par=c("edad_imc","edad_hdl", "edad_ldl","edad_sexo", "edad_hta","edad_dm", "edad_fuma","edad_soc",
"imc_hdl", "imc_ldl", "imc_sexo", "imc_hta", "imc_dm", "imc_fuma","imc_soc",
"hdl_ldl", "hdl_sexo", "hdl_hta", "hdl_dm", "hdl_fuma","hdl_soc",
"ldl_sexo", "ldl_hta", "ldl_dm", "ldl_fuma","ldl_soc",
"sexo_hta", "sexo_dm", "sexo_fuma","sexo_soc",
"hta_dm", "hta_fuma","hta_soc",
"dm_fuma","dm_soc",
"fuma_soc")
vectorCorrelaciones=c(
0.40, -0.40, 0.40, 0, 0.44, 0.35, 0.25, -0.25,
-0.45, 0.45, 0.15, 0.55, 0.40, -0.05, -0.40,
-0.55, 0.05, -0.55, -0.45, -0.1, 0.40,
-0.05, 0.55, 0.45, 0.1, -0.4,
0.1, 0.1, 0.3, 0,
0.1, 0.20, -0.4,
0.20, -0.4,
-0.20
) %>% set_names(par)
ordenLLenado=c("edad_imc",
"edad_hdl", "imc_hdl",
"edad_ldl", "imc_ldl", "hdl_ldl",
"edad_sexo","imc_sexo","hdl_sexo","ldl_sexo",
"edad_hta", "imc_hta", "hdl_hta", "ldl_hta", "sexo_hta",
"edad_dm", "imc_dm", "hdl_dm", "ldl_dm", "sexo_dm", "hta_dm",
"edad_fuma","imc_fuma","hdl_fuma","ldl_fuma","sexo_fuma","hta_fuma","dm_fuma",
"edad_soc", "imc_soc", "hdl_soc", "ldl_soc", "sexo_soc", "hta_soc", "dm_soc", "fuma_soc")
matrizCorrelaciones<-vector_a_matriz_simetrica(vectorCorrelaciones[ordenLLenado],9)
eigen(matrizCorrelaciones)$values %>% sort()
## [1] 0.1949300 0.3516115 0.4500000 0.5834265 0.7304160 0.8704806 0.9335323
## [8] 1.3069318 3.5786712
datosStd <- mvrnorm(n = 20000, mu=rep(0,9),Sigma = matrizCorrelaciones) %>% as_tibble() %>%
set_names(c("edad","imc","hdl","ldl","sexo","hta","dm","fuma","soc"))
parametros=list(
edad=list(
media=50,
desviacion=18,
p=0.5
),
imc=list(
media=26,
desviacion=3.5,
p=0.5
),
hdl=list(
media=44,
desviacion=10,
p=0.5
),
ldl=list(
media=110,
desviacion=30,
p=0.5
),
sexo=list(
p=0.5
),
hta=list(
p=0.21
),
dm=list(
p=0.08
),
fuma=list(
p=0.22
),
soc=list(
p=c(0.42,0.30)
)
)
#escala(datosStd$edad,parametros$edad)
#escala(datosStd$sexo,parametros$sexo)
#escala(datosStd$soc,parametros$soc)
datos=datosStd %>%
mutate(
edad=escala(edad,parametros$edad) %>% round(2),
imc=escala(imc,parametros$imc) %>% round(3),
hdl=escala(hdl,parametros$hdl) %>% round(1),
ldl=escala(ldl,parametros$ldl) %>% round(1),
sexo=escala(sexo,parametros$sexo) %>% factor(levels=c(0,1),labels=c("Mujer","Hombre")),
hta=escala(hta,parametros$hta)%>% factor(levels=c(0,1),labels=c("No","Sí")),
dm=escala(dm,parametros$dm)%>% factor(levels=c(0,1),labels=c("No","Sí")),
