 (0.5)
## 0.2 (0.4) 0.008
## 0.7 (0.5)
## 0.4 (0.5) 0.025
## 1.0 (0.0)
## 0.3 (0.5) 0.001
## 0.9 (0.3)
## 0.2 (0.4) 0.020
## 0.6 (0.5)
## 0.4 (0.5) 0.025
## 1.0 (0.0)
model1<-dbvirsimica %>% select(GENERO, ORIENT_SEX, MEC_TRANSM,FIEBRE,LUMBALGIA,ASTENIA,DOLOROSAS,COMOR_VIH, EXA_PAPULA,LINF_AXILAR,status) %>%
tbl_uvregression(
method = glm,
y = status,
method.args = list(family = binomial),
exponentiate = TRUE
)
## 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 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 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 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 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 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 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 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 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 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 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 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 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 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 occurred
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## 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 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 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 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 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 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 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
model1
| Characteristic |
N |
OR |
95% CI |
p-value |
| GENERO |
38 |
|
|
|
| Hombre |
|
— |
— |
|
| Mujer |
|
0.00 |
|
>0.9 |
| ORIENT_SEX |
38 |
|
|
|
| Bisexual |
|
— |
— |
|
| Gay |
|
0.00 |
|
>0.9 |
| Heterosexual |
|
0.00 |
|
>0.9 |
| Hombres que tienen sexo con hombres |
|
1.00 |
0.00, Inf |
>0.9 |
| MEC_TRANSM |
38 |
|
|
|
| Desconocida |
|
— |
— |
|
| En los Servicios Salud |
|
0.00 |
|
>0.9 |
| Persona a persona (Excepto las opciones anteriores) |
|
1.13 |
0.23, 5.52 |
0.9 |
| Sexual |
|
16.2 |
2.14, 347 |
0.020 |
| FIEBRE |
38 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
57,739,388 |
0.00, NA |
>0.9 |
| LUMBALGIA |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
4.00 |
1.02, 17.6 |
0.053 |
| ASTENIA |
38 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
6.43 |
1.62, 30.3 |
0.012 |
| DOLOROSAS |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
62,180,880 |
0.00, NA |
>0.9 |
| COMOR_VIH |
38 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
20.0 |
3.12, 398 |
0.008 |
| EXA_PAPULA |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
6.75 |
1.38, 50.8 |
0.030 |
| LINF_AXILAR |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
62,180,880 |
0.00, NA |
>0.9 |
MODEL 2
model2<-dbvirsimica %>% select(FIEBRE,LUMBALGIA,ASTENIA,DOLOROSAS,COMOR_VIH, EXA_PAPULA,LINF_AXILAR,status) %>%
tbl_uvregression(
method = glm,
y = status,
method.args = list(family = binomial),
exponentiate = TRUE
)
## 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 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 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 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 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 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 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## 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 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 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 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 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 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 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
model2
| Characteristic |
N |
OR |
95% CI |
p-value |
| FIEBRE |
38 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
57,739,388 |
0.00, NA |
>0.9 |
| LUMBALGIA |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
4.00 |
1.02, 17.6 |
0.053 |
| ASTENIA |
38 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
6.43 |
1.62, 30.3 |
0.012 |
| DOLOROSAS |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
62,180,880 |
0.00, NA |
>0.9 |
| COMOR_VIH |
38 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
20.0 |
3.12, 398 |
0.008 |
| EXA_PAPULA |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
