Menopausia

library(readxl)

Menopausia <- read_excel("Desktop/Menopausia.xlsx", 
    col_types = c("text", "text", "numeric", 
        "text", "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "text", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "text", "numeric", "numeric", 
        "text", "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "text", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "text", "numeric", 
        "text", "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "text", "numeric", "numeric", "numeric", 
        "numeric", "numeric", "text", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", "numeric", "numeric"), 
           .name_repair = janitor::make_clean_names
    )

1ª parte: Analisis de pacientes con antecedentes oncológicos

library(tidyverse)
library(skimr)
library(gtsummary)

#frecuencia de los síntomas menopausicos en mujeres con antecedentes oncológicos
sint_meno <- Menopausia %>%
  select(ha_tenido_un_cancer, no_tengo_sintomas, sofocos, sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, tengo_insomnio, estoy_irritada, me_siento_decaida_tengo_el_llanto_facil, dolor_articular_muscular)

#obtener la frecuencia de los sint_meno con tbl_summary
sint_meno %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,8701	1, N = 2571	Difference2	95% CI2,3	p-value2
no_tengo_sintomas	303.0 / 3,870.0 (7.8%)	12.0 / 257.0 (4.7%)	3.2%	0.24%, 6.1%	0.084
sofocos	2,165.0 / 3,870.0 (55.9%)	162.0 / 257.0 (63.0%)	-7.1%	-13%, -0.78%	0.031
sequedad_vaginal	1,977.0 / 3,870.0 (51.1%)	153.0 / 257.0 (59.5%)	-8.4%	-15%, -2.0%	0.010
tengo_dolor_en_las_relaciones_sexuales	1,063.0 / 3,870.0 (27.5%)	92.0 / 257.0 (35.8%)	-8.3%	-15%, -2.1%	0.005
no_tengo_deseos_de_tener_relaciones_sexuales	2,082.0 / 3,870.0 (53.8%)	143.0 / 257.0 (55.6%)	-1.8%	-8.3%, 4.6%	0.6
tengo_insomnio	2,147.0 / 3,870.0 (55.5%)	131.0 / 257.0 (51.0%)	4.5%	-2.0%, 11%	0.2
estoy_irritada	1,738.0 / 3,870.0 (44.9%)	101.0 / 257.0 (39.3%)	5.6%	-0.77%, 12%	0.092
me_siento_decaida_tengo_el_llanto_facil	1,710.0 / 3,870.0 (44.2%)	97.0 / 257.0 (37.7%)	6.4%	0.11%, 13%	0.051
dolor_articular_muscular	2,291.0 / 3,870.0 (59.2%)	158.0 / 257.0 (61.5%)	-2.3%	-8.6%, 4.1%	0.5
1 n / N (%)
2 Two sample test for equality of proportions
3 CI = Confidence Interval

#crea la variable imc que es el peso en kg dividido por la altura en metros al cuadrado
Menopausia <- Menopausia %>%
  mutate(imc = peso_kg / ((altura_cm/100)^2))

#edad y comorbilidades en pacientes con antecedentes oncológicos
Menopausia %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(edad, peso_kg, altura_cm, imc, con_que_grupo_se_identificaria_mas, tabaco, alcohol, fue_hace_mas_de_1_ano, ha_tenido_algun_ingreso_reciente, ninguna, cardiopatia, epoc, diabetes_mellitus, enfermedad_autoinmune, trasplante, ictus, problemas_de_higado, dialisis_renal),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,8701	1, N = 2571	Difference2	95% CI2,3	p-value2
edad	51.68 (6.27) 51.00 (48.00, 54.00)	52.24 (8.34) 52.00 (47.00, 57.00)	-0.57	-1.6, 0.48	0.3
peso_kg	66.60 (14.60) 64.00 (57.58, 72.40)	65.51 (12.85) 63.50 (57.75, 73.00)	1.1	-0.56, 2.7	0.2
Unknown	34	2
altura_cm	163.07 (7.04) 163.00 (159.00, 168.00)	163.12 (6.82) 163.00 (160.00, 167.00)	-0.06	-0.93, 0.82	>0.9
Unknown	18	2
imc	25.07 (5.44) 24.07 (21.72, 27.29)	24.67 (4.91) 23.95 (21.09, 27.34)	0.40	-0.23, 1.0	0.2
Unknown	48	3
con_que_grupo_se_identificaria_mas			-0.01	-0.14, 0.12
1	3,330.0 / 3,784.0 (88.0%)	219.0 / 249.0 (88.0%)
2	19.0 / 3,784.0 (0.5%)	0.0 / 249.0 (0.0%)
3	7.0 / 3,784.0 (0.2%)	2.0 / 249.0 (0.8%)
4	427.0 / 3,784.0 (11.3%)	27.0 / 249.0 (10.8%)
5	1.0 / 3,784.0 (0.0%)	1.0 / 249.0 (0.4%)
Unknown	86	8
tabaco			-0.11	-0.24, 0.02
1	2,611.0 / 3,840.0 (68.0%)	162.0 / 257.0 (63.0%)
2	169.0 / 3,840.0 (4.4%)	18.0 / 257.0 (7.0%)
3	571.0 / 3,840.0 (14.9%)	30.0 / 257.0 (11.7%)
4	489.0 / 3,840.0 (12.7%)	47.0 / 257.0 (18.3%)
Unknown	30	0
alcohol			0.05	-0.08, 0.19
1	15.0 / 3,622.0 (0.4%)	1.0 / 238.0 (0.4%)
2	2,203.0 / 3,622.0 (60.8%)	152.0 / 238.0 (63.9%)
3	818.0 / 3,622.0 (22.6%)	49.0 / 238.0 (20.6%)
4	586.0 / 3,622.0 (16.2%)	36.0 / 238.0 (15.1%)
Unknown	248	19
fue_hace_mas_de_1_ano	1,829.0 / 3,870.0 (47.3%)	179.0 / 257.0 (69.6%)	-22%	-28%, -16%	<0.001
ha_tenido_algun_ingreso_reciente	288.0 / 3,870.0 (7.4%)	32.0 / 257.0 (12.5%)	-5.0%	-9.3%, -0.68%	0.005
ninguna	2,173.0 / 3,870.0 (56.1%)	105.0 / 257.0 (40.9%)	15%	8.9%, 22%	<0.001
cardiopatia	97.0 / 3,870.0 (2.5%)	1.0 / 257.0 (0.4%)	2.1%	1.0%, 3.2%	0.051
epoc	29.0 / 3,870.0 (0.7%)	5.0 / 257.0 (1.9%)	-1.2%	-3.1%, 0.72%	0.090
diabetes_mellitus	66.0 / 3,870.0 (1.7%)	7.0 / 257.0 (2.7%)	-1.0%	-3.3%, 1.2%	0.3
enfermedad_autoinmune	305.0 / 3,870.0 (7.9%)	16.0 / 257.0 (6.2%)	1.7%	-1.6%, 4.9%	0.4
trasplante	7.0 / 3,870.0 (0.2%)	3.0 / 257.0 (1.2%)	-0.99%	-2.5%, 0.54%	0.014
ictus	10.0 / 3,870.0 (0.3%)	0.0 / 257.0 (0.0%)	0.26%	-0.11%, 0.63%	0.9
problemas_de_higado	69.0 / 3,870.0 (1.8%)	11.0 / 257.0 (4.3%)	-2.5%	-5.2%, 0.22%	0.010
dialisis_renal	2.0 / 3,870.0 (0.1%)	0.0 / 257.0 (0.0%)	0.05%	-0.07%, 0.17%	>0.9
1 Mean (SD) Median (IQR); n / N (%)
2 Welch Two Sample t-test; Standardized Mean Difference; Two sample test for equality of proportions
3 CI = Confidence Interval

#mirar el porcentaje de sexualmente activas
Menopausia %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = actividad_sexual_coito_masturbacion_caricias,
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,8701	1, N = 2571	Difference2	95% CI2,3
actividad_sexual_coito_masturbacion_caricias			0.00	-0.13, 0.13
1	329.0 / 3,763.0 (8.7%)	34.0 / 247.0 (13.8%)
2	2,157.0 / 3,763.0 (57.3%)	121.0 / 247.0 (49.0%)
3	949.0 / 3,763.0 (25.2%)	67.0 / 247.0 (27.1%)
4	301.0 / 3,763.0 (8.0%)	23.0 / 247.0 (9.3%)
5	27.0 / 3,763.0 (0.7%)	2.0 / 247.0 (0.8%)
Unknown	107	10
1 n / N (%)
2 Standardized Mean Difference
3 CI = Confidence Interval

#recodifica la variable de actividad sexual siendo 1=no y 2, 3, 4 y 5 = si
Menopausia <- Menopausia %>%
  mutate(actividad_sexual2 = ifelse(actividad_sexual_coito_masturbacion_caricias == 1, "No", "Si"))

#porcentaje actividad sexual en pacientes con antecedentes oncológicos con la nueva variable
Menopausia %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = actividad_sexual2,
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,8701	1, N = 2571	Difference2	95% CI2,3
actividad_sexual2			0.16	0.03, 0.29
No	329.0 / 3,763.0 (8.7%)	34.0 / 247.0 (13.8%)
Si	3,434.0 / 3,763.0 (91.3%)	213.0 / 247.0 (86.2%)
Unknown	107	10
1 n / N (%)
2 Standardized Mean Difference
3 CI = Confidence Interval

#frecuencia de los sint_meno por tipo de cancer

Menopausia %>%
  filter(ha_tenido_un_cancer==1) %>%
  tbl_summary(
    include = (indique_que_tipo_de_cancer_ha_tenido),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) 

Characteristic	N = 2571
indique_que_tipo_de_cancer_ha_tenido
Cérvix	28.0 / 221.0 (12.7%)
Colon	12.0 / 221.0 (5.4%)
Desconocido	1.0 / 221.0 (0.5%)
Endometrio	16.0 / 221.0 (7.2%)
Endometrio + mama	1.0 / 221.0 (0.5%)
Glotis	1.0 / 221.0 (0.5%)
Hidradenocarcinoma	1.0 / 221.0 (0.5%)
Leucemia	2.0 / 221.0 (0.9%)
Linfoma	12.0 / 221.0 (5.4%)
Mama	85.0 / 221.0 (38.5%)
Melanoma	6.0 / 221.0 (2.7%)
No	5.0 / 221.0 (2.3%)
Ovario	21.0 / 221.0 (9.5%)
Páncreas	2.0 / 221.0 (0.9%)
Piel (basocelular)	1.0 / 221.0 (0.5%)
Pulmón	2.0 / 221.0 (0.9%)
Recto	1.0 / 221.0 (0.5%)
Riñón	3.0 / 221.0 (1.4%)
Timoma	1.0 / 221.0 (0.5%)
Tiroides	19.0 / 221.0 (8.6%)
Vejiga	1.0 / 221.0 (0.5%)
Unknown	36
1 n / N (%)

#miran si ha terminado ya el tratamiento oncológico en las pacientes con cancer
Menopausia %>%
  filter(ha_tenido_un_cancer==1) %>%
  tbl_summary(
    include = (ha_finalizado_el_tratamiento),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) 

Characteristic	N = 2571
ha_finalizado_el_tratamiento	214.0 / 257.0 (83.3%)
1 n / N (%)

#mirar frecuencia de medicación para el sd climatérico entre las pacientes oncológicas
Menopausia %>%
  filter(ha_tenido_un_cancer==1) %>%
  tbl_summary(
    include = (recibe_medicacion_para_el_sindrome_climaterico),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) 

Characteristic	N = 2571
recibe_medicacion_para_el_sindrome_climaterico	46.0 / 257.0 (17.9%)
1 n / N (%)

#mirar tipo de medicación para el sd climatérico entre las pacientes oncológicas

Menopausia %>%
  filter(ha_tenido_un_cancer==1) %>%
  tbl_summary(
    include = c(terapia_hormonal_oral_o_por_la_piel_parche_espray_crema,
  terapia_hormonal_solo_por_la_vagina, ser_ms_ospemifeno,
  antidepresivos, isoflavonas_de_soja, terapias_naturales_vegetales,
  especificar, otros_tratamientos_no_hormonales, especificar_2),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) 

Characteristic	N = 2571
terapia_hormonal_oral_o_por_la_piel_parche_espray_crema	26.0 / 257.0 (10.1%)
terapia_hormonal_solo_por_la_vagina	8.0 / 257.0 (3.1%)
ser_ms_ospemifeno	0.0 / 257.0 (0.0%)
antidepresivos	6.0 / 257.0 (2.3%)
isoflavonas_de_soja	2.0 / 257.0 (0.8%)
terapias_naturales_vegetales	9.0 / 257.0 (3.5%)
especificar
Cimicifuga	2.0 / 9.0 (22.2%)
Ciminocta	1.0 / 9.0 (11.1%)
Menocta	1.0 / 9.0 (11.1%)
Serelys	3.0 / 9.0 (33.3%)
Serotogyn	1.0 / 9.0 (11.1%)
Vitaminas	1.0 / 9.0 (11.1%)
Unknown	248
otros_tratamientos_no_hormonales	5.0 / 257.0 (1.9%)
especificar_2
Analgesia	1.0 / 5.0 (20.0%)
B1	1.0 / 5.0 (20.0%)
Laser	1.0 / 5.0 (20.0%)
Vitamina D	1.0 / 5.0 (20.0%)
Vitaminas	1.0 / 5.0 (20.0%)
Unknown	252
1 n / N (%)

