Simulation for development Machine Learning algorithm
# Gestational age simulation with uniform distribution betwwen entre 24 et 25 weeks
set.seed(2345)
gen_terme_24 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 24, max = 25)) }
terme_24 <- 1:1 %>% map_df(gen_terme_24)
# Birth weigh simulation with truncated normal distribution (gestational age between 24 and 25 weeks)
set.seed(2345)
gen_poids_24 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=450, b=845, mean = 661, sd = 95)) }
poids_24 <- 1:1 %>% map_df(gen_poids_24)
# creatinine simulation with truncated normal distribution (gestational age between 24 and 25 weeks)
set.seed(2345)
gen_creat_24 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_24 <- 1:1 %>% map_df(gen_creat_24)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 24 and 25 weeks)
set.seed(2345)
gen_infection_24 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_24 <- 1:1 %>% map_df(gen_infection_24)
# weight gain simulation with truncated normal distribution (gestational age between 24 and 25 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf24 <- poids_24 %>% bind_cols(creat_24) %>% bind_cols(infection_24)%>%bind_cols(terme_24)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 24)
summary(data_demog_inf24)
## PN CREA infection GA
## Min. :496.1 Min. : 40.03 Min. : 3.031 Min. :24.01
## 1st Qu.:608.6 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:24.23
## Median :655.8 Median : 70.70 Median :27.495 Median :24.50
## Mean :659.3 Mean : 77.25 Mean :33.466 Mean :24.47
## 3rd Qu.:709.2 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:24.68
## Max. :840.2 Max. :144.55 Max. :89.937 Max. :24.98
## ID weeks
## Min. : 1.00 Min. :24
## 1st Qu.: 25.75 1st Qu.:24
## Median : 50.50 Median :24
## Mean : 50.50 Mean :24
## 3rd Qu.: 75.25 3rd Qu.:24
## Max. :100.00 Max. :24
data_demog_inf24<- data_demog_inf24%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf24_inf15 <- data_demog_inf24%>% filter(periode == "inf15")
data_demog_inf24_sup15 <- data_demog_inf24%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_24_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 3.8, sd = 6.6)) }
gain_24_inf15 <- 1:1 %>% map_df(gen_gain_24_inf15) %>% mutate(ID6 =(1:20))%>% dplyr:: select(-ID5)
gain_24_inf15 <- as_tibble(gain_24_inf15)
gain_24_inf15_bis <- gain_24_inf15 %>% bind_cols(data_demog_inf24_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_24_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 13.3, sd = 1.7)) }
gain_24_sup15 <- 1:1 %>% map_df(gen_gain_24_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_24_sup15 <- as_tibble(gain_24_sup15)
gain_24_sup15_bis <- gain_24_sup15 %>% bind_cols(data_demog_inf24_sup15)%>% dplyr::select(-ID6)
data_demog_inf24_final <-gain_24_inf15_bis%>% bind_rows(gain_24_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf24_final <- data_demog_inf24_final %>% mutate (PMA = GA+infection/7)%>% mutate(CW= PN+gain*infection)
summary(data_demog_inf24_final)
## gain PN CREA infection
## Min. : 0.215 Min. :496.1 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.343 1st Qu.:608.6 1st Qu.: 57.46 1st Qu.:17.458
## Median :12.865 Median :655.8 Median : 70.70 Median :27.495
## Mean :11.908 Mean :659.3 Mean : 77.25 Mean :33.466
## 3rd Qu.:13.792 3rd Qu.:709.2 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :17.365 Max. :840.2 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :24.01 Min. : 1.00 Min. :24 inf15:20 Min. :24.45
## 1st Qu.:24.23 1st Qu.: 25.75 1st Qu.:24 sup15:80 1st Qu.:26.79
## Median :24.50 Median : 50.50 Median :24 Median :28.43
## Mean :24.47 Mean : 50.50 Mean :24 Mean :29.25
## 3rd Qu.:24.68 3rd Qu.: 75.25 3rd Qu.:24 3rd Qu.:31.35
## Max. :24.98 Max. :100.00 Max. :24 Max. :37.64
## CW
## Min. : 583.9
## 1st Qu.: 867.6
## Median :1060.4
## Mean :1088.8
## 3rd Qu.:1288.8
## Max. :1929.4
Gestational age between 25 and 26 weeks
#Gestational age simulation with uniform distribution betwwen entre 25 et 26 weeks
set.seed(2345)
gen_terme_25 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 25, max = 26)) }
terme_25 <- 1:1 %>% map_df(gen_terme_25)
# Birth weigh simulation with truncated normal distribution (gestational age between 25 and 26 weeks)
set.seed(2345)
gen_poids_25 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=576, b=845, mean = 718, sd = 111)) }
poids_25 <- 1:1 %>% map_df(gen_poids_25)
# creatinine simulation with truncated normal distribution (gestational age between 25 and 26 weeks)
set.seed(2345)
gen_creat_25 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_25 <- 1:1 %>% map_df(gen_creat_25)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 25 and 26 weeks)
set.seed(2345)
gen_infection_25 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_25 <- 1:1 %>% map_df(gen_infection_25)
# weight gain simulation with truncated normal distribution (gestational age between 25 and 26 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf25 <- poids_25 %>% bind_cols(creat_25) %>% bind_cols(infection_25)%>%bind_cols(terme_25)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 25)
summary(data_demog_inf25)
## PN CREA infection GA
## Min. :587.1 Min. : 40.03 Min. : 3.031 Min. :25.01
## 1st Qu.:657.3 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:25.23
## Median :708.8 Median : 70.70 Median :27.495 Median :25.50
## Mean :708.2 Mean : 77.25 Mean :33.466 Mean :25.47
## 3rd Qu.:753.0 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:25.68
## Max. :840.4 Max. :144.55 Max. :89.937 Max. :25.98
## ID weeks
## Min. : 1.00 Min. :25
## 1st Qu.: 25.75 1st Qu.:25
## Median : 50.50 Median :25
## Mean : 50.50 Mean :25
## 3rd Qu.: 75.25 3rd Qu.:25
## Max. :100.00 Max. :25
data_demog_inf25<- data_demog_inf25%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf25_inf15 <- data_demog_inf25%>% filter(periode == "inf15")