6.75 |
1.38, 50.8 |
0.030 |
| LINF_AXILAR |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
62,180,880 |
0.00, NA |
>0.9 |
MODELO 3
model3<-dbvirsimica %>% select(LUMBALGIA,ASTENIA,COMOR_VIH, EXA_PAPULA,status) %>%
tbl_uvregression(
method = glm,
y = status,
method.args = list(family = binomial),
exponentiate = TRUE
)
model3
| Characteristic |
N |
OR |
95% CI |
p-value |
| LUMBALGIA |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
4.00 |
1.02, 17.6 |
0.053 |
| ASTENIA |
38 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
6.43 |
1.62, 30.3 |
0.012 |
| COMOR_VIH |
38 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
20.0 |
3.12, 398 |
0.008 |
| EXA_PAPULA |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
6.75 |
1.38, 50.8 |
0.030 |
##MERGED MODEL TABLES
library(gtsummary)
models <-
tbl_merge(
tbls = list(model1, model2, model3),
tab_spanner = c("Model 1", "Model 2", "Model 3")
)
models
| Characteristic |
Model 1
|
Model 2
|
Model 3
|
| N |
OR |
95% CI |
p-value |
N |
OR |
95% CI |
p-value |
N |
OR |
95% CI |
p-value |
| GENERO |
38 |
|
|
|
|
|
|
|
|
|
|
|
| Hombre |
|
— |
— |
|
|
|
|
|
|
|
|
|
| Mujer |
|
0.00 |
|
>0.9 |
|
|
|
|
|
|
|
|
| ORIENT_SEX |
38 |
|
|
|
|
|
|
|
|
|
|
|
| Bisexual |
|
— |
— |
|
|
|
|
|
|
|
|
|
| Gay |
|
0.00 |
|
>0.9 |
|
|
|
|
|
|
|
|
| Heterosexual |
|
0.00 |
|
>0.9 |
|
|
|
|
|
|
|
|
| Hombres que tienen sexo con hombres |
|
1.00 |
0.00, Inf |
>0.9 |
|
|
|
|
|
|
|
|
| MEC_TRANSM |
38 |
|
|
|
|
|
|
|
|
|
|
|
| Desconocida |
|
— |
— |
|
|
|
|
|
|
|
|
|
| En los Servicios Salud |
|
0.00 |
|
>0.9 |
|
|
|
|
|
|
|
|
| Persona a persona (Excepto las opciones anteriores) |
|
1.13 |
0.23, 5.52 |
0.9 |
|
|
|
|
|
|
|
|
| Sexual |
|
16.2 |
2.14, 347 |
0.020 |
|
|
|
|
|
|
|
|
| FIEBRE |
38 |
|
|
|
38 |
|
|
|
|
|
|
|
| Negative |
|
— |
— |
|
|
— |
— |
|
|
|
|
|
| Positive |
|
57,739,388 |
0.00, NA |
>0.9 |
|
57,739,388 |
0.00, NA |
>0.9 |
|
|
|
|
| LUMBALGIA |
36 |
|
|
|
36 |
|
|
|
36 |
|
|
|
| Negative |
|
— |
— |
|
|
— |
— |
|
|
— |
— |
|
| Positive |
|
4.00 |
1.02, 17.6 |
0.053 |
|
4.00 |
1.02, 17.6 |
0.053 |
|
4.00 |
1.02, 17.6 |
0.053 |
| ASTENIA |
38 |
|
|
|
38 |
|
|
|
38 |
|
|
|
| Negative |
|
— |
— |
|
|
— |
— |
|
|
— |
— |
|
| Positive |
|
6.43 |
1.62, 30.3 |
0.012 |
|
6.43 |
1.62, 30.3 |
0.012 |
|
6.43 |
1.62, 30.3 |
0.012 |
| DOLOROSAS |
36 |
|
|
|
36 |
|
|
|
|
|
|
|
| Negative |
|
— |
— |
|
|
— |
— |
|
|
|
|
|
| Positive |
|
62,180,880 |
0.00, NA |
>0.9 |
|
62,180,880 |
0.00, NA |
>0.9 |
|
|
|
|
| COMOR_VIH |
38 |
|
|
|
38 |
|
|
|
38 |
|
|
|
| Negative |
|
— |
— |
|
|
— |
— |
|
|
— |
— |
|
| Positive |
|
20.0 |
3.12, 398 |
0.008 |
|
20.0 |
3.12, 398 |
0.008 |
|
20.0 |
3.12, 398 |
0.008 |
| EXA_PAPULA |
36 |
|
|
|
36 |
|
|
|
36 |
|
|
|
| Negative |
|
— |
— |
|
|
— |
— |
|
|
— |
— |
|
| Positive |
|
6.75 |
1.38, 50.8 |
0.030 |
|
6.75 |
1.38, 50.8 |
0.030 |
|
6.75 |
1.38, 50.8 |
0.030 |
| LINF_AXILAR |
36 |
|
|
|
36 |
|
|
|
|
|
|
|
| Negative |
|
— |
— |
|
|
— |
— |
|
|
|
|
|
| Positive |
|
62,180,880 |
0.00, NA |
>0.9 |
|
62,180,880 |
0.00, NA |
>0.9 |
|
|
|
|
FOREST PLOT OR
MODEL1
library(ggstats)
glm(status~GENERO+ORIENT_SEX+MEC_TRANSM+FIEBRE+LUMBALGIA+ASTENIA+DOLOROSAS+COMOR_VIH+EXA_PAPULA+LINF_AXILAR, dbvirsimica, family = binomial) %>%
tbl_regression(
add_estimate_to_reference_rows = TRUE,
exponentiate = TRUE
) %>%
plot()
## 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 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 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 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 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 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 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 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 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 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 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 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 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 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## 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: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: glm.fit: algorithm did not converge
## 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 occurred
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: Transformation introduced infinite values in continuous x-axis