#alteración de la calidad de vida en función de los dominios
Menopausia %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(percentil_dominio_menopausia_salud, percentil_dominio_psiquico, percentil_dominio_sexualidad, percentil_dominio_pareja, percentil_global, puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,8701	1, N = 2571	Difference2	95% CI2,3	p-value2
percentil_dominio_menopausia_salud	58.75 (27.84) 62.07 (36.00, 84.82)	61.16 (27.62) 67.71 (38.01, 87.23)	-2.4	-5.9, 1.1	0.2
percentil_dominio_psiquico	59.44 (30.78) 66.65 (37.52, 86.99)	59.45 (30.21) 58.35 (37.61, 86.99)	-0.01	-3.8, 3.8	>0.9
percentil_dominio_sexualidad	52.73 (28.98) 50.00 (25.00, 75.00)	56.92 (28.72) 62.50 (37.50, 75.00)	-4.2	-7.8, -0.55	0.024
percentil_dominio_pareja	34.32 (32.76) 37.50 (0.00, 62.50)	33.03 (33.16) 25.00 (0.00, 59.09)	1.3	-2.9, 5.5	0.5
percentil_global	45.29 (32.20) 45.58 (15.00, 75.58)	43.64 (33.85) 43.49 (9.03, 76.52)	1.7	-2.6, 5.9	0.4
puntos_dominio_menopausia_salud	40.08 (20.67) 39.45 (23.89, 55.01)	41.54 (21.07) 41.12 (25.01, 56.68)	-1.5	-4.1, 1.2	0.3
puntos_dominio_psiquico	41.14 (28.19) 40.02 (20.01, 60.03)	41.14 (27.98) 33.35 (20.01, 60.03)	0.00	-3.5, 3.5	>0.9
puntos_dominio_sexualidad	47.18 (24.65) 50.00 (30.00, 60.00)	51.28 (24.10) 50.00 (40.00, 70.00)	-4.1	-7.2, -1.0	0.009
puntos_dominio_pareja	20.81 (24.11) 20.00 (0.00, 30.00)	19.92 (24.56) 10.00 (0.00, 30.00)	0.89	-2.2, 4.0	0.6
puntos_global	31.55 (20.62) 32.78 (16.57, 46.54)	30.33 (22.02) 32.23 (10.70, 45.70)	1.2	-1.6, 4.0	0.4
1 Mean (SD) Median (IQR)
2 Welch Two Sample t-test
3 CI = Confidence Interval

Suplementario: Análisis de la puntuación de los dominios en función de la edad y de la patología oncológica

Recodificación de la edad

#Crea una nueva variable edadC que recodifica le edad en "40-44", 45-47, 48-50, 51-53, 54-56, 57-59, 60-62, 63-65, 66-68, 69-71 y 72-75
Menopausia <- Menopausia %>%
  mutate(edadC = case_when(
    edad >= 40 & edad <= 44 ~ "40-44",
    edad >= 45 & edad <= 47 ~ "45-47",
    edad >= 48 & edad <= 50 ~ "48-50",
    edad >= 51 & edad <= 53 ~ "51-53",
    edad >= 54 & edad <= 56 ~ "54-56",
    edad >= 57 & edad <= 59 ~ "57-59",
    edad >= 60 & edad <= 62 ~ "60-62",
    edad >= 63 & edad <= 65 ~ "63-65",
    edad >= 66 & edad <= 68 ~ "66-68",
    edad >= 69 & edad <= 71 ~ "69-71",
    edad >= 72 & edad <= 75 ~ "72-75",
    TRUE ~ "NA"
  ))

# porcentaje de pacientes de cada grupo de edad en función de si son oncológicas o no
Menopausia %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = edadC,
    statistic = list(all_categorical() ~ "{n} / {N} ({p}%)"),
    label = list(edadC = "Edad"),
    digits = list(all_categorical() ~ 1)
  ) %>%
  bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,8701	1, N = 2571	Difference2	95% CI2,3
Edad			0.54	0.41, 0.67
40-44	305.0 / 3,870.0 (7.9%)	48.0 / 257.0 (18.7%)
45-47	537.0 / 3,870.0 (13.9%)	22.0 / 257.0 (8.6%)
48-50	944.0 / 3,870.0 (24.4%)	41.0 / 257.0 (16.0%)
51-53	955.0 / 3,870.0 (24.7%)	49.0 / 257.0 (19.1%)
54-56	571.0 / 3,870.0 (14.8%)	30.0 / 257.0 (11.7%)
57-59	264.0 / 3,870.0 (6.8%)	24.0 / 257.0 (9.3%)
60-62	124.0 / 3,870.0 (3.2%)	13.0 / 257.0 (5.1%)
63-65	34.0 / 3,870.0 (0.9%)	14.0 / 257.0 (5.4%)
66-68	15.0 / 3,870.0 (0.4%)	2.0 / 257.0 (0.8%)
69-71	5.0 / 3,870.0 (0.1%)	1.0 / 257.0 (0.4%)
72-75	116.0 / 3,870.0 (3.0%)	12.0 / 257.0 (4.7%)
NA	0.0 / 3,870.0 (0.0%)	1.0 / 257.0 (0.4%)
1 n / N (%)
2 Standardized Mean Difference
3 CI = Confidence Interval

Análisis de la puntuación de los dominios en función de la edad y de la patología oncológica

Grupo de edad 40-44

Menopausia %>%
  filter(edadC == "40-44") %>%
  filter(pareja !=1) %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_overall()

Characteristic	Overall, N = 2961	0, N = 2551	1, N = 411	Difference2	95% CI2,3	p-value2
puntos_dominio_menopausia_salud	45.37 (22.23) 45.84 (29.45, 60.15)	45.62 (22.41) 46.12 (29.17, 61.13)	43.81 (21.28) 42.78 (31.12, 57.79)	1.8	-5.4, 9.0	0.6
puntos_dominio_psiquico	49.26 (28.41) 46.69 (26.68, 73.37)	49.99 (28.65) 46.69 (26.68, 73.37)	44.74 (26.73) 46.69 (26.68, 60.03)	5.2	-3.9, 14	0.3
puntos_dominio_sexualidad	43.61 (25.92) 40.00 (20.00, 60.00)	43.49 (26.61) 40.00 (20.00, 60.00)	44.39 (21.45) 40.00 (30.00, 60.00)	-0.90	-8.4, 6.6	0.8
puntos_dominio_pareja	23.24 (23.28) 20.00 (0.00, 40.00)	23.29 (23.71) 20.00 (0.00, 40.00)	22.93 (20.65) 20.00 (0.00, 40.00)	0.37	-6.7, 7.5	>0.9
puntos_global	37.62 (18.65) 40.08 (26.09, 49.80)	37.73 (18.88) 40.29 (26.05, 50.00)	36.95 (17.34) 39.03 (26.53, 48.34)	0.78	-5.1, 6.7	0.8
1 Mean (SD) Median (IQR)
2 Welch Two Sample t-test
3 CI = Confidence Interval

Grupo de edad 45-47

Menopausia %>%
  filter(edadC == "45-47") %>%
  filter(pareja !=1) %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_overall()

Characteristic	Overall, N = 4771	0, N = 4581	1, N = 191	Difference2	95% CI2,3	p-value2
puntos_dominio_menopausia_salud	40.68 (20.39) 40.01 (24.45, 56.67)	40.44 (20.48) 39.73 (24.45, 56.12)	46.32 (17.52) 45.01 (31.67, 60.57)	-5.9	-14, 2.7	0.2
puntos_dominio_psiquico	44.79 (28.49) 46.69 (20.01, 66.70)	45.18 (28.47) 46.69 (20.01, 66.70)	35.46 (27.95) 26.68 (13.34, 60.03)	9.7	-4.0, 23	0.2
puntos_dominio_sexualidad	45.18 (24.15) 40.00 (30.00, 60.00)	45.22 (24.07) 40.00 (30.00, 60.00)	44.21 (26.52) 50.00 (25.00, 60.00)	1.0	-12, 14	0.9
puntos_dominio_pareja	22.08 (23.53) 20.00 (0.00, 30.00)	22.25 (23.84) 20.00 (0.00, 37.50)	17.89 (13.57) 20.00 (5.00, 20.00)	4.4	-2.5, 11	0.2
puntos_global	36.13 (18.91) 36.81 (23.75, 49.17)	36.14 (19.11) 36.81 (23.65, 49.28)	35.97 (13.68) 35.42 (25.00, 46.33)	0.17	-6.6, 7.0	>0.9
1 Mean (SD) Median (IQR)
2 Welch Two Sample t-test
3 CI = Confidence Interval

Grupo de edad 48-50

Menopausia %>%
  filter(edadC == "48-50") %>%
  filter(pareja !=1) %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_overall()

Characteristic	Overall, N = 8451	0, N = 8111	1, N = 341	Difference2	95% CI2,3	p-value2
puntos_dominio_menopausia_salud	40.46 (20.96) 40.01 (24.45, 55.01)	40.39 (20.98) 40.01 (23.90, 55.01)	42.08 (20.56) 38.62 (26.68, 57.93)	-1.7	-9.0, 5.6	0.6
puntos_dominio_psiquico	42.32 (28.77) 40.02 (20.01, 66.70)	42.20 (28.81) 40.02 (20.01, 66.70)	45.12 (27.97) 46.69 (26.68, 66.70)	-2.9	-13, 7.0	0.6
puntos_dominio_sexualidad	43.75 (24.87) 40.00 (30.00, 60.00)	43.46 (24.93) 40.00 (30.00, 60.00)	50.59 (22.82) 50.00 (40.00, 67.50)	-7.1	-15, 1.0	0.084
puntos_dominio_pareja	22.14 (23.24) 20.00 (0.00, 40.00)	22.03 (23.21) 20.00 (0.00, 40.00)	24.71 (24.28) 20.00 (0.00, 37.50)	-2.7	-11, 5.9	0.5
puntos_global	35.19 (18.67) 35.98 (22.50, 48.48)	35.09 (18.57) 35.98 (22.37, 48.48)	37.34 (21.22) 35.91 (26.19, 49.43)	-2.2	-9.7, 5.3	0.5
1 Mean (SD) Median (IQR)
2 Welch Two Sample t-test
3 CI = Confidence Interval

Grupo de edad 51-53

Menopausia %>%
  filter(edadC == "51-53") %>%
  filter(pareja !=1) %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_overall()

Characteristic	Overall, N = 8751	0, N = 8381	1, N = 371	Difference2	95% CI2,3	p-value2
puntos_dominio_menopausia_salud	39.70 (20.10) 39.45 (23.34, 54.45)	39.56 (20.02) 39.45 (22.92, 54.45)	42.89 (21.93) 36.68 (27.78, 56.12)	-3.3	-11, 4.1	0.4
puntos_dominio_psiquico	40.13 (27.59) 40.02 (13.34, 60.03)	39.78 (27.28) 40.02 (13.34, 60.03)	48.13 (33.24) 40.02 (20.01, 80.04)	-8.4	-20, 2.9	0.14
puntos_dominio_sexualidad	45.63 (23.80) 50.00 (30.00, 60.00)	45.43 (23.71) 50.00 (30.00, 60.00)	50.27 (25.76) 50.00 (40.00, 60.00)	-4.8	-14, 3.9	0.3
puntos_dominio_pareja	22.59 (23.79) 20.00 (0.00, 40.00)	22.51 (23.59) 20.00 (0.00, 40.00)	24.59 (28.15) 20.00 (0.00, 40.00)	-2.1	-12, 7.4	0.7
puntos_global	35.34 (17.69) 35.98 (23.48, 47.09)	35.25 (17.54) 35.84 (23.47, 46.67)	37.46 (20.94) 39.74 (25.84, 52.79)	-2.2	-9.3, 4.9	0.5
1 Mean (SD) Median (IQR)
2 Welch Two Sample t-test
3 CI = Confidence Interval

Grupo de edad 54-56

Menopausia %>%
  filter(edadC == "54-56") %>%
  filter(pareja !=1) %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_overall()

Characteristic	Overall, N = 5191	0, N = 4921	1, N = 271	Difference2	95% CI2,3	p-value2
puntos_dominio_menopausia_salud	39.28 (20.42) 39.45 (22.78, 52.79)	39.41 (20.40) 39.45 (22.78, 52.79)	36.96 (21.01) 37.79 (23.90, 45.84)	2.4	-6.0, 11	0.6
puntos_dominio_psiquico	36.88 (26.76) 33.35 (13.34, 53.36)	36.96 (26.93) 33.35 (13.34, 53.36)	35.57 (23.83) 26.68 (20.01, 50.03)	1.4	-8.3, 11	0.8
puntos_dominio_sexualidad	47.34 (25.05) 50.00 (30.00, 60.00)	46.93 (25.06) 50.00 (30.00, 60.00)	54.81 (24.08) 50.00 (40.00, 65.00)	-7.9	-18, 1.9	0.11
puntos_dominio_pareja	24.18 (24.78) 20.00 (0.00, 40.00)	23.98 (24.62) 20.00 (0.00, 40.00)	27.78 (27.78) 20.00 (5.00, 40.00)	-3.8	-15, 7.4	0.5
puntos_global	35.32 (17.89) 34.73 (23.26, 48.00)	35.26 (18.00) 34.66 (22.61, 48.06)	36.39 (16.07) 34.73 (27.78, 44.39)	-1.1	-7.7, 5.4	0.7
1 Mean (SD) Median (IQR)
2 Welch Two Sample t-test
3 CI = Confidence Interval

Grupo de edad 57-59

Menopausia %>%
  filter(edadC == "57-59") %>%
  filter(pareja !=1) %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_overall()

Characteristic	Overall, N = 2441	0, N = 2251	1, N = 191	Difference2	95% CI2,3	p-value2
puntos_dominio_menopausia_salud	38.82 (18.92) 36.68 (24.31, 54.45)	39.17 (18.76) 37.23 (24.45, 54.45)	34.71 (20.73) 27.78 (16.67, 52.23)	4.5	-5.8, 15	0.4
puntos_dominio_psiquico	36.88 (26.70) 33.35 (13.34, 60.03)	37.44 (26.35) 33.35 (13.34, 60.03)	30.19 (30.51) 26.68 (0.00, 36.69)	7.3	-7.8, 22	0.3
puntos_dominio_sexualidad	52.70 (22.93) 50.00 (40.00, 70.00)	52.00 (22.30) 50.00 (40.00, 60.00)	61.05 (28.85) 50.00 (45.00, 90.00)	-9.1	-23, 5.1	0.2
puntos_dominio_pareja	24.84 (24.35) 20.00 (0.00, 40.00)	24.58 (23.56) 20.00 (0.00, 40.00)	27.89 (32.93) 20.00 (0.00, 45.00)	-3.3	-19, 13	0.7
puntos_global	35.97 (18.15) 36.26 (23.66, 48.80)	35.76 (17.83) 36.12 (25.00, 48.48)	38.46 (21.98) 36.67 (21.95, 52.51)	-2.7	-14, 8.1	0.6
1 Mean (SD) Median (IQR)
2 Welch Two Sample t-test
3 CI = Confidence Interval