data_demog_inf25_sup15 <- data_demog_inf25%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_25_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 3.8, sd = 6.6)) }
gain_25_inf15 <- 1:1 %>% map_df(gen_gain_25_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_25_inf15 <- as_tibble(gain_25_inf15)
gain_25_inf15_bis <- gain_25_inf15 %>% bind_cols(data_demog_inf25_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_25_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 13.3, sd = 1.7)) }
gain_25_sup15 <- 1:1 %>% map_df(gen_gain_25_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_25_sup15 <- as_tibble(gain_25_sup15)
gain_25_sup15_bis <- gain_25_sup15 %>% bind_cols(data_demog_inf25_sup15)%>% dplyr::select(-ID6)
data_demog_inf25_final <-gain_25_inf15_bis%>% bind_rows(gain_25_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf25_final <- data_demog_inf25_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf25_final)
## gain PN CREA infection
## Min. : 0.215 Min. :587.1 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.343 1st Qu.:657.3 1st Qu.: 57.46 1st Qu.:17.458
## Median :12.865 Median :708.8 Median : 70.70 Median :27.495
## Mean :11.908 Mean :708.2 Mean : 77.25 Mean :33.466
## 3rd Qu.:13.792 3rd Qu.:753.0 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :17.365 Max. :840.4 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :25.01 Min. : 1.00 Min. :25 inf15:20 Min. :25.45
## 1st Qu.:25.23 1st Qu.: 25.75 1st Qu.:25 sup15:80 1st Qu.:27.79
## Median :25.50 Median : 50.50 Median :25 Median :29.43
## Mean :25.47 Mean : 50.50 Mean :25 Mean :30.25
## 3rd Qu.:25.68 3rd Qu.: 75.25 3rd Qu.:25 3rd Qu.:32.35
## Max. :25.98 Max. :100.00 Max. :25 Max. :38.64
## CW
## Min. : 614.2
## 1st Qu.: 924.7
## Median :1087.4
## Mean :1137.7
## 3rd Qu.:1342.4
## Max. :1924.4
Gestational age between 26 and 27 weeks
#Gestational age simulation with uniform distribution betwwen entre 26 et 27 weeks
set.seed(2345)
gen_terme_26 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 26, max = 27)) }
terme_26 <- 1:1 %>% map_df(gen_terme_26)
# Birth weigh simulation with truncated normal distribution (gestational age between 26 and 27 weeks)
set.seed(2345)
gen_poids_26 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=660, b=995, mean = 820, sd = 138)) }
poids_26 <- 1:1 %>% map_df(gen_poids_26)
# creatinine simulation with truncated normal distribution (gestational age between 26 and 27 weeks)
set.seed(2345)
gen_creat_26 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_26 <- 1:1 %>% map_df(gen_creat_26)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 26 and 27 weeks)
set.seed(2345)
gen_infection_26<- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_26 <- 1:1 %>% map_df(gen_infection_26)
# weight gain simulation with truncated normal distribution (gestational age between 26 and 27 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf26 <- poids_26 %>% bind_cols(creat_26) %>% bind_cols(infection_26)%>%bind_cols(terme_26)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 26)
summary(data_demog_inf26)
## PN CREA infection GA
## Min. :673.8 Min. : 40.03 Min. : 3.031 Min. :26.01
## 1st Qu.:757.4 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:26.23
## Median :820.2 Median : 70.70 Median :27.495 Median :26.50
## Mean :819.8 Mean : 77.25 Mean :33.466 Mean :26.47
## 3rd Qu.:865.6 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:26.68
## Max. :989.2 Max. :144.55 Max. :89.937 Max. :26.98
## ID weeks
## Min. : 1.00 Min. :26
## 1st Qu.: 25.75 1st Qu.:26
## Median : 50.50 Median :26
## Mean : 50.50 Mean :26
## 3rd Qu.: 75.25 3rd Qu.:26
## Max. :100.00 Max. :26
data_demog_inf26<- data_demog_inf26%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf26_inf15 <- data_demog_inf26%>% filter(periode == "inf15")
data_demog_inf26_sup15 <- data_demog_inf26%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_26_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 2.5, sd = 5)) }
gain_26_inf15 <- 1:1 %>% map_df(gen_gain_26_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_26_inf15 <- as_tibble(gain_26_inf15)
gain_26_inf15_bis <- gain_26_inf15 %>% bind_cols(data_demog_inf26_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_26_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.3, sd = 2.2)) }
gain_26_sup15 <- 1:1 %>% map_df(gen_gain_26_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_26_sup15 <- as_tibble(gain_26_sup15)
gain_26_sup15_bis <- gain_26_sup15 %>% bind_cols(data_demog_inf26_sup15)%>% dplyr::select(-ID6)
data_demog_inf26_final <-gain_26_inf15_bis%>% bind_rows(gain_26_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf26_final <- data_demog_inf26_final %>% mutate (PMA = GA+infection/7)
data_demog_inf26_final <- data_demog_inf26_final %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf26_final)
## gain PN CREA infection
## Min. : 0.004318 Min. :673.8 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.649118 1st Qu.:757.4 1st Qu.: 57.46 1st Qu.:17.458
## Median :13.612963 Median :820.2 Median : 70.70 Median :27.495
## Mean :12.368598 Mean :819.8 Mean : 77.25 Mean :33.466
## 3rd Qu.:14.906179 3rd Qu.:865.6 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :18.821767 Max. :989.2 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :26.01 Min. : 1.00 Min. :26 inf15:20 Min. :26.45
## 1st Qu.:26.23 1st Qu.: 25.75 1st Qu.:26 sup15:80 1st Qu.:28.79
## Median :26.50 Median : 50.50 Median :26 Median :30.43
## Mean :26.47 Mean : 50.50 Mean :26 Mean :31.25
## 3rd Qu.:26.68 3rd Qu.: 75.25 3rd Qu.:26 3rd Qu.:33.35
## Max. :26.98 Max. :100.00 Max. :26 Max. :39.64
## CW
## Min. : 731.4
## 1st Qu.:1024.1
## Median :1221.4
## Mean :1277.1
## 3rd Qu.:1489.4
## Max. :2104.5
Gestational age between 27 and 28 weeks