## Transformation introduced infinite values in continuous x-axis
## Transformation introduced infinite values in continuous x-axis
##MODEL 2
glm(status~FIEBRE+LUMBALGIA+ASTENIA+DOLOROSAS+COMOR_VIH+EXA_PAPULA+LINF_AXILAR, dbvirsimica, family = binomial) %>%
tbl_regression(
add_estimate_to_reference_rows = TRUE,
exponentiate = TRUE
) %>%
plot()
## 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 occurred
## Warning: glm.fit: algorithm did not converge
## 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 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 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 occurred
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
## collapsing to unique 'x' values
## Warning: Transformation introduced infinite values in continuous x-axis
## Transformation introduced infinite values in continuous x-axis
## Transformation introduced infinite values in continuous x-axis
##MODEL 3
glm(status~LUMBALGIA+ASTENIA+COMOR_VIH+EXA_PAPULA, dbvirsimica, family = binomial) %>%
tbl_regression(
add_estimate_to_reference_rows = TRUE,
exponentiate = TRUE
) %>%
plot()
FP
#Looping multiple univariate models
mv_reg <- glm(status ~LUMBALGIA+ASTENIA+COMOR_VIH+EXA_PAPULA, family = "binomial", data = dbvirsimica)
summary(mv_reg)
##
## Call:
## glm(formula = status ~ LUMBALGIA + ASTENIA + COMOR_VIH + EXA_PAPULA,
## family = "binomial", data = dbvirsimica)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8578 -0.6468 -0.2496 0.9057 1.5070
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.4535 1.3508 -2.557 0.0106 *
## LUMBALGIAPositive 0.7101 1.0098 0.703 0.4820
## ASTENIAPositive 1.4273 1.0424 1.369 0.1709
## COMOR_VIHPositive 3.3874 1.4320 2.366 0.0180 *
## EXA_PAPULAPositive 1.9953 1.2512 1.595 0.1108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 49.795 on 35 degrees of freedom
## Residual deviance: 30.557 on 31 degrees of freedom
## (2 observations deleted due to missingness)
## AIC: 40.557
##
## Number of Fisher Scoring iterations: 5
## choose a model using forward selection based on AIC
## you can also do "backward" or "both" by adjusting the direction
final_mv_reg <- mv_reg %>%
step(direction = "forward", trace = FALSE)
final_mv_reg
##
## Call: glm(formula = status ~ LUMBALGIA + ASTENIA + COMOR_VIH + EXA_PAPULA,
## family = "binomial", data = dbvirsimica)
##
## Coefficients:
## (Intercept) LUMBALGIAPositive ASTENIAPositive COMOR_VIHPositive
## -3.4535 0.7101 1.4273 3.3874
## EXA_PAPULAPositive
## 1.9953
##
## Degrees of Freedom: 35 Total (i.e. Null); 31 Residual
## (2 observations deleted due to missingness)
## Null Deviance: 49.8
## Residual Deviance: 30.56 AIC: 40.56
mv_tab_base <- final_mv_reg %>%
broom::tidy(exponentiate = TRUE, conf.int = TRUE) %>% ## get a tidy dataframe of estimates
mutate(across(where(is.numeric), round, digits = 2)) ## round
## show results table of final regression
mv_tab <- tbl_regression(final_mv_reg, exponentiate = TRUE)
mv_tab
| Characteristic |
OR |
95% CI |
p-value |
| LUMBALGIA |
|
|
|
| Negative |
— |
— |
|
| Positive |
2.03 |
0.26, 15.7 |
0.5 |
| ASTENIA |
|
|
|
| Negative |
— |
— |
|
| Positive |
4.17 |
0.57, 40.5 |
0.2 |
| COMOR_VIH |
|
|
|
| Negative |
— |
— |
|
| Positive |
29.6 |
2.87, 1,126 |
0.018 |
| EXA_PAPULA |
|
|
|
| Negative |
— |
— |
|
| Positive |
7.35 |
0.82, 172 |
0.11 |
library(broom)
## define variables of interest
explanatory_vars <- c("LUMBALGIA","ASTENIA","COMOR_VIH", "EXA_PAPULA")
explanatory_vars %>% str_c("status ~ ", .)
## [1] "status ~ LUMBALGIA" "status ~ ASTENIA" "status ~ COMOR_VIH"
## [4] "status ~ EXA_PAPULA"
models <- explanatory_vars %>% # begin with variables of interest
str_c("status ~ ", .) %>% # combine each variable into formula ("outcome ~ variable of interest")
# iterate through each univariate formula
map(
.f = ~glm( # pass the formulas one-by-one to glm()
formula = as.formula(.x), # within glm(), the string formula is .x
family = "binomial", # specify type of glm (logistic)
data = dbvirsimica)) %>% # dataset
# tidy up each of the glm regression outputs from above
map(
.f = ~tidy(
.x,
exponentiate = TRUE, # exponentiate
conf.int = TRUE)) %>% # return confidence intervals
# collapse the list of regression outputs in to one data frame
bind_rows() %>%
# round all numeric columns
mutate(across(where(is.numeric), round, digits = 2))