Grupo de edad 60-62

Menopausia %>%
  filter(edadC == "60-62") %>%
  filter(pareja !=1) %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_overall()

Characteristic	Overall, N = 1141	0, N = 1021	1, N = 121	Difference2	95% CI2,3	p-value2
puntos_dominio_menopausia_salud	35.13 (20.73) 31.95 (17.37, 48.20)	34.76 (20.23) 32.23 (17.23, 47.23)	38.34 (25.38) 28.90 (20.70, 59.32)	-3.6	-20, 13	0.6
puntos_dominio_psiquico	28.20 (24.99) 23.35 (6.67, 46.69)	26.94 (24.83) 20.01 (6.67, 40.02)	38.91 (24.77) 43.36 (20.01, 51.69)	-12	-28, 4.3	0.14
puntos_dominio_sexualidad	46.84 (20.23) 40.00 (32.50, 60.00)	47.25 (19.56) 45.00 (40.00, 60.00)	43.33 (26.05) 40.00 (27.50, 52.50)	3.9	-13, 21	0.6
puntos_dominio_pareja	23.07 (22.85) 20.00 (0.00, 40.00)	22.35 (22.03) 20.00 (0.00, 40.00)	29.17 (29.37) 20.00 (7.50, 45.00)	-6.8	-26, 12	0.5
puntos_global	32.74 (16.28) 31.74 (21.25, 43.20)	32.19 (15.88) 31.74 (21.36, 42.34)	37.44 (19.50) 32.23 (20.11, 51.71)	-5.3	-18, 7.4	0.4
1 Mean (SD) Median (IQR)
2 Welch Two Sample t-test
3 CI = Confidence Interval

Grupo de edad 63-65

Menopausia %>%
  filter(edadC == "63-65") %>%
  filter(pareja !=1) %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
     type = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global) ~ "continuous",
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_overall()

Characteristic	Overall, N = 381	0, N = 301	1, N = 81	Difference2	95% CI2,3	p-value2
puntos_dominio_menopausia_salud	32.58 (17.95) 32.51 (15.70, 45.28)	32.25 (19.23) 31.67 (15.56, 47.79)	33.83 (12.97) 36.96 (25.84, 44.73)	-1.6	-14, 11	0.8
puntos_dominio_psiquico	29.49 (24.21) 20.01 (8.34, 46.69)	28.46 (24.95) 20.01 (6.67, 46.69)	33.35 (22.27) 26.68 (18.34, 50.03)	-4.9	-25, 15	0.6
puntos_dominio_sexualidad	47.37 (25.54) 50.00 (30.00, 60.00)	50.00 (26.00) 50.00 (40.00, 60.00)	37.50 (22.52) 35.00 (27.50, 52.50)	13	-7.6, 33	0.2
puntos_dominio_pareja	25.00 (19.83) 20.00 (2.50, 40.00)	27.00 (20.20) 20.00 (12.50, 40.00)	17.50 (17.53) 20.00 (0.00, 22.50)	9.5	-6.1, 25	0.2
puntos_global	30.18 (18.50) 30.01 (18.02, 44.66)	33.43 (17.45) 36.19 (21.43, 45.81)	18.01 (18.25) 15.77 (0.00, 30.36)	15	-0.48, 31	0.056
1 Mean (SD) Median (IQR)
2 Welch Two Sample t-test
3 CI = Confidence Interval

Grupo de edad 66-68

Menopausia %>%
  filter(edadC == "66-68") %>%
  filter(pareja !=1) %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
     type = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global) ~ "continuous",
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_overall()

Characteristic	Overall, N = 161	0, N = 141	1, N = 21	Difference2	95% CI2,3	p-value2
puntos_dominio_menopausia_salud	32.57 (16.91) 33.62 (18.89, 38.34)	34.77 (16.62) 36.12 (20.14, 40.56)	17.22 (12.57) 17.22 (12.78, 21.67)	18	-39, 75	0.3
puntos_dominio_psiquico	22.93 (24.83) 13.34 (6.67, 35.02)	23.82 (25.73) 13.34 (6.67, 36.69)	16.68 (23.58) 16.68 (8.34, 25.01)	7.1	-118, 132	0.7
puntos_dominio_sexualidad	55.63 (16.32) 50.00 (40.00, 62.50)	55.71 (17.42) 50.00 (40.00, 67.50)	55.00 (7.07) 55.00 (52.50, 57.50)	0.71	-20, 21	>0.9
puntos_dominio_pareja	28.75 (28.95) 20.00 (7.50, 42.50)	27.14 (30.49) 15.00 (2.50, 40.00)	40.00 (14.14) 40.00 (35.00, 45.00)	-13	-57, 31	0.4
puntos_global	32.65 (16.63) 31.67 (23.75, 39.17)	32.71 (17.86) 30.56 (23.47, 40.00)	32.23 (0.98) 32.23 (31.88, 32.57)	0.49	-9.9, 11	>0.9
1 Mean (SD) Median (IQR)
2 Welch Two Sample t-test
3 CI = Confidence Interval

Grupo de edad 69-71

Menopausia %>%
  filter(edadC == "69-71") %>%
  filter(pareja !=1) %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
    type = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global) ~ "continuous",
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_overall()

Characteristic	Overall, N = 51	0, N = 41	1, N = 11	p-value
puntos_dominio_menopausia_salud	27.22 (29.41) 20.00 (8.89, 26.67)	14.72 (10.56) 14.45 (7.50, 21.67)	77.23 (NA) 77.23 (77.23, 77.23)
puntos_dominio_psiquico	28.01 (43.84) 0.00 (0.00, 40.02)	10.01 (20.01) 0.00 (0.00, 10.01)	100.05 (NA) 100.05 (100.05, 100.05)
puntos_dominio_sexualidad	60.00 (24.49) 60.00 (60.00, 80.00)	60.00 (28.28) 70.00 (50.00, 80.00)	60.00 (NA) 60.00 (60.00, 60.00)
puntos_dominio_pareja	22.00 (21.68) 30.00 (0.00, 30.00)	27.50 (20.62) 30.00 (22.50, 35.00)	0.00 (NA) 0.00 (0.00, 0.00)
puntos_global	34.31 (17.86) 34.72 (28.33, 39.17)	28.06 (12.83) 31.53 (23.75, 35.83)	59.32 (NA) 59.32 (59.32, 59.32)
1 Mean (SD) Median (IQR)

Grupo de edad 72-75

Menopausia %>%
  filter(edadC == "72-75") %>%
  filter(pareja !=1) %>%
  tbl_summary(
    by = ha_tenido_un_cancer,
    include = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global),
    type = c(puntos_dominio_menopausia_salud, puntos_dominio_psiquico, puntos_dominio_sexualidad, puntos_dominio_pareja, puntos_global) ~ "continuous",
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_overall()

Characteristic	Overall, N = 1021	0, N = 931	1, N = 91	Difference2	95% CI2,3	p-value2
puntos_dominio_menopausia_salud	36.61 (22.01) 33.06 (18.62, 54.45)	36.02 (22.39) 32.23 (17.78, 54.45)	42.72 (17.44) 43.34 (25.00, 56.12)	-6.7	-21, 7.1	0.3
puntos_dominio_psiquico	37.93 (27.54) 33.35 (15.01, 60.03)	37.58 (28.52) 33.35 (13.34, 60.03)	41.50 (14.45) 33.35 (33.35, 60.03)	-3.9	-16, 8.1	0.5
puntos_dominio_sexualidad	51.76 (25.23) 50.00 (40.00, 70.00)	51.08 (25.60) 50.00 (40.00, 70.00)	58.89 (20.88) 60.00 (50.00, 70.00)	-7.8	-24, 8.7	0.3
puntos_dominio_pareja	20.49 (21.91) 20.00 (0.00, 30.00)	21.40 (22.34) 20.00 (0.00, 30.00)	11.11 (14.53) 0.00 (0.00, 30.00)	10	-1.4, 22	0.079
puntos_global	35.12 (17.28) 33.41 (24.03, 46.78)	34.79 (17.92) 33.34 (22.92, 48.06)	38.56 (7.71) 34.73 (32.78, 43.34)	-3.8	-10, 2.9	0.3
1 Mean (SD) Median (IQR)
2 Welch Two Sample t-test
3 CI = Confidence Interval

2ª parte: Analisis en función de la situación laboral

#porcentaje de la situación laboral
Menopausia %>%
  tbl_summary(
    include = situacion_laboral,
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  )

Characteristic	N = 4,6951
situacion_laboral
1	432.0 / 4,548.0 (9.5%)
2	3,685.0 / 4,548.0 (81.0%)
3	279.0 / 4,548.0 (6.1%)
4	152.0 / 4,548.0 (3.3%)
Unknown	147
1 n / N (%)

#características demográficas en función de situación laboral
#situacion laboral = 1
Menopausia %>%
  filter(situacion_laboral==1) %>%
  tbl_summary(
    include = c(edad, peso_kg, altura_cm, imc, con_que_grupo_se_identificaria_mas, tabaco, alcohol, fue_hace_mas_de_1_ano, ha_tenido_algun_ingreso_reciente, ninguna, cardiopatia, epoc, diabetes_mellitus, enfermedad_autoinmune, trasplante, ictus, problemas_de_higado, dialisis_renal),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) 

Characteristic	N = 4321
edad	52.10 (6.77) 51.00 (48.00, 56.00)
peso_kg	67.68 (15.02) 65.00 (57.60, 75.00)
Unknown	3
altura_cm	161.66 (7.07) 162.00 (158.00, 166.00)
Unknown	5
imc	25.96 (5.81) 24.96 (22.08, 28.42)
Unknown	8
con_que_grupo_se_identificaria_mas
1	329.0 / 415.0 (79.3%)
2	1.0 / 415.0 (0.2%)
4	85.0 / 415.0 (20.5%)
Unknown	17
tabaco
1	312.0 / 429.0 (72.7%)
2	17.0 / 429.0 (4.0%)
3	59.0 / 429.0 (13.8%)
4	41.0 / 429.0 (9.6%)
Unknown	3
alcohol
2	268.0 / 375.0 (71.5%)
3	60.0 / 375.0 (16.0%)
4	47.0 / 375.0 (12.5%)
Unknown	57
fue_hace_mas_de_1_ano	209.0 / 432.0 (48.4%)
ha_tenido_algun_ingreso_reciente	33.0 / 432.0 (7.6%)
ninguna	227.0 / 432.0 (52.5%)
cardiopatia	16.0 / 432.0 (3.7%)
epoc	2.0 / 432.0 (0.5%)
diabetes_mellitus	13.0 / 432.0 (3.0%)
enfermedad_autoinmune	35.0 / 432.0 (8.1%)
trasplante	0.0 / 432.0 (0.0%)
ictus	2.0 / 432.0 (0.5%)
problemas_de_higado	12.0 / 432.0 (2.8%)
dialisis_renal	0.0 / 432.0 (0.0%)
1 Mean (SD) Median (IQR); n / N (%)

#situacion laboral = 2
Menopausia %>%
  filter(situacion_laboral==2) %>%
  tbl_summary(
    include = c(edad, peso_kg, altura_cm, imc, con_que_grupo_se_identificaria_mas, tabaco, alcohol, fue_hace_mas_de_1_ano, ha_tenido_algun_ingreso_reciente, ninguna, cardiopatia, epoc, diabetes_mellitus, enfermedad_autoinmune, trasplante, ictus, problemas_de_higado, dialisis_renal),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  )

Characteristic	N = 3,6851
edad	51.57 (6.21) 51.00 (48.00, 54.00)
peso_kg	66.13 (14.22) 64.00 (57.03, 72.00)
Unknown	27
altura_cm	163.27 (6.98) 163.00 (159.00, 168.00)
Unknown	15
imc	24.83 (5.26) 23.93 (21.61, 26.91)
Unknown	40
con_que_grupo_se_identificaria_mas
1	3,207.0 / 3,611.0 (88.8%)
2	16.0 / 3,611.0 (0.4%)
3	8.0 / 3,611.0 (0.2%)
4	378.0 / 3,611.0 (10.5%)
5	2.0 / 3,611.0 (0.1%)
Unknown	74
tabaco
1	2,465.0 / 3,664.0 (67.3%)
2	184.0 / 3,664.0 (5.0%)
3	530.0 / 3,664.0 (14.5%)
4	485.0 / 3,664.0 (13.2%)
Unknown	21
alcohol
1	16.0 / 3,455.0 (0.5%)
2	2,058.0 / 3,455.0 (59.6%)
3	807.0 / 3,455.0 (23.4%)
4	574.0 / 3,455.0 (16.6%)
Unknown	230
fue_hace_mas_de_1_ano	1,782.0 / 3,685.0 (48.4%)
ha_tenido_algun_ingreso_reciente	247.0 / 3,685.0 (6.7%)
ninguna	2,015.0 / 3,685.0 (54.7%)
cardiopatia	88.0 / 3,685.0 (2.4%)
epoc	30.0 / 3,685.0 (0.8%)
diabetes_mellitus	60.0 / 3,685.0 (1.6%)
enfermedad_autoinmune	274.0 / 3,685.0 (7.4%)
trasplante	7.0 / 3,685.0 (0.2%)
ictus	8.0 / 3,685.0 (0.2%)
problemas_de_higado	62.0 / 3,685.0 (1.7%)
dialisis_renal	2.0 / 3,685.0 (0.1%)
1 Mean (SD) Median (IQR); n / N (%)

#situacion laboral = 3
Menopausia %>%
  filter(situacion_laboral==3) %>%
  tbl_summary(
    include = c(edad, peso_kg, altura_cm, imc, con_que_grupo_se_identificaria_mas, tabaco, alcohol, fue_hace_mas_de_1_ano, ha_tenido_algun_ingreso_reciente, ninguna, cardiopatia, epoc, diabetes_mellitus, enfermedad_autoinmune, trasplante, ictus, problemas_de_higado, dialisis_renal),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  )