fuma=escala(fuma,parametros$fuma)%>% factor(levels=c(0,1),labels=c("No","Sí")),
soc=escala(soc,parametros$soc)%>% factor(levels=c(0,1,2),labels=c("Baja","Media","Alta"))
)%>% filter(edad>=18 & edad<=80,
hdl>=24 & hdl<=70,
ldl>=38 & ldl<=220,
imc>=16 & imc<=40)
datos %>% head()
## # A tibble: 6 × 9
## edad imc hdl ldl sexo hta dm fuma soc
## <dbl> <dbl> <dbl> <dbl> <fct> <fct> <fct> <fct> <fct>
## 1 70.0 27.7 40 146. Hombre No No No Baja
## 2 55.2 27.1 43 124. Hombre No No No Media
## 3 35.9 28.1 47.1 75.9 Mujer No No No Media
## 4 55.5 21.5 33.8 147. Mujer No No No Media
## 5 64.0 29.2 43.6 112. Hombre No Sí No Baja
## 6 35.5 28.5 46.3 114. Mujer No No No Media
datos %>% tbl_summary(
# include=c(edad,imc,hdl,ldl),
by = sexo,
missing = "no",
statistic = list(all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)")
) |>
add_n() |>
add_p(test = list(all_continuous() ~ "t.test",
all_categorical() ~ "chisq.test")) |>
modify_header(label = "**Variable**") |>
bold_labels()
| Variable |
N |
Mujer
N = 8,832 |
Hombre
N = 8,918 |
p-value |
| edad |
17,750 |
50 (15) |
50 (15) |
>0.9 |
| imc |
17,750 |
25.6 (3.3) |
26.3 (3.3) |
<0.001 |
| hdl |
17,750 |
44 (9) |
45 (9) |
<0.001 |
| ldl |
17,750 |
111 (28) |
108 (28) |
<0.001 |
| hta |
17,750 |
|
|
<0.001 |
| No |
|
7,320 (83%) |
6,982 (78%) |
|
| Sí |
|
1,512 (17%) |
1,936 (22%) |
|
| dm |
17,750 |
|
|
<0.001 |
| No |
|
8,303 (94%) |
8,197 (92%) |
|
| Sí |
|
529 (6.0%) |
721 (8.1%) |
|
| fuma |
17,750 |
|
|
<0.001 |
| No |
|
7,565 (86%) |
6,327 (71%) |
|
| Sí |
|
1,267 (14%) |
2,591 (29%) |
|
| soc |
17,750 |
|
|
0.7 |
| Baja |
|
2,425 (27%) |
2,402 (27%) |
|
| Media |
|
3,759 (43%) |
3,829 (43%) |
|
| Alta |
|
2,648 (30%) |
2,687 (30%) |
|
hdl
datos %>% ggplot(aes(x=hdl))+geom_histogram(bins=20)+
theme_minimal()
datos %>% finalfit(dependent = "hdl", explanatory=c("edad","imc", "sexo", "hta", "fuma","soc"),metrics = TRUE)%>% .[[1]] %>% knitr::kable()
| 1 |
edad |
[18.0,80.0] |
Mean (sd) |
44.4 (9.0) |
-0.19 (-0.20 to -0.18, p<0.001) |
-0.10 (-0.11 to -0.09, p<0.001) |
| 6 |
imc |
[16.1,38.2] |
Mean (sd) |
44.4 (9.0) |
-1.06 (-1.09 to -1.02, p<0.001) |
-0.61 (-0.65 to -0.57, p<0.001) |
| 8 |
sexo |
Mujer |
Mean (sd) |
44.0 (9.0) |
- |
- |
| 7 |
|
Hombre |
Mean (sd) |
44.8 (9.0) |
0.75 (0.48 to 1.01, p<0.001) |
1.42 (1.18 to 1.65, p<0.001) |
| 4 |
hta |
No |
Mean (sd) |
45.9 (8.7) |
- |
- |
| 5 |