#Gestational age simulation with uniform distribution betwwen entre 27 et 28 weeks
set.seed(2345)
gen_terme_27 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 27, max = 28)) }
terme_27 <- 1:1 %>% map_df(gen_terme_27)
# Birth weigh simulation with truncated normal distribution (gestational age between 27 and 28 weeks)
set.seed(2345)
gen_poids_27<- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=685, b=1199, mean = 943, sd = 201)) }
poids_27 <- 1:1 %>% map_df(gen_poids_27)
# creatinine simulation with truncated normal distribution (gestational age between 27 and 28 weeks)
set.seed(2345)
gen_creat_27 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_27 <- 1:1 %>% map_df(gen_creat_27)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 27 and 28 weeks)
set.seed(2345)
gen_infection_27<- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_27 <- 1:1 %>% map_df(gen_infection_27)
# weight gain simulation with truncated normal distribution (gestational age between 27 and 28 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf27 <- poids_27 %>% bind_cols(creat_27) %>% bind_cols(infection_27)%>%bind_cols(terme_27)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 27)
summary(data_demog_inf27)
## PN CREA infection GA
## Min. : 706.2 Min. : 40.03 Min. : 3.031 Min. :27.01
## 1st Qu.: 834.4 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:27.23
## Median : 930.8 Median : 70.70 Median :27.495 Median :27.50
## Mean : 930.7 Mean : 77.25 Mean :33.466 Mean :27.47
## 3rd Qu.:1000.5 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:27.68
## Max. :1190.1 Max. :144.55 Max. :89.937 Max. :27.98
## ID weeks
## Min. : 1.00 Min. :27
## 1st Qu.: 25.75 1st Qu.:27
## Median : 50.50 Median :27
## Mean : 50.50 Mean :27
## 3rd Qu.: 75.25 3rd Qu.:27
## Max. :100.00 Max. :27
data_demog_inf27<- data_demog_inf27%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf27_inf15 <- data_demog_inf27%>% filter(periode == "inf15")
data_demog_inf27_sup15 <- data_demog_inf27%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_27_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 2.5, sd = 5)) }
gain_27_inf15 <- 1:1 %>% map_df(gen_gain_27_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_27_inf15 <- as_tibble(gain_27_inf15)
gain_27_inf15_bis <- gain_27_inf15 %>% bind_cols(data_demog_inf27_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_27_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.3, sd = 2.2)) }
gain_27_sup15 <- 1:1 %>% map_df(gen_gain_27_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_27_sup15 <- as_tibble(gain_27_sup15)
gain_27_sup15_bis <- gain_27_sup15 %>% bind_cols(data_demog_inf27_sup15)%>% dplyr::select(-ID6)
data_demog_inf27_final <-gain_27_inf15_bis%>% bind_rows(gain_27_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf27_final <- data_demog_inf27_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf27_final)
## gain PN CREA infection
## Min. : 0.004318 Min. : 706.2 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.649118 1st Qu.: 834.4 1st Qu.: 57.46 1st Qu.:17.458
## Median :13.612963 Median : 930.8 Median : 70.70 Median :27.495
## Mean :12.368598 Mean : 930.7 Mean : 77.25 Mean :33.466
## 3rd Qu.:14.906179 3rd Qu.:1000.5 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :18.821767 Max. :1190.1 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :27.01 Min. : 1.00 Min. :27 inf15:20 Min. :27.45
## 1st Qu.:27.23 1st Qu.: 25.75 1st Qu.:27 sup15:80 1st Qu.:29.79
## Median :27.50 Median : 50.50 Median :27 Median :31.43
## Mean :27.47 Mean : 50.50 Mean :27 Mean :32.25
## 3rd Qu.:27.68 3rd Qu.: 75.25 3rd Qu.:27 3rd Qu.:34.35
## Max. :27.98 Max. :100.00 Max. :27 Max. :40.64
## CW
## Min. : 789.6
## 1st Qu.:1143.6
## Median :1340.4
## Mean :1388.0
## 3rd Qu.:1568.4
## Max. :2273.5
Gestational age between 28 and 29 weeks
#Gestational age simulation with uniform distribution betwwen entre 28 et 29 weeks
set.seed(2345)
gen_terme_28 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 28, max = 29)) }
terme_28 <- 1:1 %>% map_df(gen_terme_28)
# Birth weigh simulation with truncated normal distribution (gestational age between 28 and 29 weeks)
set.seed(2345)
gen_poids_28<- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=750, b=1270, mean = 1028, sd = 196)) }
poids_28 <- 1:1 %>% map_df(gen_poids_28)
# creatinine simulation with truncated normal distribution (gestational age between 28 and 29 weeks)
set.seed(2345)
gen_creat_28 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_28 <- 1:1 %>% map_df(gen_creat_28)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 28 and 29 weeks)
set.seed(2345)
gen_infection_28<- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_28 <- 1:1 %>% map_df(gen_infection_28)
# weight gain simulation with truncated normal distribution (gestational age between 28 and 29 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf28 <- poids_28 %>% bind_cols(creat_28) %>% bind_cols(infection_28)%>%bind_cols(terme_28)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 28)
summary(data_demog_inf28)