Characteristic	N = 2791
edad	51.73 (6.84) 51.00 (48.00, 55.00)
peso_kg	68.28 (16.55) 66.00 (57.00, 75.90)
Unknown	2
altura_cm	162.44 (7.47) 162.00 (158.00, 167.00)
imc	25.96 (6.44) 25.12 (21.72, 28.48)
Unknown	2
con_que_grupo_se_identificaria_mas
1	229.0 / 273.0 (83.9%)
2	1.0 / 273.0 (0.4%)
3	1.0 / 273.0 (0.4%)
4	42.0 / 273.0 (15.4%)
Unknown	6
tabaco
1	167.0 / 276.0 (60.5%)
2	11.0 / 276.0 (4.0%)
3	65.0 / 276.0 (23.6%)
4	33.0 / 276.0 (12.0%)
Unknown	3
alcohol
2	151.0 / 246.0 (61.4%)
3	48.0 / 246.0 (19.5%)
4	47.0 / 246.0 (19.1%)
Unknown	33
fue_hace_mas_de_1_ano	132.0 / 279.0 (47.3%)
ha_tenido_algun_ingreso_reciente	32.0 / 279.0 (11.5%)
ninguna	133.0 / 279.0 (47.7%)
cardiopatia	3.0 / 279.0 (1.1%)
epoc	2.0 / 279.0 (0.7%)
diabetes_mellitus	8.0 / 279.0 (2.9%)
enfermedad_autoinmune	25.0 / 279.0 (9.0%)
trasplante	1.0 / 279.0 (0.4%)
ictus	0.0 / 279.0 (0.0%)
problemas_de_higado	9.0 / 279.0 (3.2%)
dialisis_renal	0.0 / 279.0 (0.0%)
1 Mean (SD) Median (IQR); n / N (%)

#situacion laboral = 4
Menopausia %>%
  filter(situacion_laboral==4) %>%
  tbl_summary(
    include = c(edad, peso_kg, altura_cm, imc, con_que_grupo_se_identificaria_mas, tabaco, alcohol, fue_hace_mas_de_1_ano, ha_tenido_algun_ingreso_reciente, ninguna, cardiopatia, epoc, diabetes_mellitus, enfermedad_autoinmune, trasplante, ictus, problemas_de_higado, dialisis_renal),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  )

Characteristic	N = 1521
edad	57.08 (9.28) 56.00 (50.00, 64.00)
peso_kg	67.64 (13.68) 66.00 (58.00, 75.00)
Unknown	3
altura_cm	162.82 (6.52) 163.00 (158.00, 167.00)
Unknown	2
imc	25.61 (5.43) 24.82 (21.61, 28.18)
Unknown	4
con_que_grupo_se_identificaria_mas
1	136.0 / 147.0 (92.5%)
2	1.0 / 147.0 (0.7%)
4	10.0 / 147.0 (6.8%)
Unknown	5
tabaco
1	93.0 / 151.0 (61.6%)
2	5.0 / 151.0 (3.3%)
3	28.0 / 151.0 (18.5%)
4	25.0 / 151.0 (16.6%)
Unknown	1
alcohol
2	90.0 / 139.0 (64.7%)
3	27.0 / 139.0 (19.4%)
4	22.0 / 139.0 (15.8%)
Unknown	13
fue_hace_mas_de_1_ano	102.0 / 152.0 (67.1%)
ha_tenido_algun_ingreso_reciente	25.0 / 152.0 (16.4%)
ninguna	52.0 / 152.0 (34.2%)
cardiopatia	5.0 / 152.0 (3.3%)
epoc	3.0 / 152.0 (2.0%)
diabetes_mellitus	8.0 / 152.0 (5.3%)
enfermedad_autoinmune	26.0 / 152.0 (17.1%)
trasplante	1.0 / 152.0 (0.7%)
ictus	2.0 / 152.0 (1.3%)
problemas_de_higado	5.0 / 152.0 (3.3%)
dialisis_renal	0.0 / 152.0 (0.0%)
1 Mean (SD) Median (IQR); n / N (%)

#sintomas de menopausia en función de situación laboral
#situación laboral = 1
Menopausia %>%
  filter(situacion_laboral==1) %>%
  tbl_summary(
    include = c(ha_tenido_un_cancer, no_tengo_sintomas, sofocos, sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, tengo_insomnio, estoy_irritada, me_siento_decaida_tengo_el_llanto_facil, dolor_articular_muscular, recibe_medicacion_para_el_sindrome_climaterico),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) 

Characteristic	N = 4321
ha_tenido_un_cancer	19.0 / 358.0 (5.3%)
Unknown	74
no_tengo_sintomas	32.0 / 432.0 (7.4%)
sofocos	267.0 / 432.0 (61.8%)
sequedad_vaginal	231.0 / 432.0 (53.5%)
tengo_dolor_en_las_relaciones_sexuales	134.0 / 432.0 (31.0%)
no_tengo_deseos_de_tener_relaciones_sexuales	240.0 / 432.0 (55.6%)
tengo_insomnio	262.0 / 432.0 (60.6%)
estoy_irritada	224.0 / 432.0 (51.9%)
me_siento_decaida_tengo_el_llanto_facil	214.0 / 432.0 (49.5%)
dolor_articular_muscular	293.0 / 432.0 (67.8%)
recibe_medicacion_para_el_sindrome_climaterico	52.0 / 432.0 (12.0%)
1 n / N (%)

#situación laboral = 2
Menopausia %>%
  filter(situacion_laboral==2) %>%
  tbl_summary(
    include = c(ha_tenido_un_cancer, no_tengo_sintomas, sofocos, sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, tengo_insomnio, estoy_irritada, me_siento_decaida_tengo_el_llanto_facil, dolor_articular_muscular, recibe_medicacion_para_el_sindrome_climaterico),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) 

Characteristic	N = 3,6851
ha_tenido_un_cancer	198.0 / 3,285.0 (6.0%)
Unknown	400
no_tengo_sintomas	275.0 / 3,685.0 (7.5%)
sofocos	2,093.0 / 3,685.0 (56.8%)
sequedad_vaginal	1,908.0 / 3,685.0 (51.8%)
tengo_dolor_en_las_relaciones_sexuales	1,012.0 / 3,685.0 (27.5%)
no_tengo_deseos_de_tener_relaciones_sexuales	1,985.0 / 3,685.0 (53.9%)
tengo_insomnio	2,014.0 / 3,685.0 (54.7%)
estoy_irritada	1,608.0 / 3,685.0 (43.6%)
me_siento_decaida_tengo_el_llanto_facil	1,554.0 / 3,685.0 (42.2%)
dolor_articular_muscular	2,100.0 / 3,685.0 (57.0%)
recibe_medicacion_para_el_sindrome_climaterico	582.0 / 3,685.0 (15.8%)
1 n / N (%)

#situación laboral = 3
Menopausia %>%
  filter(situacion_laboral==3) %>%
  tbl_summary(
    include = c(ha_tenido_un_cancer, no_tengo_sintomas, sofocos, sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, tengo_insomnio, estoy_irritada, me_siento_decaida_tengo_el_llanto_facil, dolor_articular_muscular, recibe_medicacion_para_el_sindrome_climaterico),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) 

Characteristic	N = 2791
ha_tenido_un_cancer	11.0 / 244.0 (4.5%)
Unknown	35
no_tengo_sintomas	22.0 / 279.0 (7.9%)
sofocos	155.0 / 279.0 (55.6%)
sequedad_vaginal	134.0 / 279.0 (48.0%)
tengo_dolor_en_las_relaciones_sexuales	81.0 / 279.0 (29.0%)
no_tengo_deseos_de_tener_relaciones_sexuales	143.0 / 279.0 (51.3%)
tengo_insomnio	150.0 / 279.0 (53.8%)
estoy_irritada	129.0 / 279.0 (46.2%)
me_siento_decaida_tengo_el_llanto_facil	150.0 / 279.0 (53.8%)
dolor_articular_muscular	182.0 / 279.0 (65.2%)
recibe_medicacion_para_el_sindrome_climaterico	38.0 / 279.0 (13.6%)
1 n / N (%)

#situación laboral = 4
Menopausia %>%
  filter(situacion_laboral==4) %>%
  tbl_summary(
    include = c(ha_tenido_un_cancer, no_tengo_sintomas, sofocos, sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, tengo_insomnio, estoy_irritada, me_siento_decaida_tengo_el_llanto_facil, dolor_articular_muscular, recibe_medicacion_para_el_sindrome_climaterico),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) 

Characteristic	N = 1521
ha_tenido_un_cancer	23.0 / 134.0 (17.2%)
Unknown	18
no_tengo_sintomas	10.0 / 152.0 (6.6%)
sofocos	85.0 / 152.0 (55.9%)
sequedad_vaginal	101.0 / 152.0 (66.4%)
tengo_dolor_en_las_relaciones_sexuales	58.0 / 152.0 (38.2%)
no_tengo_deseos_de_tener_relaciones_sexuales	89.0 / 152.0 (58.6%)
tengo_insomnio	95.0 / 152.0 (62.5%)
estoy_irritada	72.0 / 152.0 (47.4%)
me_siento_decaida_tengo_el_llanto_facil	75.0 / 152.0 (49.3%)
dolor_articular_muscular	119.0 / 152.0 (78.3%)
recibe_medicacion_para_el_sindrome_climaterico	26.0 / 152.0 (17.1%)
1 n / N (%)

#recodifica la variable sitaucion_laboral siendo 1, 2=1 y 3,4=2
Menopausia <- Menopausia %>%
  mutate(situacion_laboral2 = recode(situacion_laboral, "1" = "1", "2" = "1", "3" = "2", "4" = "2"))


#características demográficas en función de situación laboral
Menopausia %>%
  tbl_summary(
    by = situacion_laboral2,
    include = c(edad, peso_kg, altura_cm, imc, con_que_grupo_se_identificaria_mas, tabaco, alcohol, fue_hace_mas_de_1_ano, ha_tenido_algun_ingreso_reciente, ninguna, cardiopatia, epoc, diabetes_mellitus, enfermedad_autoinmune, trasplante, ictus, problemas_de_higado, dialisis_renal),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	1, N = 4,1171	2, N = 4311	Difference2	95% CI2,3	p-value2
edad	51.63 (6.27) 51.00 (48.00, 54.00)	53.62 (8.19) 52.00 (48.00, 57.00)	-2.0	-2.8, -1.2	<0.001
peso_kg	66.29 (14.31) 64.00 (57.20, 72.00)	68.06 (15.59) 66.00 (57.10, 75.25)	-1.8	-3.3, -0.22	0.025
Unknown	30	5
altura_cm	163.10 (7.01) 163.00 (159.00, 168.00)	162.57 (7.14) 162.00 (158.00, 167.00)	0.53	-0.18, 1.2	0.15
Unknown	20	2
imc	24.95 (5.33) 24.02 (21.64, 27.10)	25.84 (6.10) 25.00 (21.61, 28.48)	-0.89	-1.5, -0.28	0.004
Unknown	48	6
con_que_grupo_se_identificaria_mas			-0.03	-0.13, 0.07
1	3,536.0 / 4,026.0 (87.8%)	365.0 / 420.0 (86.9%)
2	17.0 / 4,026.0 (0.4%)	2.0 / 420.0 (0.5%)
3	8.0 / 4,026.0 (0.2%)	1.0 / 420.0 (0.2%)
4	463.0 / 4,026.0 (11.5%)	52.0 / 420.0 (12.4%)
5	2.0 / 4,026.0 (0.0%)	0.0 / 420.0 (0.0%)
Unknown	91	11
tabaco			-0.14	-0.24, -0.04
1	2,777.0 / 4,093.0 (67.8%)	260.0 / 427.0 (60.9%)
2	201.0 / 4,093.0 (4.9%)	16.0 / 427.0 (3.7%)
3	589.0 / 4,093.0 (14.4%)	93.0 / 427.0 (21.8%)
4	526.0 / 4,093.0 (12.9%)	58.0 / 427.0 (13.6%)
Unknown	24	4
alcohol			-0.01	-0.11, 0.10
1	16.0 / 3,830.0 (0.4%)	0.0 / 385.0 (0.0%)
2	2,326.0 / 3,830.0 (60.7%)	241.0 / 385.0 (62.6%)
3	867.0 / 3,830.0 (22.6%)	75.0 / 385.0 (19.5%)
4	621.0 / 3,830.0 (16.2%)	69.0 / 385.0 (17.9%)
Unknown	287	46
fue_hace_mas_de_1_ano	1,991.0 / 4,117.0 (48.4%)	234.0 / 431.0 (54.3%)	-5.9%	-11%, -0.86%	0.022
ha_tenido_algun_ingreso_reciente	280.0 / 4,117.0 (6.8%)	57.0 / 431.0 (13.2%)	-6.4%	-9.8%, -3.0%	<0.001
ninguna	2,242.0 / 4,117.0 (54.5%)	185.0 / 431.0 (42.9%)	12%	6.5%, 17%	<0.001
cardiopatia	104.0 / 4,117.0 (2.5%)	8.0 / 431.0 (1.9%)	0.67%	-0.82%, 2.2%	0.5
epoc	32.0 / 4,117.0 (0.8%)	5.0 / 431.0 (1.2%)	-0.38%	-1.6%, 0.79%	0.6
diabetes_mellitus	73.0 / 4,117.0 (1.8%)	16.0 / 431.0 (3.7%)	-1.9%	-3.9%, 0.02%	0.010
enfermedad_autoinmune	309.0 / 4,117.0 (7.5%)	51.0 / 431.0 (11.8%)	-4.3%	-7.6%, -1.0%	0.002
trasplante	7.0 / 4,117.0 (0.2%)	2.0 / 431.0 (0.5%)	-0.29%	-1.1%, 0.49%	0.5
ictus	10.0 / 4,117.0 (0.2%)	2.0 / 431.0 (0.5%)	-0.22%	-1.0%, 0.57%	0.7
problemas_de_higado	74.0 / 4,117.0 (1.8%)	14.0 / 431.0 (3.2%)	-1.5%	-3.3%, 0.40%	0.058
dialisis_renal	2.0 / 4,117.0 (0.0%)	0.0 / 431.0 (0.0%)	0.05%	-0.07%, 0.16%	>0.9
1 Mean (SD) Median (IQR); n / N (%)
2 Welch Two Sample t-test; Standardized Mean Difference; Two sample test for equality of proportions
3 CI = Confidence Interval