|
Sí |
Mean (sd) |
38.3 (7.7) |
-7.56 (-7.87 to -7.24, p<0.001) |
-4.06 (-4.38 to -3.75, p<0.001) |
| 2 |
fuma |
No |
Mean (sd) |
44.5 (9.0) |
- |
- |
| 3 |
|
Sí |
Mean (sd) |
43.9 (9.1) |
-0.61 (-0.94 to -0.29, p<0.001) |
-0.15 (-0.44 to 0.15, p=0.327) |
| 10 |
soc |
Baja |
Mean (sd) |
40.4 (8.2) |
- |
- |
| 11 |
|
Media |
Mean (sd) |
44.3 (8.6) |
3.97 (3.66 to 4.28, p<0.001) |
2.07 (1.78 to 2.36, p<0.001) |
| 9 |
|
Alta |
Mean (sd) |
48.2 (8.7) |
7.80 (7.47 to 8.14, p<0.001) |
4.33 (4.00 to 4.65, p<0.001) |
ldl
datos %>% ggplot(aes(x=ldl))+geom_histogram(bins=20)+
theme_minimal()
datos %>% finalfit(dependent = "ldl", explanatory=c("edad","imc", "sexo","hta","fuma","soc"),metrics = TRUE)%>% .[[1]] %>% knitr::kable()
| 1 |
edad |
[18.0,80.0] |
Mean (sd) |
109.5 (27.9) |
0.59 (0.56 to 0.62, p<0.001) |
0.29 (0.26 to 0.32, p<0.001) |
| 6 |
imc |
[16.1,38.2] |
Mean (sd) |
109.5 (27.9) |
3.32 (3.21 to 3.43, p<0.001) |
1.90 (1.77 to 2.02, p<0.001) |
| 8 |
sexo |
Mujer |
Mean (sd) |
111.0 (27.8) |
- |
- |
| 7 |
|
Hombre |
Mean (sd) |
108.1 (27.9) |
-2.86 (-3.68 to -2.04, p<0.001) |
-5.09 (-5.81 to -4.37, p<0.001) |
| 4 |
hta |
No |
Mean (sd) |
104.6 (26.2) |
- |
- |
| 5 |
|
Sí |
Mean (sd) |
130.0 (25.0) |
25.36 (24.39 to 26.33, p<0.001) |
14.76 (13.80 to 15.72, p<0.001) |
| 2 |
fuma |
No |
Mean (sd) |
109.0 (27.8) |
- |
- |
| 3 |
|
Sí |
Mean (sd) |
111.2 (28.0) |
2.16 (1.16 to 3.15, p<0.001) |
0.79 (-0.10 to 1.67, p=0.082) |
| 10 |
soc |
Baja |
Mean (sd) |
122.1 (26.6) |
- |
- |
| 11 |
|
Media |
Mean (sd) |
109.8 (26.3) |
-12.38 (-13.33 to -11.43, p<0.001) |
-6.19 (-7.07 to -5.31, p<0.001) |
| 9 |
|
Alta |
Mean (sd) |
97.7 (26.1) |
-24.40 (-25.42 to -23.37, p<0.001) |
-13.17 (-14.17 to -12.17, p<0.001) |
imc
datos %>% ggplot(aes(x=imc))+geom_histogram(bins=20)+
theme_minimal()
datos %>% finalfit(dependent = "imc", explanatory=c("edad", "hdl", "ldl", "sexo","hta","dm","fuma","soc"),metrics = TRUE) %>% .[[1]] %>% knitr::kable()
| 3 |
edad |
[18.0,80.0] |
Mean (sd) |
26.0 (3.3) |
0.07 (0.07 to 0.08, p<0.001) |
0.04 (0.04 to 0.04, p<0.001) |
| 6 |
hdl |
[24.0,70.0] |
Mean (sd) |
26.0 (3.3) |
-0.14 (-0.15 to -0.14, p<0.001) |
-0.05 (-0.06 to -0.05, p<0.001) |
| 9 |
ldl |
[38.0,212.2] |
Mean (sd) |
26.0 (3.3) |
0.05 (0.05 to 0.05, p<0.001) |
0.02 (0.02 to 0.02, p<0.001) |
| 11 |
sexo |
Mujer |
Mean (sd) |
25.6 (3.3) |
- |
- |
| 10 |