## PN CREA infection GA
## Min. : 754.0 Min. : 40.03 Min. : 3.031 Min. :28.01
## 1st Qu.: 883.9 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:28.23
## Median :1008.2 Median : 70.70 Median :27.495 Median :28.50
## Mean : 998.5 Mean : 77.25 Mean :33.466 Mean :28.47
## 3rd Qu.:1081.5 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:28.68
## Max. :1266.5 Max. :144.55 Max. :89.937 Max. :28.98
## ID weeks
## Min. : 1.00 Min. :28
## 1st Qu.: 25.75 1st Qu.:28
## Median : 50.50 Median :28
## Mean : 50.50 Mean :28
## 3rd Qu.: 75.25 3rd Qu.:28
## Max. :100.00 Max. :28
data_demog_inf28<- data_demog_inf28%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf28_inf15 <- data_demog_inf28%>% filter(periode == "inf15")
data_demog_inf28_sup15 <- data_demog_inf28%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_28_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 2.5, sd = 5)) }
gain_28_inf15 <- 1:1 %>% map_df(gen_gain_28_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_28_inf15 <- as_tibble(gain_28_inf15)
gain_28_inf15_bis <- gain_28_inf15 %>% bind_cols(data_demog_inf28_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_28_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.3, sd = 2.2)) }
gain_28_sup15 <- 1:1 %>% map_df(gen_gain_28_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_28_sup15 <- as_tibble(gain_28_sup15)
gain_28_sup15_bis <- gain_28_sup15 %>% bind_cols(data_demog_inf28_sup15)%>% dplyr::select(-ID6)
data_demog_inf28_final <-gain_28_inf15_bis%>% bind_rows(gain_28_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf28_final <- data_demog_inf28_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf28_final)
## gain PN CREA infection
## Min. : 0.004318 Min. : 754.0 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.649118 1st Qu.: 883.9 1st Qu.: 57.46 1st Qu.:17.458
## Median :13.612963 Median :1008.2 Median : 70.70 Median :27.495
## Mean :12.368598 Mean : 998.5 Mean : 77.25 Mean :33.466
## 3rd Qu.:14.906179 3rd Qu.:1081.5 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :18.821767 Max. :1266.5 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :28.01 Min. : 1.00 Min. :28 inf15:20 Min. :28.45
## 1st Qu.:28.23 1st Qu.: 25.75 1st Qu.:28 sup15:80 1st Qu.:30.79
## Median :28.50 Median : 50.50 Median :28 Median :32.43
## Mean :28.47 Mean : 50.50 Mean :28 Mean :33.25
## 3rd Qu.:28.68 3rd Qu.: 75.25 3rd Qu.:28 3rd Qu.:35.35
## Max. :28.98 Max. :100.00 Max. :28 Max. :41.64
## CW
## Min. : 837
## 1st Qu.:1227
## Median :1400
## Mean :1456
## 3rd Qu.:1726
## Max. :2318
Gestational age between 29 and 30 weeks
#Gestational age simulation with uniform distribution betwwen entre 29 et 30 weeks
set.seed(2345)
gen_terme_29 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 29, max = 30)) }
terme_29 <- 1:1 %>% map_df(gen_terme_29)
# Birth weigh simulation with truncated normal distribution (gestational age between 29 and 30 weeks)
set.seed(2345)
gen_poids_29<- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=908, b=1472, mean = 1205, sd = 256)) }
poids_29 <- 1:1 %>% map_df(gen_poids_29)
# creatinine simulation with truncated normal distribution (gestational age between 29 and 30 weeks)
set.seed(2345)
gen_creat_29 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_29 <- 1:1 %>% map_df(gen_creat_29)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 29 and 30 weeks)
set.seed(2345)
gen_infection_29<- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_29 <- 1:1 %>% map_df(gen_infection_29)
# weight gain simulation with truncated normal distribution (gestational age between 29 and 30 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf29 <- poids_29 %>% bind_cols(creat_29) %>% bind_cols(infection_29)%>%bind_cols(terme_29)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 29)
summary(data_demog_inf29)
## PN CREA infection GA
## Min. : 931.3 Min. : 40.03 Min. : 3.031 Min. :29.01
## 1st Qu.:1088.9 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:29.23
## Median :1186.8 Median : 70.70 Median :27.495 Median :29.50
## Mean :1191.2 Mean : 77.25 Mean :33.466 Mean :29.47
## 3rd Qu.:1298.6 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:29.68
## Max. :1462.3 Max. :144.55 Max. :89.937 Max. :29.98
## ID weeks
## Min. : 1.00 Min. :29
## 1st Qu.: 25.75 1st Qu.:29
## Median : 50.50 Median :29
## Mean : 50.50 Mean :29
## 3rd Qu.: 75.25 3rd Qu.:29
## Max. :100.00 Max. :29
data_demog_inf29<- data_demog_inf29%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf29_inf15 <- data_demog_inf29%>% filter(periode == "inf15")
data_demog_inf29_sup15 <- data_demog_inf29%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_29_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_29_inf15 <- 1:1 %>% map_df(gen_gain_29_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_29_inf15 <- as_tibble(gain_29_inf15)
gain_29_inf15_bis <- gain_29_inf15 %>% bind_cols(data_demog_inf29_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_29_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_29_sup15 <- 1:1 %>% map_df(gen_gain_29_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_29_sup15 <- as_tibble(gain_29_sup15)
gain_29_sup15_bis <- gain_29_sup15 %>% bind_cols(data_demog_inf29_sup15)%>% dplyr::select(-ID6)
data_demog_inf29_final <-gain_29_inf15_bis%>% bind_rows(gain_29_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf29_final <- data_demog_inf29_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf29_final)