models
## # A tibble: 8 × 7
## term estimate std.error statistic p.value conf.low conf.high
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.5 0.46 -1.5 0.13 0.19 1.2
## 2 LUMBALGIAPositive 4 0.72 1.93 0.05 1.02 17.6
## 3 (Intercept) 0.33 0.58 -1.9 0.06 0.09 0.96
## 4 ASTENIAPositive 6.43 0.74 2.53 0.01 1.62 30.4
## 5 (Intercept) 0.5 0.41 -1.7 0.09 0.21 1.09
## 6 COMOR_VIHPositive 20 1.13 2.66 0.01 3.12 398.
## 7 (Intercept) 0.22 0.78 -1.92 0.05 0.03 0.86
## 8 EXA_PAPULAPositive 6.75 0.88 2.17 0.03 1.38 50.8
## for each explanatory variable
univ_tab_base <- explanatory_vars %>%
map(.f =
~{dbvirsimica %>% ## begin with linelist
group_by(status) %>% ## group data set by outcome
count(.data[[.x]]) %>% ## produce counts for variable of interest
pivot_wider( ## spread to wide format (as in cross-tabulation)
names_from = status,
values_from = n) %>%
drop_na(.data[[.x]]) %>% ## drop rows with missings
rename("variable" = .x) %>% ## change variable of interest column to "variable"
mutate(variable = as.character(variable))} ## convert to character, else non-dichotomous (categorical) variables come out as factor and cant be merged
) %>%
## collapse the list of count outputs in to one data frame
bind_rows() %>%
## merge with the outputs of the regression
bind_cols(., models) %>%
## only keep columns interested in
select(term, 2:3, estimate, conf.low, conf.high, p.value) %>%
## round decimal places
mutate(across(where(is.numeric), round, digits = 2))
## Warning: Use of .data in tidyselect expressions was deprecated in tidyselect 1.2.0.
## ℹ Please use `all_of(var)` (or `any_of(var)`) instead of `.data[[var]]`
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(.x)
##
## # Now:
## data %>% select(all_of(.x))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
univ_tab <- dbvirsimica %>%
dplyr::select(explanatory_vars, status) %>% ## select variables of interest
tbl_uvregression( ## produce univariate table
method = glm, ## define regression want to run (generalised linear model)
y = status, ## define outcome variable
method.args = list(family = binomial), ## define what type of glm want to run (logistic)
exponentiate = TRUE ## exponentiate to produce odds ratios (rather than log odds)
)
## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(explanatory_vars)
##
## # Now:
## data %>% select(all_of(explanatory_vars))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## view univariate results table
univ_tab
| Characteristic |
N |
OR |
95% CI |
p-value |
| LUMBALGIA |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
4.00 |
1.02, 17.6 |
0.053 |
| ASTENIA |
38 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
6.43 |
1.62, 30.3 |
0.012 |
| COMOR_VIH |
38 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
20.0 |
3.12, 398 |
0.008 |
| EXA_PAPULA |
36 |
|
|
|
| Negative |
|
— |
— |
|
| Positive |
|
6.75 |
1.38, 50.8 |
0.030 |
## combine with univariate results
tbl_merge(
tbls = list(univ_tab, mv_tab), # combine
tab_spanner = c("**Univariate**", "**Multivariable**")) # set header names
| Characteristic |
Univariate
|
Multivariable
|
| N |
OR |
95% CI |
p-value |
OR |
95% CI |
p-value |
| LUMBALGIA |
36 |
|
|
|
|
|
|
| Negative |
|
— |
— |
|
— |
— |
|
| Positive |
|
4.00 |
1.02, 17.6 |
0.053 |
2.03 |
0.26, 15.7 |
0.5 |
| ASTENIA |
38 |
|
|
|
|
|
|
| Negative |
|
— |
— |
|
— |
— |
|
| Positive |
|
6.43 |
1.62, 30.3 |
0.012 |
4.17 |
0.57, 40.5 |
0.2 |
| COMOR_VIH |
38 |
|
|
|
|
|
|
| Negative |
|
— |
— |
|
— |
— |
|
| Positive |
|
20.0 |
3.12, 398 |
0.008 |
29.6 |
2.87, 1,126 |
0.018 |
| EXA_PAPULA |
36 |
|
|
|
|
|
|
| Negative |
|
— |
— |
|
— |
— |
|
| Positive |
|
6.75 |
1.38, 50.8 |
0.030 |
7.35 |
0.82, 172 |
0.11 |
ROC CURVES
#dbsimicaroc<-read_excel("C:/Users/fidel/OneDrive - CINVESTAV/Proyecto colaboración SSA/base de datos/Viruelasimica/dbvirsimica.xlsx")
#str(dbsimicaroc)