#síntomas en función de situación laboral (situación laboral comovariable dicotómica)
Menopausia %>%
  tbl_summary(
    by = situacion_laboral2,
    include = c(ha_tenido_un_cancer, no_tengo_sintomas, sofocos, sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, tengo_insomnio, estoy_irritada, me_siento_decaida_tengo_el_llanto_facil, dolor_articular_muscular, recibe_medicacion_para_el_sindrome_climaterico),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	1, N = 4,1171	2, N = 4311	Difference2	95% CI2,3	p-value2
ha_tenido_un_cancer	217.0 / 3,643.0 (6.0%)	34.0 / 378.0 (9.0%)	-3.0%	-6.2%, 0.09%	0.027
Unknown	474	53
no_tengo_sintomas	307.0 / 4,117.0 (7.5%)	32.0 / 431.0 (7.4%)	0.03%	-2.6%, 2.7%	>0.9
sofocos	2,360.0 / 4,117.0 (57.3%)	240.0 / 431.0 (55.7%)	1.6%	-3.4%, 6.7%	0.5
sequedad_vaginal	2,139.0 / 4,117.0 (52.0%)	235.0 / 431.0 (54.5%)	-2.6%	-7.6%, 2.5%	0.3
tengo_dolor_en_las_relaciones_sexuales	1,146.0 / 4,117.0 (27.8%)	139.0 / 431.0 (32.3%)	-4.4%	-9.2%, 0.33%	0.060
no_tengo_deseos_de_tener_relaciones_sexuales	2,225.0 / 4,117.0 (54.0%)	232.0 / 431.0 (53.8%)	0.22%	-4.9%, 5.3%	>0.9
tengo_insomnio	2,276.0 / 4,117.0 (55.3%)	245.0 / 431.0 (56.8%)	-1.6%	-6.6%, 3.5%	0.6
estoy_irritada	1,832.0 / 4,117.0 (44.5%)	201.0 / 431.0 (46.6%)	-2.1%	-7.2%, 2.9%	0.4
me_siento_decaida_tengo_el_llanto_facil	1,768.0 / 4,117.0 (42.9%)	225.0 / 431.0 (52.2%)	-9.3%	-14%, -4.2%	<0.001
dolor_articular_muscular	2,393.0 / 4,117.0 (58.1%)	301.0 / 431.0 (69.8%)	-12%	-16%, -7.0%	<0.001
recibe_medicacion_para_el_sindrome_climaterico	634.0 / 4,117.0 (15.4%)	64.0 / 431.0 (14.8%)	0.55%	-3.1%, 4.2%	0.8
1 n / N (%)
2 Two sample test for equality of proportions
3 CI = Confidence Interval

3ª parte: Depresión y ansiedad en la menopausia

Prevalencia de depresión y ansiedad

#prevalencia de depresión y ansiedad
Menopausia %>%
  tbl_summary(
    include = c(ha_sido_diagnosticada_de_depresion_ansiedad),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  )

Characteristic	N = 4,6951
ha_sido_diagnosticada_de_depresion_ansiedad	1,064.0 / 4,249.0 (25.0%)
Unknown	446
1 n / N (%)

Porcentaje de mujeres en tratamiento

#porcentaje de mujeres en tratamiento

Menopausia %>%
  filter(ha_sido_diagnosticada_de_depresion_ansiedad == 1) %>%
  tbl_summary(
    include = c(toma_medicacion_para_ello, indique_que_tratamiento_toma),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  )