## gain PN CREA infection
## Min. : 0.08977 Min. : 931.3 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:1088.9 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :1186.8 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :1191.2 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:1298.6 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :1462.3 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :29.01 Min. : 1.00 Min. :29 inf15:20 Min. :29.45
## 1st Qu.:29.23 1st Qu.: 25.75 1st Qu.:29 sup15:80 1st Qu.:31.79
## Median :29.50 Median : 50.50 Median :29 Median :33.43
## Mean :29.47 Mean : 50.50 Mean :29 Mean :34.25
## 3rd Qu.:29.68 3rd Qu.: 75.25 3rd Qu.:29 3rd Qu.:36.35
## Max. :29.98 Max. :100.00 Max. :29 Max. :42.64
## CW
## Min. :1006
## 1st Qu.:1424
## Median :1611
## Mean :1670
## 3rd Qu.:1901
## Max. :2686
Gestational age between 30 and 31 weeks
#Gestational age simulation with uniform distribution betwwen entre 30 et 31 weeks
set.seed(2345)
gen_terme_30 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 30, max = 31)) }
terme_30 <- 1:1 %>% map_df(gen_terme_30)
# Birth weigh simulation with truncated normal distribution (gestational age between 30 and 31 weeks)
set.seed(2345)
gen_poids_30<- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=1041, b=1672, mean = 1377, sd = 311)) }
poids_30 <- 1:1 %>% map_df(gen_poids_30)
# creatinine simulation with truncated normal distribution (gestational age between 30 and 31 weeks)
set.seed(2345)
gen_creat_30 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_30 <- 1:1 %>% map_df(gen_creat_30)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 30 and 31 weeks)
set.seed(2345)
gen_infection_30 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_30 <- 1:1 %>% map_df(gen_infection_30)
# weight gain simulation with truncated normal distribution (gestational age between 30 and 31 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf30 <- poids_30 %>% bind_cols(creat_30) %>% bind_cols(infection_30)%>%bind_cols(terme_30)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 30)
summary(data_demog_inf30)
## PN CREA infection GA
## Min. :1067 Min. : 40.03 Min. : 3.031 Min. :30.01
## 1st Qu.:1232 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:30.23
## Median :1353 Median : 70.70 Median :27.495 Median :30.50
## Mean :1357 Mean : 77.25 Mean :33.466 Mean :30.47
## 3rd Qu.:1492 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:30.68
## Max. :1661 Max. :144.55 Max. :89.937 Max. :30.98
## ID weeks
## Min. : 1.00 Min. :30
## 1st Qu.: 25.75 1st Qu.:30
## Median : 50.50 Median :30
## Mean : 50.50 Mean :30
## 3rd Qu.: 75.25 3rd Qu.:30
## Max. :100.00 Max. :30
data_demog_inf30<- data_demog_inf30%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf30_inf15 <- data_demog_inf30%>% filter(periode == "inf15")
data_demog_inf30_sup15 <- data_demog_inf30%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_30_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_30_inf15 <- 1:1 %>% map_df(gen_gain_30_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_30_inf15 <- as_tibble(gain_30_inf15)
gain_30_inf15_bis <- gain_30_inf15 %>% bind_cols(data_demog_inf30_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_30_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_30_sup15 <- 1:1 %>% map_df(gen_gain_30_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_30_sup15 <- as_tibble(gain_30_sup15)
gain_30_sup15_bis <- gain_30_sup15 %>% bind_cols(data_demog_inf30_sup15)%>% dplyr::select(-ID6)
data_demog_inf30_final <-gain_30_inf15_bis%>% bind_rows(gain_30_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf30_final <- data_demog_inf30_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf30_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :1067 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:1232 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :1353 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :1357 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:1492 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :1661 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :30.01 Min. : 1.00 Min. :30 inf15:20 Min. :30.45
## 1st Qu.:30.23 1st Qu.: 25.75 1st Qu.:30 sup15:80 1st Qu.:32.79
## Median :30.50 Median : 50.50 Median :30 Median :34.43
## Mean :30.47 Mean : 50.50 Mean :30 Mean :35.25
## 3rd Qu.:30.68 3rd Qu.: 75.25 3rd Qu.:30 3rd Qu.:37.35
## Max. :30.98 Max. :100.00 Max. :30 Max. :43.64
## CW
## Min. :1202
## 1st Qu.:1544
## Median :1782
## Mean :1836
## 3rd Qu.:2048
## Max. :2623
Gestational age between 31 and 32 weeks
#Gestational age simulation with uniform distribution betwwen entre 31 et 32 weeks
set.seed(2345)
gen_terme_31 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 31, max = 32)) }
terme_31 <- 1:1 %>% map_df(gen_terme_31)
# Birth weigh simulation with truncated normal distribution (gestational age between 31 and 32 weeks)
set.seed(2345)
gen_poids_31<- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=1140, b=1933, mean = 1550, sd = 333)) }
poids_31 <- 1:1 %>% map_df(gen_poids_31)
# creatinine simulation with truncated normal distribution (gestational age between 31 and 32 weeks)
set.seed(2345)
gen_creat_31 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_31 <- 1:1 %>% map_df(gen_creat_31)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 31 and 32 weeks)
set.seed(2345)
gen_infection_31 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_31 <- 1:1 %>% map_df(gen_infection_31)
# weight gain simulation with truncated normal distribution (gestational age between 31 and 32 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf31 <- poids_31 %>% bind_cols(creat_31) %>% bind_cols(infection_31)%>%bind_cols(terme_31)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 31)
summary(data_demog_inf31)