|
Hombre |
Mean (sd) |
26.3 (3.3) |
0.78 (0.68 to 0.87, p<0.001) |
0.99 (0.91 to 1.07, p<0.001) |
| 7 |
hta |
No |
Mean (sd) |
25.4 (3.2) |
- |
- |
| 8 |
|
Sí |
Mean (sd) |
28.3 (3.0) |
2.92 (2.80 to 3.03, p<0.001) |
1.40 (1.29 to 1.51, p<0.001) |
| 1 |
dm |
No |
Mean (sd) |
25.8 (3.3) |
- |
- |
| 2 |
|
Sí |
Mean (sd) |
28.1 (3.2) |
2.28 (2.09 to 2.47, p<0.001) |
0.84 (0.67 to 1.00, p<0.001) |
| 4 |
fuma |
No |
Mean (sd) |
26.1 (3.3) |
- |
- |
| 5 |
|
Sí |
Mean (sd) |
25.5 (3.3) |
-0.61 (-0.73 to -0.49, p<0.001) |
-1.35 (-1.45 to -1.25, p<0.001) |
| 13 |
soc |
Baja |
Mean (sd) |
27.5 (3.1) |
- |
- |
| 14 |
|
Media |
Mean (sd) |
26.0 (3.2) |
-1.51 (-1.63 to -1.40, p<0.001) |
-0.75 (-0.86 to -0.65, p<0.001) |
| 12 |
|
Alta |
Mean (sd) |
24.5 (3.1) |
-2.98 (-3.10 to -2.85, p<0.001) |
-1.57 (-1.69 to -1.46, p<0.001) |
hta
datos %>% finalfit(dependent = "hta", explanatory=c("edad", "imc", "hdl", "ldl", "sexo","fuma","soc"),metrics = TRUE)%>% .[[1]] %>% knitr::kable()
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
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## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Setting levels: control = 0, case = 1
## Setting direction: controls < cases
| 1 |
edad |
Mean (SD) |
48.0 (14.3) |
56.8 (13.3) |
1.05 (1.04-1.05, p<0.001) |
1.02 (1.01-1.02, p<0.001) |
| 5 |
imc |
Mean (SD) |
25.4 (3.2) |
28.3 (3.0) |
1.35 (1.33-1.37, p<0.001) |
1.20 (1.18-1.22, p<0.001) |
| 4 |
hdl |
Mean (SD) |
45.9 (8.7) |
38.3 (7.7) |
0.90 (0.89-0.90, p<0.001) |
0.95 (0.94-0.95, p<0.001) |
| 6 |
ldl |
Mean (SD) |
104.6 (26.2) |
130.0 (25.0) |
1.04 (1.04-1.04, p<0.001) |
1.02 (1.02-1.02, p<0.001) |
| 8 |
sexo |
Mujer |
7320 (82.9) |
1512 (17.1) |
- |
- |
| 7 |
|
Hombre |
6982 (78.3) |
1936 (21.7) |
1.34 (1.25-1.45, p<0.001) |
1.31 (1.20-1.43, p<0.001) |
| 2 |
fuma |
No |
11419 (82.2) |
2473 (17.8) |
- |
- |
| 3 |
|
Sí |
2883 (74.7) |
975 (25.3) |
1.56 (1.43-1.70, p<0.001) |
1.61 (1.45-1.78, p<0.001) |
| 10 |
soc |
Baja |
3226 (66.8) |
1601 (33.2) |
- |
- |
| 11 |
|
Media |
6239 (82.2) |
1349 (17.8) |
0.44 (0.40-0.47, p<0.001) |
0.79 (0.72-0.87, p<0.001) |
| 9 |
|
Alta |
4837 (90.7) |
498 (9.3) |
0.21 (0.19-0.23, p<0.001) |
0.72 (0.64-0.82, p<0.001) |
dm
datos %>% finalfit(dependent = "dm", explanatory=c("edad", "imc", "hdl", "ldl", "sexo","fuma","soc"),metrics = TRUE)%>% .[[1]] %>% knitr::kable()