Characteristic	N = 1,0641
toma_medicacion_para_ello	612.0 / 1,064.0 (57.5%)
indique_que_tratamiento_toma
0,25 mg de lorazepan al dia y 0,50 mg de paroxetina al dia	1.0 / 590.0 (0.2%)
0,5 ravotril	1.0 / 590.0 (0.2%)
2 antidepresivos: Mirtazapina y Pristiq, además de rivotril. Pregabalina 75l	1.0 / 590.0 (0.2%)
Actualmente hiperico y 5htp.anteriormente paroxetina	1.0 / 590.0 (0.2%)
Adoren, Brintellix	1.0 / 590.0 (0.2%)
Alparazolan y fluxetina	1.0 / 590.0 (0.2%)
Alpeazolam	1.0 / 590.0 (0.2%)
Alprazolam	8.0 / 590.0 (1.4%)
Alprazolam 0,5	1.0 / 590.0 (0.2%)
Alprazolam 0'25	1.0 / 590.0 (0.2%)
Alprazolam esporádicamente crisis	1.0 / 590.0 (0.2%)
Alprazolam, lorazepam	1.0 / 590.0 (0.2%)
Alprazolam, paroxetina, valium	1.0 / 590.0 (0.2%)
Alprazolam, Propanodol	1.0 / 590.0 (0.2%)
Alprazolan	5.0 / 590.0 (0.8%)
Altruline	1.0 / 590.0 (0.2%)
Amprazolan y melatonina	1.0 / 590.0 (0.2%)
Amsium xeristar	2.0 / 590.0 (0.3%)
Anafranil	1.0 / 590.0 (0.2%)
Anafranil 100mg. 2 veces al día y traxilium 3 veces al dia	1.0 / 590.0 (0.2%)
Anafranil 10mg	1.0 / 590.0 (0.2%)
Ansiolitico	1.0 / 590.0 (0.2%)
ansiolitico, antidepresivo	1.0 / 590.0 (0.2%)
Ansiolíticos	2.0 / 590.0 (0.3%)
Ansiolíticos y antes depresivos	1.0 / 590.0 (0.2%)
Ansioliticos y antidepresivos	1.0 / 590.0 (0.2%)
Ansiomed	1.0 / 590.0 (0.2%)
Antesepresivo	1.0 / 590.0 (0.2%)
Antidepresivos	2.0 / 590.0 (0.3%)
Antidepresivos y benzodiazapinas	1.0 / 590.0 (0.2%)
Antidepresivos y para la TA	1.0 / 590.0 (0.2%)
Antidepresivos y tratamiento migraña	1.0 / 590.0 (0.2%)
Antidepresivos, ansioliticos, somníferos	1.0 / 590.0 (0.2%)
Antideptrdivo y ansiolítico	1.0 / 590.0 (0.2%)
Atarax	1.0 / 590.0 (0.2%)
Bandral 75 mgr	1.0 / 590.0 (0.2%)
bersitran	1.0 / 590.0 (0.2%)
Besitran	3.0 / 590.0 (0.5%)
Besitran 50	2.0 / 590.0 (0.3%)
Besitran lexatin	1.0 / 590.0 (0.2%)
Besitran O,25	1.0 / 590.0 (0.2%)
Boltin	1.0 / 590.0 (0.2%)
Brentellix	1.0 / 590.0 (0.2%)
Brintelix	3.0 / 590.0 (0.5%)
Brintelix 15 mg	1.0 / 590.0 (0.2%)
Brintellis	2.0 / 590.0 (0.3%)
brintellix	1.0 / 590.0 (0.2%)
Brintellix	3.0 / 590.0 (0.5%)
Brintellix 10 mg	1.0 / 590.0 (0.2%)
Brintellix 15 mg	1.0 / 590.0 (0.2%)
Brintellix 20	1.0 / 590.0 (0.2%)
Brintellix 20 mg 1 al dia	1.0 / 590.0 (0.2%)
Brintellix y alprazolan	1.0 / 590.0 (0.2%)
Brintellix y Deprax	1.0 / 590.0 (0.2%)
Brintellix y Elontril	1.0 / 590.0 (0.2%)
Brintellix y rivotril	2.0 / 590.0 (0.3%)
Brintellix20 mg	1.0 / 590.0 (0.2%)
Bromacepam	2.0 / 590.0 (0.3%)
Bupropion y lormetazepan	1.0 / 590.0 (0.2%)
Captopril	1.0 / 590.0 (0.2%)
Centralina	1.0 / 590.0 (0.2%)
Cilitralopram, 20 GM.	1.0 / 590.0 (0.2%)
Cimbalta	1.0 / 590.0 (0.2%)
Cimbalta y lorazepan	1.0 / 590.0 (0.2%)
Cimbalta, pantoprazol, triptizol 10, atrovastatina, deprax ,1 noches	1.0 / 590.0 (0.2%)
Citalopram	7.0 / 590.0 (1.2%)
Citalopram 10mg	1.0 / 590.0 (0.2%)
Citalopram y aprozalam	1.0 / 590.0 (0.2%)
Citalopram y Tranquimazin	1.0 / 590.0 (0.2%)
Citalopran	4.0 / 590.0 (0.7%)
Citalopran 20	1.0 / 590.0 (0.2%)
Citalopran 20mg	1.0 / 590.0 (0.2%)
Cllnazepam	1.0 / 590.0 (0.2%)
Clonazepam	2.0 / 590.0 (0.3%)
Clonazepan	2.0 / 590.0 (0.3%)
Clorazepan sertralina	1.0 / 590.0 (0.2%)
Clordiazepixido	1.0 / 590.0 (0.2%)
clordiazepóxido	1.0 / 590.0 (0.2%)
Cotalopram y trankimazin	1.0 / 590.0 (0.2%)
Crisomet	2.0 / 590.0 (0.3%)
Crisomet 1 al día por la noche	1.0 / 590.0 (0.2%)
Cymbalta	3.0 / 590.0 (0.5%)
Cymbalta 30	1.0 / 590.0 (0.2%)
Cymbalta 60 mg	1.0 / 590.0 (0.2%)
Cymbalta 90, Seroquel Prolong 100, Zolpidem 10, Orfidal 30, Diazepam 10	1.0 / 590.0 (0.2%)
Cymbalta60	1.0 / 590.0 (0.2%)
Deprax	5.0 / 590.0 (0.8%)
Deprax 1 comprimido por la noche	1.0 / 590.0 (0.2%)
Deprax media pastilla por la noche	1.0 / 590.0 (0.2%)
Desvenlafaxina	2.0 / 590.0 (0.3%)
Desvenlafaxina/trazodona	1.0 / 590.0 (0.2%)
Diacepan	2.0 / 590.0 (0.3%)
diazepam	1.0 / 590.0 (0.2%)
Diazepam	5.0 / 590.0 (0.8%)
Diazepam y Tramadol	1.0 / 590.0 (0.2%)
Diazepam, noctamid y mirtazapina	1.0 / 590.0 (0.2%)
Diazepan	7.0 / 590.0 (1.2%)
Diazepan , y Cymbalta	1.0 / 590.0 (0.2%)
Diazepan tryptizol	1.0 / 590.0 (0.2%)
Discrepan de 5	1.0 / 590.0 (0.2%)
Dobupal	1.0 / 590.0 (0.2%)
Duloxatina	1.0 / 590.0 (0.2%)
Duloxcetina	1.0 / 590.0 (0.2%)
Duloxetina	4.0 / 590.0 (0.7%)
Duloxetina 30	1.0 / 590.0 (0.2%)
Duloxetina 60 mg 1 al día. Lorazepam 1 mg. 2 al día	1.0 / 590.0 (0.2%)
duloxetina 60mg una vez al día	1.0 / 590.0 (0.2%)
Duloxetina y orfidal	1.0 / 590.0 (0.2%)
Duloxetina y Orfidal	1.0 / 590.0 (0.2%)
Duloxetina, lormetazepam, esomeprazol,	1.0 / 590.0 (0.2%)
Duloxetina,tepazepam,loracepam	1.0 / 590.0 (0.2%)
Ecitalopran	1.0 / 590.0 (0.2%)
Efecto	1.0 / 590.0 (0.2%)
Elontril	2.0 / 590.0 (0.3%)
Elontril y orfidal	1.0 / 590.0 (0.2%)
Enzude	1.0 / 590.0 (0.2%)
Es medicamento natural	1.0 / 590.0 (0.2%)
Esacitalopran y lorazepan	1.0 / 590.0 (0.2%)
Escilatopram, mirtazapina, bromacepam, tidazazina	1.0 / 590.0 (0.2%)
escitalopram	1.0 / 590.0 (0.2%)
Escitalopram	28.0 / 590.0 (4.7%)
Escitaloprám	1.0 / 590.0 (0.2%)
Escitalopram 5 mg	1.0 / 590.0 (0.2%)
escitalopram 10	1.0 / 590.0 (0.2%)
Escitalopram 10 mg	2.0 / 590.0 (0.3%)
Escitalopram 10 mg 1	1.0 / 590.0 (0.2%)
ESCITALOPRAM 10 MG TABLET	1.0 / 590.0 (0.2%)
Escitalopram 10mg	1.0 / 590.0 (0.2%)
Escitalopram 15mg	3.0 / 590.0 (0.5%)
Escitalopram 20. Rivotril 0’5	1.0 / 590.0 (0.2%)
Escitalopram 20mg	1.0 / 590.0 (0.2%)
Escitalopram 5 mg	1.0 / 590.0 (0.2%)
Escitalopram 50/ dia	1.0 / 590.0 (0.2%)
escitalopram y lorazepam	1.0 / 590.0 (0.2%)
Escitalopram y lorazepam	1.0 / 590.0 (0.2%)
Escitalopram y orfidal	1.0 / 590.0 (0.2%)
Escitalopram y Sedistress	1.0 / 590.0 (0.2%)
Escitalopram y trankimazin	2.0 / 590.0 (0.3%)
Escitalopram y trazodona	1.0 / 590.0 (0.2%)
Escitalopran	3.0 / 590.0 (0.5%)
Escitalopran 20mg 1y medio al día. Rivotril gotas y Trankimacín	1.0 / 590.0 (0.2%)
Escitalopran ratio 10mg	1.0 / 590.0 (0.2%)
escitalopran y lorazepan	1.0 / 590.0 (0.2%)
Escitalopran, lorazepan	1.0 / 590.0 (0.2%)
Escitoplan	1.0 / 590.0 (0.2%)
Escotalopram	1.0 / 590.0 (0.2%)
Escutalopram	1.0 / 590.0 (0.2%)
Esertia	2.0 / 590.0 (0.3%)
Esertia gotas	1.0 / 590.0 (0.2%)
Esitalopram	1.0 / 590.0 (0.2%)
Eutirox tasedan Keppra500mg	1.0 / 590.0 (0.2%)
Excitalopla	1.0 / 590.0 (0.2%)
Excitalopram	2.0 / 590.0 (0.3%)
Excitalopram 15	1.0 / 590.0 (0.2%)
Excitralopam	1.0 / 590.0 (0.2%)
Felicita	1.0 / 590.0 (0.2%)
Felícita	1.0 / 590.0 (0.2%)
Fluocetina	1.0 / 590.0 (0.2%)
Fluoroxetina lorazepan	1.0 / 590.0 (0.2%)
Fluoxac	1.0 / 590.0 (0.2%)
fluoxetin y trittico	1.0 / 590.0 (0.2%)
Fluoxetina	26.0 / 590.0 (4.4%)
FLUOXETINA	1.0 / 590.0 (0.2%)
Fluoxetina 20	1.0 / 590.0 (0.2%)
Fluoxetina 20 mg	1.0 / 590.0 (0.2%)
Fluoxetina y cloraxane	1.0 / 590.0 (0.2%)
Fluoxetina y Deprax	1.0 / 590.0 (0.2%)
Fluoxetina y orfidal	1.0 / 590.0 (0.2%)
Fluoxetina y seroxat	1.0 / 590.0 (0.2%)
Fluoxetina y zolpidem	1.0 / 590.0 (0.2%)
Fluoxetina, alprazolam y tranxilium	1.0 / 590.0 (0.2%)
Fluoxetina, Alprazolam	1.0 / 590.0 (0.2%)
Fluoxetina, alprazolam, myrtazapina, melatonina	1.0 / 590.0 (0.2%)
Fluoxetina, Elintril, Dormodor, Atarax, Abilify	1.0 / 590.0 (0.2%)
Fluoxetina, Lormetazepan	1.0 / 590.0 (0.2%)
Fluoxetina, modalfinilo 400 al dia	1.0 / 590.0 (0.2%)
Fluoxetina, trankimazin	1.0 / 590.0 (0.2%)
Fluoxetina,lorazepan	1.0 / 590.0 (0.2%)
Fluoxitina	1.0 / 590.0 (0.2%)
Frisium	1.0 / 590.0 (0.2%)
Heipram	4.0 / 590.0 (0.7%)
Heipram y trankimacin	1.0 / 590.0 (0.2%)
Heipram, Duloxetina, Trankimazin	1.0 / 590.0 (0.2%)
Heipram. Lorazepan	1.0 / 590.0 (0.2%)
Heipran	1.0 / 590.0 (0.2%)
Heipran 20	1.0 / 590.0 (0.2%)
Hiepram, alprazolan 1mg	1.0 / 590.0 (0.2%)
HIPÉRICO	1.0 / 590.0 (0.2%)
Ibritinib 400mg	1.0 / 590.0 (0.2%)
Lamictal 25 mg	1.0 / 590.0 (0.2%)
Lamotrigina	1.0 / 590.0 (0.2%)
Lamotrigina venlafaxins	1.0 / 590.0 (0.2%)
Lavanda	1.0 / 590.0 (0.2%)
Lexatin	10.0 / 590.0 (1.7%)
Lexatín	1.0 / 590.0 (0.2%)
Lexatin 1,5	1.0 / 590.0 (0.2%)
Lexatín ocasional	1.0 / 590.0 (0.2%)
Lexatín y Fluoxetina	1.0 / 590.0 (0.2%)
Lexatin, Orfidal	2.0 / 590.0 (0.3%)
Lexatin,pristik	1.0 / 590.0 (0.2%)
LiRica,ToPiRamaTo,FLuoXeTiNa,DePRaX,eTc.....	1.0 / 590.0 (0.2%)
liryca,cimbalta	1.0 / 590.0 (0.2%)
LITIO	1.0 / 590.0 (0.2%)
Loracepam	2.0 / 590.0 (0.3%)
Loracepan	2.0 / 590.0 (0.3%)
Lorazapan	1.0 / 590.0 (0.2%)
Lorazepam	13.0 / 590.0 (2.2%)
lorazepam 025, para dormir	1.0 / 590.0 (0.2%)
Lorazepam 1 MG y fluoxetina	1.0 / 590.0 (0.2%)
Lorazepam 1omg al dia	1.0 / 590.0 (0.2%)
Lorazepam y Citalopram	1.0 / 590.0 (0.2%)
Lorazepan	1.0 / 590.0 (0.2%)
Lorazepan y Sertralina	1.0 / 590.0 (0.2%)
Lorazepan, Citalopram	1.0 / 590.0 (0.2%)
Lormetasepan	1.0 / 590.0 (0.2%)
Lormetazepam	1.0 / 590.0 (0.2%)
Lormetazepan	1.0 / 590.0 (0.2%)
Mirtazapina	3.0 / 590.0 (0.5%)
Mirtazapina 15mg	1.0 / 590.0 (0.2%)
Mirtazapina lexatin	1.0 / 590.0 (0.2%)
Mirtazapina, citalopram	1.0 / 590.0 (0.2%)
Naturales	1.0 / 590.0 (0.2%)
Naturista	1.0 / 590.0 (0.2%)
Ninguno	1.0 / 590.0 (0.2%)
No	4.0 / 590.0 (0.7%)
No lo se	1.0 / 590.0 (0.2%)
Noctamid,lorazepan,deprax,heipran,abiliti,zipresa,misertapina	1.0 / 590.0 (0.2%)
Orbidal y Noctamid 1mg	1.0 / 590.0 (0.2%)
Orfidal	11.0 / 590.0 (1.9%)
Orfidal (esporádicamente), melatonina, suplementos vitaminas	1.0 / 590.0 (0.2%)
Orfidal Vandral 150 Deprax 100	1.0 / 590.0 (0.2%)
Orfidal-lormatazepan	1.0 / 590.0 (0.2%)
Orfidal, escitalopram zolpidem	1.0 / 590.0 (0.2%)
Orfidal/ lexatin a demanda	1.0 / 590.0 (0.2%)
Oxitril	1.0 / 590.0 (0.2%)
paroxetina	2.0 / 590.0 (0.3%)
Paroxetina	19.0 / 590.0 (3.2%)
PAROXETINA	2.0 / 590.0 (0.3%)
Paroxetina , gabapentina ,diazepan	1.0 / 590.0 (0.2%)
Paroxetina 20 mg	1.0 / 590.0 (0.2%)
Paroxetina 20, carbonato de litio, quetiapina 200/25	1.0 / 590.0 (0.2%)
Paroxetina 20mg y Tranquimazin en ocasiones	1.0 / 590.0 (0.2%)
Paroxetina y elontril	1.0 / 590.0 (0.2%)
Paroxetina y Triptizom	1.0 / 590.0 (0.2%)
Paroxetina, Lorazepam	1.0 / 590.0 (0.2%)
Pastillas	1.0 / 590.0 (0.2%)
Pregabalina y Paroxetina mañanas	1.0 / 590.0 (0.2%)
Premax, muy esporádicamente	1.0 / 590.0 (0.2%)
Prestiq, Distraneurine, Lyrica, Rivotril	1.0 / 590.0 (0.2%)
Prisquik	1.0 / 590.0 (0.2%)
Pristic	1.0 / 590.0 (0.2%)
Prístic	1.0 / 590.0 (0.2%)
Pristic 100mg y lorazepan 1 mg	1.0 / 590.0 (0.2%)
Pristic y lorazepam	1.0 / 590.0 (0.2%)
Pristik	2.0 / 590.0 (0.3%)
pristiq	1.0 / 590.0 (0.2%)
Pristiq	2.0 / 590.0 (0.3%)
Pristiq 200	1.0 / 590.0 (0.2%)
Pristiq 50mg, tranxilium 5mg, deprax 100 mg	1.0 / 590.0 (0.2%)
Pristiq y Sedotime	1.0 / 590.0 (0.2%)
Pristiq, orfidal	1.0 / 590.0 (0.2%)
Pristiq. Deprax	1.0 / 590.0 (0.2%)
Pristique	1.0 / 590.0 (0.2%)
Prozac	3.0 / 590.0 (0.5%)
Prozac , orfidal	1.0 / 590.0 (0.2%)
Prozac 20mg y rivotril 0'50mg	1.0 / 590.0 (0.2%)
Prozac Diazepam	1.0 / 590.0 (0.2%)
Realta	1.0 / 590.0 (0.2%)
Risperdal y sertralina	1.0 / 590.0 (0.2%)
Rivoltril	1.0 / 590.0 (0.2%)
Rivotril o Lexatin	1.0 / 590.0 (0.2%)
Rivotril y sertralina	1.0 / 590.0 (0.2%)
Rize	1.0 / 590.0 (0.2%)
Salbacolina y lorazepan	1.0 / 590.0 (0.2%)
Scitalopram	1.0 / 590.0 (0.2%)
Sedosil serenus	1.0 / 590.0 (0.2%)
Sedotime	2.0 / 590.0 (0.3%)
Sedotime 15mg	1.0 / 590.0 (0.2%)
Sedotime y excitalopram	1.0 / 590.0 (0.2%)
Senselix	1.0 / 590.0 (0.2%)
Seropram	1.0 / 590.0 (0.2%)
Seropram 30mg	1.0 / 590.0 (0.2%)
Serotonina	1.0 / 590.0 (0.2%)
Seroxar plus 20mg	1.0 / 590.0 (0.2%)
Seroxat	1.0 / 590.0 (0.2%)
Sertealina y Rivotril	1.0 / 590.0 (0.2%)
sertralina	1.0 / 590.0 (0.2%)
Sertralina	27.0 / 590.0 (4.6%)
Sertralina + trazodona	1.0 / 590.0 (0.2%)
Sertralina 150	1.0 / 590.0 (0.2%)
Sertralina y bromazepan	1.0 / 590.0 (0.2%)
Sertralina y bupropion	1.0 / 590.0 (0.2%)
Sertralina y clomipramina	1.0 / 590.0 (0.2%)
Sertralina y Mirtazapina	1.0 / 590.0 (0.2%)
Sertralina y valium	1.0 / 590.0 (0.2%)
Sertralina, Alprazolan	1.0 / 590.0 (0.2%)
Sertralina, lorazepam	1.0 / 590.0 (0.2%)
Sertralina, mirtazapia, transilium, sedotime	1.0 / 590.0 (0.2%)
Sertralina, mirtazapina, transilium, sedotime	1.0 / 590.0 (0.2%)
Sertralina. Abilifi	1.0 / 590.0 (0.2%)
Sertralina. Bupropion.	1.0 / 590.0 (0.2%)
Sinogan	1.0 / 590.0 (0.2%)
Sitalopram. Lorazepam	1.0 / 590.0 (0.2%)
Tome y tuve	1.0 / 590.0 (0.2%)
Trankimacin	1.0 / 590.0 (0.2%)
Trankimaxin(alprazolan)	1.0 / 590.0 (0.2%)
Trankimazin	1.0 / 590.0 (0.2%)
Trankimazin y Fluoxetina	1.0 / 590.0 (0.2%)
Tranquimacin	1.0 / 590.0 (0.2%)
Tranquimacin 1mg y escitalopran 10mgr	1.0 / 590.0 (0.2%)
Tranxilium 5 mg. y Vandral. Aquilea sueño y otros fármacos venta libre para palpitaciones, taquicardia.	1.0 / 590.0 (0.2%)
Tranxilium pero lo tomo solo en ocasiones puntuales	1.0 / 590.0 (0.2%)
trictisol	1.0 / 590.0 (0.2%)
Triptizol	4.0 / 590.0 (0.7%)
Triptizol 25	1.0 / 590.0 (0.2%)
Triptizol lorazepan	1.0 / 590.0 (0.2%)
Triptysol, Lyrica	1.0 / 590.0 (0.2%)
Tryptizol y Sertralina	1.0 / 590.0 (0.2%)
Valeriana	1.0 / 590.0 (0.2%)
Valium	1.0 / 590.0 (0.2%)
Valium 10 , sentralina últimamente	1.0 / 590.0 (0.2%)
Vandral	2.0 / 590.0 (0.3%)
Velafaxina	2.0 / 590.0 (0.3%)
Velafaxina y lormetazepan	1.0 / 590.0 (0.2%)
Velafaxinq	1.0 / 590.0 (0.2%)
Velanfaxina	2.0 / 590.0 (0.3%)
Venlafaxina	13.0 / 590.0 (2.2%)
Venlafaxina 175 diarios desde hace 1 año	1.0 / 590.0 (0.2%)
Venlafaxina 300mg/Lamictal 100mg/Olanzapina 5mg/Noctamid 1 mg	1.0 / 590.0 (0.2%)
venlafaxina 75	1.0 / 590.0 (0.2%)
Venlafaxina 75 mg	1.0 / 590.0 (0.2%)
Venlafaxina, lexatin	1.0 / 590.0 (0.2%)
Venlafaxina, lorazepan, Diazepam	1.0 / 590.0 (0.2%)
Venlaflaxina y Lorazepam	1.0 / 590.0 (0.2%)
Vistaril 50 mg	1.0 / 590.0 (0.2%)
Vortioxetina 15mg lorazepam 2 mg	1.0 / 590.0 (0.2%)
Votioxetina 20mg	1.0 / 590.0 (0.2%)
Xanax en caso de necesitarlo	1.0 / 590.0 (0.2%)
Xeristar	2.0 / 590.0 (0.3%)
Xeristar 60	1.0 / 590.0 (0.2%)
Xeristar 60 gm	1.0 / 590.0 (0.2%)
Xeristar 60 y 30	1.0 / 590.0 (0.2%)
Xeristar 60, Xeristar 30 y Ansium	1.0 / 590.0 (0.2%)
Xeristar, lyrica	2.0 / 590.0 (0.3%)
Xeristar, Lyrica	2.0 / 590.0 (0.3%)
Xeristar, Lyrica, Lenzetto	1.0 / 590.0 (0.2%)
Xeristar, lyrica, lormetazepam	1.0 / 590.0 (0.2%)
Zafril	1.0 / 590.0 (0.2%)
Zaleris, lorazepan	1.0 / 590.0 (0.2%)
Zaleris,zolpiden. Terapia con psicologo	1.0 / 590.0 (0.2%)
Zarelis 225 mg y zolpiden 5	1.0 / 590.0 (0.2%)
Zarelis 225 ml	1.0 / 590.0 (0.2%)
Zerelid	1.0 / 590.0 (0.2%)
Zispric	1.0 / 590.0 (0.2%)
Zonegran, fluoxetina, diazepan	1.0 / 590.0 (0.2%)
Zopiclona	1.0 / 590.0 (0.2%)
Zopiclona 1 al dia	1.0 / 590.0 (0.2%)
Zypreza 5mg	1.0 / 590.0 (0.2%)
Unknown	474
1 n / N (%)

relación de la depresión/ansiedad con el tiempo de menopausia

#relación de la depresión/ansiedad con el tiempo de menopausia
Menopausia %>%
  tbl_summary(
    by = ha_sido_diagnosticada_de_depresion_ansiedad,
       include = fue_hace_mas_de_1_ano,
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,1851	1, N = 1,0641	Difference2	95% CI2,3	p-value2
fue_hace_mas_de_1_ano	1,560.0 / 3,185.0 (49.0%)	506.0 / 1,064.0 (47.6%)	1.4%	-2.1%, 5.0%	0.4
1 n / N (%)
2 Two sample test for equality of proportions
3 CI = Confidence Interval