## PN CREA infection GA
## Min. :1173 Min. : 40.03 Min. : 3.031 Min. :31.01
## 1st Qu.:1380 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:31.23
## Median :1532 Median : 70.70 Median :27.495 Median :31.50
## Mean :1530 Mean : 77.25 Mean :33.466 Mean :31.47
## 3rd Qu.:1662 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:31.68
## Max. :1919 Max. :144.55 Max. :89.937 Max. :31.98
## ID weeks
## Min. : 1.00 Min. :31
## 1st Qu.: 25.75 1st Qu.:31
## Median : 50.50 Median :31
## Mean : 50.50 Mean :31
## 3rd Qu.: 75.25 3rd Qu.:31
## Max. :100.00 Max. :31
data_demog_inf31<- data_demog_inf31%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf31_inf15 <- data_demog_inf31%>% filter(periode == "inf15")
data_demog_inf31_sup15 <- data_demog_inf31%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_31_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_31_inf15 <- 1:1 %>% map_df(gen_gain_31_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_31_inf15 <- as_tibble(gain_31_inf15)
gain_31_inf15_bis <- gain_31_inf15 %>% bind_cols(data_demog_inf31_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_31_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_31_sup15 <- 1:1 %>% map_df(gen_gain_31_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_31_sup15 <- as_tibble(gain_31_sup15)
gain_31_sup15_bis <- gain_31_sup15 %>% bind_cols(data_demog_inf31_sup15)%>% dplyr::select(-ID6)
data_demog_inf31_final <-gain_31_inf15_bis%>% bind_rows(gain_31_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf31_final <- data_demog_inf31_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf31_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :1173 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:1380 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :1532 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :1530 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:1662 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :1919 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :31.01 Min. : 1.00 Min. :31 inf15:20 Min. :31.45
## 1st Qu.:31.23 1st Qu.: 25.75 1st Qu.:31 sup15:80 1st Qu.:33.79
## Median :31.50 Median : 50.50 Median :31 Median :35.43
## Mean :31.47 Mean : 50.50 Mean :31 Mean :36.25
## 3rd Qu.:31.68 3rd Qu.: 75.25 3rd Qu.:31 3rd Qu.:38.35
## Max. :31.98 Max. :100.00 Max. :31 Max. :44.64
## CW
## Min. :1251
## 1st Qu.:1706
## Median :1964
## Mean :2009
## 3rd Qu.:2230
## Max. :3434
Gestational age between 32 and 33 weeks
#Gestational age simulation with uniform distribution betwwen entre 32 et 33 weeks
set.seed(2345)
gen_terme_32 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 32, max = 32)) }
terme_32 <- 1:1 %>% map_df(gen_terme_32)
# Birth weigh simulation with truncated normal distribution (gestational age between 32 and 33 weeks)
set.seed(2345)
gen_poids_32<- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=1328, b=2097, mean = 1713, sd = 331)) }
poids_32 <- 1:1 %>% map_df(gen_poids_32)
# creatinine simulation with truncated normal distribution (gestational age between 32 and 33 weeks)
set.seed(2345)
gen_creat_32 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_32 <- 1:1 %>% map_df(gen_creat_32)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 32 and 33 weeks)
set.seed(2345)
gen_infection_32 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_32 <- 1:1 %>% map_df(gen_infection_32)
# weight gain simulation with truncated normal distribution (gestational age between 32 and 33 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 day
data_demog_inf32 <- poids_32 %>% bind_cols(creat_32) %>% bind_cols(infection_32)%>%bind_cols(terme_32)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 32)
summary(data_demog_inf32)
## PN CREA infection GA ID
## Min. :1360 Min. : 40.03 Min. : 3.031 Min. :32 Min. : 1.00
## 1st Qu.:1551 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:32 1st Qu.: 25.75
## Median :1703 Median : 70.70 Median :27.495 Median :32 Median : 50.50
## Mean :1701 Mean : 77.25 Mean :33.466 Mean :32 Mean : 50.50
## 3rd Qu.:1812 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:32 3rd Qu.: 75.25
## Max. :2084 Max. :144.55 Max. :89.937 Max. :32 Max. :100.00
## weeks
## Min. :32
## 1st Qu.:32
## Median :32
## Mean :32
## 3rd Qu.:32
## Max. :32
data_demog_inf32<- data_demog_inf32%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf32_inf15 <- data_demog_inf32%>% filter(periode == "inf15")
data_demog_inf32_sup15 <- data_demog_inf32%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_32_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_32_inf15 <- 1:1 %>% map_df(gen_gain_32_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_32_inf15 <- as_tibble(gain_32_inf15)
gain_32_inf15_bis <- gain_32_inf15 %>% bind_cols(data_demog_inf32_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_32_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_32_sup15 <- 1:1 %>% map_df(gen_gain_32_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_32_sup15 <- as_tibble(gain_32_sup15)
gain_32_sup15_bis <- gain_32_sup15 %>% bind_cols(data_demog_inf32_sup15)%>% dplyr::select(-ID6)
data_demog_inf32_final <-gain_32_inf15_bis%>% bind_rows(gain_32_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf32_final <- data_demog_inf32_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf32_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :1360 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:1551 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :1703 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :1701 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:1812 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :2084 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :32 Min. : 1.00 Min. :32 inf15:20 Min. :32.43