## Waiting for profiling to be done...
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## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Setting levels: control = 0, case = 1
## Setting direction: controls < cases
| 1 |
edad |
Mean (SD) |
49.2 (14.5) |
56.8 (13.3) |
1.04 (1.03-1.04, p<0.001) |
1.01 (1.01-1.02, p<0.001) |
| 5 |
imc |
Mean (SD) |
25.8 (3.3) |
28.1 (3.2) |
1.23 (1.21-1.26, p<0.001) |
1.07 (1.05-1.10, p<0.001) |
| 4 |
hdl |
Mean (SD) |
44.9 (8.9) |
37.9 (7.6) |
0.91 (0.90-0.92, p<0.001) |
0.95 (0.95-0.96, p<0.001) |
| 6 |
ldl |
Mean (SD) |
107.9 (27.3) |
130.6 (26.4) |
1.03 (1.03-1.03, p<0.001) |
1.02 (1.01-1.02, p<0.001) |
| 8 |
sexo |
Mujer |
8303 (94.0) |
529 (6.0) |
- |
- |
| 7 |
|
Hombre |
8197 (91.9) |
721 (8.1) |
1.38 (1.23-1.55, p<0.001) |
1.40 (1.23-1.59, p<0.001) |
| 2 |
fuma |
No |
13021 (93.7) |
871 (6.3) |
- |
- |
| 3 |
|
Sí |
3479 (90.2) |
379 (9.8) |
1.63 (1.43-1.85, p<0.001) |
1.35 (1.17-1.55, p<0.001) |
| 10 |
soc |
Baja |
4134 (85.6) |
693 (14.4) |
- |
- |
| 11 |
|
Media |
7147 (94.2) |
441 (5.8) |
0.37 (0.32-0.42, p<0.001) |
0.58 (0.51-0.66, p<0.001) |
| 9 |
|
Alta |
5219 (97.8) |
116 (2.2) |
0.13 (0.11-0.16, p<0.001) |
0.34 (0.27-0.42, p<0.001) |
fuma
datos %>% finalfit(dependent = "fuma", explanatory=c("edad", "imc", "sexo","soc"),metrics = TRUE) %>% .[[1]] %>% knitr::kable()
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Setting levels: control = 0, case = 1
## Setting direction: controls < cases
| 1 |
edad |
Mean (SD) |
48.8 (14.5) |
53.0 (14.3) |
1.02 (1.02-1.02, p<0.001) |
1.03 (1.03-1.03, p<0.001) |
| 2 |
imc |
Mean (SD) |
26.1 (3.3) |
25.5 (3.3) |
0.95 (0.94-0.96, p<0.001) |
0.85 (0.84-0.86, p<0.001) |
| 4 |
sexo |
Mujer |
7565 (85.7) |
1267 (14.3) |
- |
- |
| 3 |
|
Hombre |
6327 (70.9) |
2591 (29.1) |
2.45 (2.27-2.64, p<0.001) |
2.95 (2.72-3.19, p<0.001) |
| 6 |
soc |
Baja |
3464 (71.8) |
1363 (28.2) |
- |
- |
| 7 |
|
Media |
5969 (78.7) |
1619 (21.3) |
0.69 (0.63-0.75, p<0.001) |
0.57 (0.52-0.62, p<0.001) |
| 5 |
|
Alta |
4459 (83.6) |
876 (16.4) |
0.50 (0.45-0.55, p<0.001) |
0.35 (0.31-0.39, p<0.001) |
datos %>%
ggplot(aes(x=edad))+geom_histogram(bins=20)+
theme_minimal()
haven::write_sav(datos, "datos.sav")