Relación de la depresión/ansiedad con los síntomas de la menopausia

#relación de la depresión/ansiedad con los síntomas de la menopausia

Menopausia %>%
  tbl_summary(
    by = ha_sido_diagnosticada_de_depresion_ansiedad,
    include = c(no_tengo_sintomas, sofocos, sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, tengo_insomnio, estoy_irritada, me_siento_decaida_tengo_el_llanto_facil, dolor_articular_muscular),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,1851	1, N = 1,0641	Difference2	95% CI2,3	p-value2
no_tengo_sintomas	266.0 / 3,185.0 (8.4%)	56.0 / 1,064.0 (5.3%)	3.1%	1.4%, 4.8%	0.001
sofocos	1,758.0 / 3,185.0 (55.2%)	645.0 / 1,064.0 (60.6%)	-5.4%	-8.9%, -2.0%	0.002
sequedad_vaginal	1,612.0 / 3,185.0 (50.6%)	591.0 / 1,064.0 (55.5%)	-4.9%	-8.4%, -1.4%	0.006
tengo_dolor_en_las_relaciones_sexuales	872.0 / 3,185.0 (27.4%)	324.0 / 1,064.0 (30.5%)	-3.1%	-6.3%, 0.16%	0.059
no_tengo_deseos_de_tener_relaciones_sexuales	1,657.0 / 3,185.0 (52.0%)	641.0 / 1,064.0 (60.2%)	-8.2%	-12%, -4.7%	<0.001
tengo_insomnio	1,686.0 / 3,185.0 (52.9%)	666.0 / 1,064.0 (62.6%)	-9.7%	-13%, -6.2%	<0.001
estoy_irritada	1,331.0 / 3,185.0 (41.8%)	573.0 / 1,064.0 (53.9%)	-12%	-16%, -8.6%	<0.001
me_siento_decaida_tengo_el_llanto_facil	1,247.0 / 3,185.0 (39.2%)	637.0 / 1,064.0 (59.9%)	-21%	-24%, -17%	<0.001
dolor_articular_muscular	1,792.0 / 3,185.0 (56.3%)	732.0 / 1,064.0 (68.8%)	-13%	-16%, -9.2%	<0.001
1 n / N (%)
2 Two sample test for equality of proportions
3 CI = Confidence Interval

Relación de la depresión/ansiedad con las edad, peso, imc, etc.

#relación de la depresión/ansiedad con las edad, peso, imc, etc.


Menopausia %>%
  tbl_summary(
    by = ha_sido_diagnosticada_de_depresion_ansiedad,
    include = c(edad, peso_kg, altura_cm, imc, con_que_grupo_se_identificaria_mas, tabaco, alcohol,  ha_tenido_algun_ingreso_reciente, ninguna, cardiopatia, epoc, diabetes_mellitus, enfermedad_autoinmune, trasplante, ictus, problemas_de_higado, dialisis_renal),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ), 
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,1851	1, N = 1,0641	Difference2	95% CI2,3	p-value2
edad	51.79 (6.36) 51.00 (48.00, 54.00)	51.62 (6.63) 51.00 (48.00, 54.00)	0.17	-0.28, 0.63	0.5
peso_kg	65.91 (13.96) 63.50 (57.00, 72.00)	68.32 (15.89) 65.00 (58.00, 75.60)	-2.4	-3.5, -1.3	<0.001
Unknown	26	11
altura_cm	163.28 (7.03) 163.00 (159.00, 168.00)	162.48 (6.92) 163.00 (158.00, 167.00)	0.80	0.32, 1.3	0.001
Unknown	14	6
imc	24.75 (5.20) 23.88 (21.56, 26.84)	25.90 (6.00) 24.84 (22.04, 28.33)	-1.2	-1.6, -0.75	<0.001
Unknown	37	15
con_que_grupo_se_identificaria_mas			-0.01	-0.08, 0.06
1	2,749.0 / 3,118.0 (88.2%)	902.0 / 1,029.0 (87.7%)
2	12.0 / 3,118.0 (0.4%)	6.0 / 1,029.0 (0.6%)
3	7.0 / 3,118.0 (0.2%)	2.0 / 1,029.0 (0.2%)
4	348.0 / 3,118.0 (11.2%)	119.0 / 1,029.0 (11.6%)
5	2.0 / 3,118.0 (0.1%)	0.0 / 1,029.0 (0.0%)
Unknown	67	35
tabaco			-0.13	-0.20, -0.06
1	2,201.0 / 3,162.0 (69.6%)	652.0 / 1,053.0 (61.9%)
2	141.0 / 3,162.0 (4.5%)	49.0 / 1,053.0 (4.7%)
3	401.0 / 3,162.0 (12.7%)	214.0 / 1,053.0 (20.3%)
4	419.0 / 3,162.0 (13.3%)	138.0 / 1,053.0 (13.1%)
Unknown	23	11
alcohol			0.09	0.02, 0.17
1	13.0 / 3,002.0 (0.4%)	3.0 / 958.0 (0.3%)
2	1,794.0 / 3,002.0 (59.8%)	636.0 / 958.0 (66.4%)
3	707.0 / 3,002.0 (23.6%)	170.0 / 958.0 (17.7%)
4	488.0 / 3,002.0 (16.3%)	149.0 / 958.0 (15.6%)
Unknown	183	106
ha_tenido_algun_ingreso_reciente	249.0 / 3,185.0 (7.8%)	74.0 / 1,064.0 (7.0%)	0.86%	-0.99%, 2.7%	0.4
ninguna	1,852.0 / 3,185.0 (58.1%)	470.0 / 1,064.0 (44.2%)	14%	10%, 17%	<0.001
cardiopatia	61.0 / 3,185.0 (1.9%)	42.0 / 1,064.0 (3.9%)	-2.0%	-3.4%, -0.71%	<0.001
epoc	21.0 / 3,185.0 (0.7%)	15.0 / 1,064.0 (1.4%)	-0.75%	-1.6%, 0.07%	0.034
diabetes_mellitus	50.0 / 3,185.0 (1.6%)	29.0 / 1,064.0 (2.7%)	-1.2%	-2.3%, -0.02%	0.022
enfermedad_autoinmune	242.0 / 3,185.0 (7.6%)	86.0 / 1,064.0 (8.1%)	-0.48%	-2.4%, 1.5%	0.7
trasplante	8.0 / 3,185.0 (0.3%)	2.0 / 1,064.0 (0.2%)	0.06%	-0.31%, 0.44%	>0.9
ictus	5.0 / 3,185.0 (0.2%)	6.0 / 1,064.0 (0.6%)	-0.41%	-0.94%, 0.13%	0.056
problemas_de_higado	45.0 / 3,185.0 (1.4%)	40.0 / 1,064.0 (3.8%)	-2.3%	-3.6%, -1.1%	<0.001
dialisis_renal	2.0 / 3,185.0 (0.1%)	0.0 / 1,064.0 (0.0%)	0.06%	-0.09%, 0.21%	>0.9
1 Mean (SD) Median (IQR); n / N (%)
2 Welch Two Sample t-test; Standardized Mean Difference; Two sample test for equality of proportions
3 CI = Confidence Interval

4ª parte. Síntomas de la esfera sexual

Prevalencia de sintomaticas y no sintomaticas en general

#prevalencia de sintomaticas y no sintomátias en general
Menopausia %>%
  tbl_summary(
    include =no_tengo_sintomas,
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  )

Characteristic	N = 4,6951
no_tengo_sintomas	347.0 / 4,695.0 (7.4%)
1 n / N (%)

Prevalencia de los síntomas de la esfera sexual

#crea la variable sintomas_esf_sex si tengo_dolor_en_las_relaciones_sexuales o no_tengo_deseos_de_tener_relaciones_sexuales o no_tengo_orgasmos_ahora_pero_si_los_tenia_antes == 1
Menopausia <- Menopausia %>%
  mutate (sintomas_esf_sex = ifelse(sequedad_vaginal == 1 | tengo_dolor_en_las_relaciones_sexuales == 1 | no_tengo_deseos_de_tener_relaciones_sexuales == 1 | no_tengo_orgasmos_ahora_pero_si_los_tenia_antes == 1, 1, 0))

#prevalencia de los síntomas de la esfera sexual
Menopausia %>%
  tbl_summary(
    include = sintomas_esf_sex,
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  )

Characteristic	N = 4,6951
sintomas_esf_sex	3,426.0 / 4,695.0 (73.0%)
1 n / N (%)

Prevalencia de los síntomas de la esfera sexual entre las sintomáticas

#prevalencia de los síntomas de la esfera sexual entre las sintomáticas
Menopausia %>%
  filter(no_tengo_sintomas == 0) %>% 
  tbl_summary(
    include = sintomas_esf_sex,
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  )

Characteristic	N = 4,3481
sintomas_esf_sex	3,332.0 / 4,348.0 (76.6%)
1 n / N (%)

Prevalencia de los síntomas de la esfera sexual

#prevalencia de los síntomas de la esfera sexual
Menopausia %>%
  tbl_summary(
    include = c(sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, no_tengo_orgasmos_ahora_pero_si_los_tenia_antes),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  )

Characteristic	N = 4,6951
sequedad_vaginal	2,438.0 / 4,695.0 (51.9%)
tengo_dolor_en_las_relaciones_sexuales	1,316.0 / 4,695.0 (28.0%)
no_tengo_deseos_de_tener_relaciones_sexuales	2,527.0 / 4,695.0 (53.8%)
no_tengo_orgasmos_ahora_pero_si_los_tenia_antes	647.0 / 4,695.0 (13.8%)
1 n / N (%)

Sintomas en funcón del tiempo de menopausia

.

#Sintomas en función del tiempo de menopausia

Menopausia %>%
  tbl_summary(
    by = fue_hace_mas_de_1_ano,
    include = c(sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, no_tengo_orgasmos_ahora_pero_si_los_tenia_antes),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 2,4021	1, N = 2,2931	Difference2	95% CI2,3	p-value2
sequedad_vaginal	1,018.0 / 2,402.0 (42.4%)	1,420.0 / 2,293.0 (61.9%)	-20%	-22%, -17%	<0.001
tengo_dolor_en_las_relaciones_sexuales	480.0 / 2,402.0 (20.0%)	836.0 / 2,293.0 (36.5%)	-16%	-19%, -14%	<0.001
no_tengo_deseos_de_tener_relaciones_sexuales	1,168.0 / 2,402.0 (48.6%)	1,359.0 / 2,293.0 (59.3%)	-11%	-14%, -7.8%	<0.001
no_tengo_orgasmos_ahora_pero_si_los_tenia_antes	293.0 / 2,402.0 (12.2%)	354.0 / 2,293.0 (15.4%)	-3.2%	-5.3%, -1.2%	0.001
1 n / N (%)
2 Two sample test for equality of proportions
3 CI = Confidence Interval

Sintomas en función de si tienen pareja

#crea la nueva variable pareja2, desde la variable pareja 1 = No, 2 = Sí, 3 = Si.
#recodifica la variable sitaucion_laboral siendo 1, 2=1 y 3,4=2
Menopausia <- Menopausia %>%
  mutate(pareja2 = recode(pareja, "1" = "0", "2" = "1", "3" = "1"))


#Sintomas en función de si tienen pareja
Menopausia %>%
  tbl_summary(
    by = pareja2,
    include = c(sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, no_tengo_orgasmos_ahora_pero_si_los_tenia_antes),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 6121	1, N = 3,9971	Difference2	95% CI2,3	p-value2
sequedad_vaginal	262.0 / 612.0 (42.8%)	2,145.0 / 3,997.0 (53.7%)	-11%	-15%, -6.5%	<0.001
tengo_dolor_en_las_relaciones_sexuales	76.0 / 612.0 (12.4%)	1,227.0 / 3,997.0 (30.7%)	-18%	-21%, -15%	<0.001
no_tengo_deseos_de_tener_relaciones_sexuales	252.0 / 612.0 (41.2%)	2,241.0 / 3,997.0 (56.1%)	-15%	-19%, -11%	<0.001
no_tengo_orgasmos_ahora_pero_si_los_tenia_antes	70.0 / 612.0 (11.4%)	567.0 / 3,997.0 (14.2%)	-2.7%	-5.6%, 0.09%	0.077
1 n / N (%)
2 Two sample test for equality of proportions
3 CI = Confidence Interval

Sintomas en función de tratamiento para la menopausia

#Sintomas en función de tratamiento para la menopausia
Menopausia %>%
  tbl_summary(
    by = recibe_medicacion_para_el_sindrome_climaterico,
    include = c(sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, no_tengo_orgasmos_ahora_pero_si_los_tenia_antes),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,9721	1, N = 7231	Difference2	95% CI2,3	p-value2
sequedad_vaginal	2,023.0 / 3,972.0 (50.9%)	415.0 / 723.0 (57.4%)	-6.5%	-10%, -2.5%	0.002
tengo_dolor_en_las_relaciones_sexuales	1,065.0 / 3,972.0 (26.8%)	251.0 / 723.0 (34.7%)	-7.9%	-12%, -4.1%	<0.001
no_tengo_deseos_de_tener_relaciones_sexuales	2,104.0 / 3,972.0 (53.0%)	423.0 / 723.0 (58.5%)	-5.5%	-9.5%, -1.5%	0.007
no_tengo_orgasmos_ahora_pero_si_los_tenia_antes	528.0 / 3,972.0 (13.3%)	119.0 / 723.0 (16.5%)	-3.2%	-6.1%, -0.18%	0.027
1 n / N (%)
2 Two sample test for equality of proportions
3 CI = Confidence Interval