## 1st Qu.:32 1st Qu.: 25.75 1st Qu.:32 sup15:80 1st Qu.:34.49
## Median :32 Median : 50.50 Median :32 Median :35.93
## Mean :32 Mean : 50.50 Mean :32 Mean :36.78
## 3rd Qu.:32 3rd Qu.: 75.25 3rd Qu.:32 3rd Qu.:38.99
## Max. :32 Max. :100.00 Max. :32 Max. :44.85
## CW
## Min. :1485
## 1st Qu.:1873
## Median :2133
## Mean :2180
## 3rd Qu.:2390
## Max. :3553
Gestational age between 33 and 34 weeks
#Gestational age simulation with uniform distribution betwwen entre 33 et 34 weeks
set.seed(2345)
gen_terme_33 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 33, max = 34)) }
terme_33 <- 1:1 %>% map_df(gen_terme_33)
# Birth weigh simulation with truncated normal distribution (gestational age between 33 and 34 weeks)
set.seed(2345)
gen_poids_33 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=1479, b=2370, mean = 1918, sd = 377)) }
poids_33 <- 1:1 %>% map_df(gen_poids_33)
# creatinine simulation with truncated normal distribution (gestational age between 33 and 34 weeks)
set.seed(2345)
gen_creat_33 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_33 <- 1:1 %>% map_df(gen_creat_33)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 33 and 34 weeks)
set.seed(2345)
gen_infection_33 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_33 <- 1:1 %>% map_df(gen_infection_33)
# weight gain simulation with truncated normal distribution (gestational age between 33 and 34 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf33 <- poids_33 %>% bind_cols(creat_33) %>% bind_cols(infection_33)%>%bind_cols(terme_33)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 33)
summary(data_demog_inf33)
## PN CREA infection GA
## Min. :1516 Min. : 40.03 Min. : 3.031 Min. :33.01
## 1st Qu.:1738 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:33.23
## Median :1913 Median : 70.70 Median :27.495 Median :33.50
## Mean :1910 Mean : 77.25 Mean :33.466 Mean :33.47
## 3rd Qu.:2035 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:33.68
## Max. :2355 Max. :144.55 Max. :89.937 Max. :33.98
## ID weeks
## Min. : 1.00 Min. :33
## 1st Qu.: 25.75 1st Qu.:33
## Median : 50.50 Median :33
## Mean : 50.50 Mean :33
## 3rd Qu.: 75.25 3rd Qu.:33
## Max. :100.00 Max. :33
data_demog_inf33<- data_demog_inf33%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf33_inf15 <- data_demog_inf33%>% filter(periode == "inf15")
data_demog_inf33_sup15 <- data_demog_inf33%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_33_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_33_inf15 <- 1:1 %>% map_df(gen_gain_33_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_33_inf15 <- as_tibble(gain_33_inf15)
gain_33_inf15_bis <- gain_33_inf15 %>% bind_cols(data_demog_inf33_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_33_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_33_sup15 <- 1:1 %>% map_df(gen_gain_33_sup15)%>% mutate(ID6 =(21:100))%>%dplyr:: select(-ID5)
gain_33_sup15 <- as_tibble(gain_33_sup15)
gain_33_sup15_bis <- gain_33_sup15 %>% bind_cols(data_demog_inf33_sup15)%>% dplyr::select(-ID6)
data_demog_inf33_final <-gain_33_inf15_bis%>% bind_rows(gain_33_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf33_final <- data_demog_inf33_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf33_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :1516 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:1738 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :1913 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :1910 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:2035 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :2355 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :33.01 Min. : 1.00 Min. :33 inf15:20 Min. :33.45
## 1st Qu.:33.23 1st Qu.: 25.75 1st Qu.:33 sup15:80 1st Qu.:35.79
## Median :33.50 Median : 50.50 Median :33 Median :37.43
## Mean :33.47 Mean : 50.50 Mean :33 Mean :38.25
## 3rd Qu.:33.68 3rd Qu.: 75.25 3rd Qu.:33 3rd Qu.:40.35
## Max. :33.98 Max. :100.00 Max. :33 Max. :46.64
## CW
## Min. :1558
## 1st Qu.:2058
## Median :2347
## Mean :2389
## 3rd Qu.:2650
## Max. :3870
Gestational age between 34 and 35 weeks
#Gestational age simulation with uniform distribution betwwen entre 34 et 35 weeks
set.seed(2345)
gen_terme_34 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 34, max = 35)) }
terme_34 <- 1:1 %>% map_df(gen_terme_34)
# Birth weigh simulation with truncated normal distribution (gestational age between 34 and 35 weeks)
set.seed(2345)
gen_poids_34 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=1625, b=2640, mean = 2138, sd = 436)) }
poids_34 <- 1:1 %>% map_df(gen_poids_34)
# creatinine simulation with truncated normal distribution (gestational age between 34 and 35 weeks)
set.seed(2345)
gen_creat_34 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_34 <- 1:1 %>% map_df(gen_creat_34)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 34 and 35 weeks)
set.seed(2345)
gen_infection_34 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_34 <- 1:1 %>% map_df(gen_infection_34)
# weight gain simulation with truncated normal distribution (gestational age between 34 and 35 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf34 <- poids_34 %>% bind_cols(creat_34) %>% bind_cols(infection_34)%>%bind_cols(terme_34)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 34)
summary(data_demog_inf34)