Sintomas en función de tabaco

#crea la nueva variable pareja2, desde la variable pareja 1 = No, 2 = Sí, 3 = Si.
#recodifica la variable sitaucion_laboral siendo 1 y 4=0, 2 y 3 =1
Menopausia <- Menopausia %>%
  mutate(tabaco2 = recode(tabaco, "1" = "0", "4" = "0", "2" = "1", "3" = "1"))


#Sintomas en función de si tienen pareja
Menopausia %>%
  tbl_summary(
    by = tabaco2,
    include = c(sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, no_tengo_orgasmos_ahora_pero_si_los_tenia_antes),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,7161	1, N = 9241	Difference2	95% CI2,3	p-value2
sequedad_vaginal	1,969.0 / 3,716.0 (53.0%)	447.0 / 924.0 (48.4%)	4.6%	0.94%, 8.3%	0.013
tengo_dolor_en_las_relaciones_sexuales	1,084.0 / 3,716.0 (29.2%)	223.0 / 924.0 (24.1%)	5.0%	1.8%, 8.2%	0.003
no_tengo_deseos_de_tener_relaciones_sexuales	2,005.0 / 3,716.0 (54.0%)	495.0 / 924.0 (53.6%)	0.38%	-3.3%, 4.0%	0.9
no_tengo_orgasmos_ahora_pero_si_los_tenia_antes	501.0 / 3,716.0 (13.5%)	135.0 / 924.0 (14.6%)	-1.1%	-3.7%, 1.5%	0.4
1 n / N (%)
2 Two sample test for equality of proportions
3 CI = Confidence Interval

Actividad sexual en función de tabaco

#acitividad sexual en función de tabaco
Menopausia %>%
  tbl_summary(
    by = tabaco2,
    include = actividad_sexual_coito_masturbacion_caricias,
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    bold_labels() %>%
  add_difference() %>%
  add_p()

Characteristic	0, N = 3,7161	1, N = 9241	Difference2	95% CI2,3	p-value4
actividad_sexual_coito_masturbacion_caricias			-0.03	-0.10, 0.04	0.083
1	316.0 / 3,612.0 (8.7%)	93.0 / 896.0 (10.4%)
2	2,063.0 / 3,612.0 (57.1%)	483.0 / 896.0 (53.9%)
3	922.0 / 3,612.0 (25.5%)	222.0 / 896.0 (24.8%)
4	283.0 / 3,612.0 (7.8%)	91.0 / 896.0 (10.2%)
5	28.0 / 3,612.0 (0.8%)	7.0 / 896.0 (0.8%)
Unknown	104	28
1 n / N (%)
2 Standardized Mean Difference
3 CI = Confidence Interval
4 Pearson’s Chi-squared test

Síntomas de la esfera sexual en función de actividad sexual

#Síntomas de la esfera sexual en función de actividad sexual
#Actividad sexual = 1
Menopausia %>%
  filter(actividad_sexual_coito_masturbacion_caricias == 1) %>%
  tbl_summary(
    include = c(sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, no_tengo_orgasmos_ahora_pero_si_los_tenia_antes),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    modify_caption("Actividad sexual = 1")

Actividad sexual = 1

Characteristic	N = 4131
sequedad_vaginal	209.0 / 413.0 (50.6%)
tengo_dolor_en_las_relaciones_sexuales	86.0 / 413.0 (20.8%)
no_tengo_deseos_de_tener_relaciones_sexuales	274.0 / 413.0 (66.3%)
no_tengo_orgasmos_ahora_pero_si_los_tenia_antes	92.0 / 413.0 (22.3%)
1 n / N (%)

#Actividad sexual = 2
Menopausia %>%
  filter(actividad_sexual_coito_masturbacion_caricias == 2) %>%
  tbl_summary(
    include = c(sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, no_tengo_orgasmos_ahora_pero_si_los_tenia_antes),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    modify_caption("Actividad sexual = 2")

Actividad sexual = 2

Characteristic	N = 2,5541
sequedad_vaginal	1,277.0 / 2,554.0 (50.0%)
tengo_dolor_en_las_relaciones_sexuales	680.0 / 2,554.0 (26.6%)
no_tengo_deseos_de_tener_relaciones_sexuales	1,156.0 / 2,554.0 (45.3%)
no_tengo_orgasmos_ahora_pero_si_los_tenia_antes	268.0 / 2,554.0 (10.5%)
1 n / N (%)

#Actividad sexual = 3
Menopausia %>%
  filter(actividad_sexual_coito_masturbacion_caricias == 3) %>%
  tbl_summary(
    include = c(sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, no_tengo_orgasmos_ahora_pero_si_los_tenia_antes),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    modify_caption("Actividad sexual = 3")

Actividad sexual = 3

Characteristic	N = 1,1521
sequedad_vaginal	657.0 / 1,152.0 (57.0%)
tengo_dolor_en_las_relaciones_sexuales	419.0 / 1,152.0 (36.4%)
no_tengo_deseos_de_tener_relaciones_sexuales	790.0 / 1,152.0 (68.6%)
no_tengo_orgasmos_ahora_pero_si_los_tenia_antes	208.0 / 1,152.0 (18.1%)
1 n / N (%)

#Actividad sexual = 4
Menopausia %>%
  filter(actividad_sexual_coito_masturbacion_caricias == 4) %>%
  tbl_summary(
    include = c(sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, no_tengo_orgasmos_ahora_pero_si_los_tenia_antes),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    modify_caption("Actividad sexual = 4")

Actividad sexual = 4

Characteristic	N = 3761
sequedad_vaginal	196.0 / 376.0 (52.1%)
tengo_dolor_en_las_relaciones_sexuales	79.0 / 376.0 (21.0%)
no_tengo_deseos_de_tener_relaciones_sexuales	197.0 / 376.0 (52.4%)
no_tengo_orgasmos_ahora_pero_si_los_tenia_antes	33.0 / 376.0 (8.8%)
1 n / N (%)

#Actividad sexual = 5
Menopausia %>%
  filter(actividad_sexual_coito_masturbacion_caricias == 5) %>%
  tbl_summary(
    include = c(sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, no_tengo_orgasmos_ahora_pero_si_los_tenia_antes),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
    modify_caption("Actividad sexual = 5")

Actividad sexual = 5

Characteristic	N = 351
sequedad_vaginal	23.0 / 35.0 (65.7%)
tengo_dolor_en_las_relaciones_sexuales	16.0 / 35.0 (45.7%)
no_tengo_deseos_de_tener_relaciones_sexuales	28.0 / 35.0 (80.0%)
no_tengo_orgasmos_ahora_pero_si_los_tenia_antes	15.0 / 35.0 (42.9%)
1 n / N (%)

sintomas de las que tienen sintomas y usan tratamiento

#sintomas de las que tienen sintomas y usan tratamiento

Menopausia %>%
  filter(no_tengo_sintomas == 0) %>%
  tbl_summary(
    by = recibe_medicacion_para_el_sindrome_climaterico,
    include = c(sequedad_vaginal,tengo_dolor_en_las_relaciones_sexuales, no_tengo_deseos_de_tener_relaciones_sexuales, no_tengo_orgasmos_ahora_pero_si_los_tenia_antes),
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  ) %>%
  bold_labels() %>%
  add_difference()

Characteristic	0, N = 3,6651	1, N = 6831	Difference2	95% CI2,3	p-value2
sequedad_vaginal	1,975.0 / 3,665.0 (53.9%)	405.0 / 683.0 (59.3%)	-5.4%	-9.5%, -1.3%	0.010
tengo_dolor_en_las_relaciones_sexuales	1,047.0 / 3,665.0 (28.6%)	244.0 / 683.0 (35.7%)	-7.2%	-11%, -3.2%	<0.001
no_tengo_deseos_de_tener_relaciones_sexuales	2,052.0 / 3,665.0 (56.0%)	415.0 / 683.0 (60.8%)	-4.8%	-8.9%, -0.69%	0.023
no_tengo_orgasmos_ahora_pero_si_los_tenia_antes	514.0 / 3,665.0 (14.0%)	114.0 / 683.0 (16.7%)	-2.7%	-5.8%, 0.43%	0.078
1 n / N (%)
2 Two sample test for equality of proportions
3 CI = Confidence Interval

puntuación dominio sexualidad de las que tienen sintomas (quitando las que “no tienen pareja”

#puntuación dominio sexualidad de las que tienen sintomas

Menopausia %>%
  filter(no_tengo_sintomas == 0) %>%
  filter(pareja !=1) %>%
  tbl_summary(
    include = puntos_dominio_sexualidad,
    statistic = list(
      all_continuous() ~ "{mean} ({sd}) {median} ({p25}, {p75})",
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
    digits = list(
      all_continuous() ~ 2,
      all_categorical() ~ 1
    )
  )

Characteristic	N = 3,7251
puntos_dominio_sexualidad	46.53 (24.45) 50.00 (30.00, 60.00)
1 Mean (SD) Median (IQR)

Comparación puntos dominio sexual entre sintomáticas y asintomáticas

Menopausia %>%
  filter(pareja != 1) %>%
  select(puntos_dominio_sexualidad, no_tengo_sintomas) %>%
  t.test(puntos_dominio_sexualidad ~ no_tengo_sintomas, data = .)

## 
##  Welch Two Sample t-test
## 
## data:  puntos_dominio_sexualidad by no_tengo_sintomas
## t = 5.0199, df = 308.31, p-value = 8.759e-07
## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
## 95 percent confidence interval:
##   4.886187 11.186237
## sample estimates:
## mean in group 0 mean in group 1 
##        46.52886        38.49265

Comparación con el estudio de referencia

Grupo de edad 40-44 años

library(BSDA)

tsum.test(mean.x=37.33,   s.x=18.88, n.x=41,
          mean.y=26.5, s.y=16.4, n.y=105)

## 
##  Welch Modified Two-Sample t-Test
## 
## data:  Summarized x and y
## t = 3.2281, df = 64.877, p-value = 0.001956
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   4.129507 17.530493
## sample estimates:
## mean of x mean of y 
##     37.33     26.50

Grupo de edad 45-47 años

tsum.test(mean.x=35.97,   s.x=13.68, n.x=19,
          mean.y=30.2, s.y=16.9, n.y=172)

## 
##  Welch Modified Two-Sample t-Test
## 
## data:  Summarized x and y
## t = 1.7007, df = 24.507, p-value = 0.1017
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1.224436 12.764436
## sample estimates:
## mean of x mean of y 
##     35.97     30.20

Grupo de edad 48-50 años

tsum.test(mean.x=37.4,   s.x=21.22, n.x=34,
          mean.y=34.3, s.y=18, n.y=390)

## 
##  Welch Modified Two-Sample t-Test
## 
## data:  Summarized x and y
## t = 0.82631, df = 37.258, p-value = 0.4139
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.499702 10.699702
## sample estimates:
## mean of x mean of y 
##      37.4      34.3

Grupo de edad 51-53 años

tsum.test(mean.x=37.46,   s.x=20.94, n.x=37,
          mean.y=36, s.y=17.8, n.y=574)

## 
##  Welch Modified Two-Sample t-Test
## 
## data:  Summarized x and y
## t = 0.41456, df = 39.426, p-value = 0.6807
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -5.660999  8.580999
## sample estimates:
## mean of x mean of y 
##     37.46     36.00

Grupo de edad 54-56 años

tsum.test(mean.x=36.39,   s.x=16.07, n.x=37,
          mean.y=37.2, s.y=16.4, n.y=791)

## 
##  Welch Modified Two-Sample t-Test
## 
## data:  Summarized x and y
## t = -0.29939, df = 39.589, p-value = 0.7662
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -6.279747  4.659747
## sample estimates:
## mean of x mean of y 
##     36.39     37.20

Grupo de edad 57-59 años

tsum.test(mean.x=38.46,   s.x=21.98, n.x=19,
          mean.y=38.3, s.y=16.3, n.y=892)

## 
##  Welch Modified Two-Sample t-Test
## 
## data:  Summarized x and y
## t = 0.031546, df = 18.424, p-value = 0.9752
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -10.47833  10.79833
## sample estimates:
## mean of x mean of y 
##     38.46     38.30

Grupo de edad 60-62 años

tsum.test(mean.x=37.44,   s.x=21.2, n.x=12,
          mean.y=39.6, s.y=15.8 , n.y=815)

## 
##  Welch Modified Two-Sample t-Test
## 
## data:  Summarized x and y
## t = -0.35151, df = 11.181, p-value = 0.7317
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -15.65819  11.33819
## sample estimates:
## mean of x mean of y 
##     37.44     39.60

Grupo de edad 63-65 años

tsum.test(mean.x=18.01,   s.x=18.25, n.x=8,
          mean.y=40.3, s.y=15.8, n.y=560)

## 
##  Welch Modified Two-Sample t-Test
## 
## data:  Summarized x and y
## t = -3.4362, df = 7.1507, p-value = 0.01054
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -37.563561  -7.016439
## sample estimates:
## mean of x mean of y 
##     18.01     40.30

Grupo de edad 66-68 años

tsum.test(mean.x=32.3,   s.x=0.98, n.x=2,
          mean.y=44.7, s.y=15.6 , n.y=307)

## 
##  Welch Modified Two-Sample t-Test
## 
## data:  Summarized x and y
## t = -10.991, df = 6.9646, p-value = 1.187e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -15.070592  -9.729408
## sample estimates:
## mean of x mean of y 
##      32.3      44.7

Grupo de edad 69-71 años

tsum.test(mean.x=59.32,   s.x=0.0001, n.x=1,
          mean.y=44-7, s.y=14.8, n.y=307)

## 
##  Welch Modified Two-Sample t-Test
## 
## data:  Summarized x and y
## t = 26.424, df = 0, p-value = NA
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  NaN NaN
## sample estimates:
## mean of x mean of y 
##     59.32     37.00

Grupo de edad 72-75 años

tsum.test(mean.x=38.56,   s.x=7.71, n.x=9,
          mean.y=46.1, s.y=13.9, n.y=224)

## 
##  Welch Modified Two-Sample t-Test
## 
## data:  Summarized x and y
## t = -2.7592, df = 10.22, p-value = 0.01978
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -13.61106  -1.46894
## sample estimates:
## mean of x mean of y 
##     38.56     46.10