## PN CREA infection GA
## Min. :1667 Min. : 40.03 Min. : 3.031 Min. :34.01
## 1st Qu.:1932 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:34.23
## Median :2126 Median : 70.70 Median :27.495 Median :34.50
## Mean :2124 Mean : 77.25 Mean :33.466 Mean :34.47
## 3rd Qu.:2293 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:34.68
## Max. :2622 Max. :144.55 Max. :89.937 Max. :34.98
## ID weeks
## Min. : 1.00 Min. :34
## 1st Qu.: 25.75 1st Qu.:34
## Median : 50.50 Median :34
## Mean : 50.50 Mean :34
## 3rd Qu.: 75.25 3rd Qu.:34
## Max. :100.00 Max. :34
data_demog_inf34<- data_demog_inf34%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf34_inf15 <- data_demog_inf34%>% filter(periode == "inf15")
data_demog_inf34_sup15 <- data_demog_inf34%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_34_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_34_inf15 <- 1:1 %>% map_df(gen_gain_34_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_34_inf15 <- as_tibble(gain_34_inf15)
gain_34_inf15_bis <- gain_34_inf15 %>% bind_cols(data_demog_inf34_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_34_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_34_sup15 <- 1:1 %>% map_df(gen_gain_33_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_34_sup15 <- as_tibble(gain_34_sup15)
gain_34_sup15_bis <- gain_34_sup15 %>% bind_cols(data_demog_inf34_sup15)%>% dplyr::select(-ID6)
data_demog_inf34_final <-gain_34_inf15_bis%>% bind_rows(gain_34_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf34_final <- data_demog_inf34_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf34_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :1667 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:1932 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :2126 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :2124 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:2293 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :2622 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :34.01 Min. : 1.00 Min. :34 inf15:20 Min. :34.45
## 1st Qu.:34.23 1st Qu.: 25.75 1st Qu.:34 sup15:80 1st Qu.:36.79
## Median :34.50 Median : 50.50 Median :34 Median :38.43
## Mean :34.47 Mean : 50.50 Mean :34 Mean :39.25
## 3rd Qu.:34.68 3rd Qu.: 75.25 3rd Qu.:34 3rd Qu.:41.35
## Max. :34.98 Max. :100.00 Max. :34 Max. :47.64
## CW
## Min. :1766
## 1st Qu.:2306
## Median :2571
## Mean :2603
## 3rd Qu.:2855
## Max. :4138
Gestational age between 35 and 36 weeks
#Gestational age simulation with uniform distribution betwwen entre 35 et 36 weeks
set.seed(2345)
gen_terme_35 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 35, max = 36)) }
terme_35 <- 1:1 %>% map_df(gen_terme_35)
# Birth weigh simulation with truncated normal distribution (gestational age between 35 and 36 weeks)
set.seed(2345)
gen_poids_35 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=1780, b=3010, mean = 2389, sd = 491)) }
poids_35 <- 1:1 %>% map_df(gen_poids_35)
# creatinine simulation with truncated normal distribution (gestational age between 35 and 36 weeks)
set.seed(2345)
gen_creat_35 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_35 <- 1:1 %>% map_df(gen_creat_35)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 35 and 36 weeks)
set.seed(2345)
gen_infection_35 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_35 <- 1:1 %>% map_df(gen_infection_35)
# weight gain simulation with truncated normal distribution (gestational age between 35 and 36 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf35 <- poids_35 %>% bind_cols(creat_35) %>% bind_cols(infection_35)%>%bind_cols(terme_35)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 35)
summary(data_demog_inf35)
## PN CREA infection GA
## Min. :1831 Min. : 40.03 Min. : 3.031 Min. :35.01
## 1st Qu.:2137 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:35.23
## Median :2368 Median : 70.70 Median :27.495 Median :35.50
## Mean :2368 Mean : 77.25 Mean :33.466 Mean :35.47
## 3rd Qu.:2535 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:35.68
## Max. :2989 Max. :144.55 Max. :89.937 Max. :35.98
## ID weeks
## Min. : 1.00 Min. :35
## 1st Qu.: 25.75 1st Qu.:35
## Median : 50.50 Median :35
## Mean : 50.50 Mean :35
## 3rd Qu.: 75.25 3rd Qu.:35
## Max. :100.00 Max. :35
data_demog_inf35<- data_demog_inf35%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf35_inf15 <- data_demog_inf35%>% filter(periode == "inf15")
data_demog_inf35_sup15 <- data_demog_inf35%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_35_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_35_inf15 <- 1:1 %>% map_df(gen_gain_35_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_35_inf15 <- as_tibble(gain_35_inf15)
gain_35_inf15_bis <- gain_35_inf15 %>% bind_cols(data_demog_inf35_inf15)%>%dplyr:: select(-ID6)
set.seed(2345)
gen_gain_35_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_35_sup15 <- 1:1 %>% map_df(gen_gain_35_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_35_sup15 <- as_tibble(gain_35_sup15)
gain_35_sup15_bis <- gain_35_sup15 %>% bind_cols(data_demog_inf35_sup15)%>% dplyr::select(-ID6)
data_demog_inf35_final <-gain_35_inf15_bis%>% bind_rows(gain_35_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf35_final <- data_demog_inf35_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf35_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :1831 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:2137 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :2368 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :2368 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:2535 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :2989 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :35.01 Min. : 1.00 Min. :35 inf15:20 Min. :35.45
## 1st Qu.:35.23 1st Qu.: 25.75 1st Qu.:35 sup15:80 1st Qu.:37.79
## Median :35.50 Median : 50.50 Median :35 Median :39.43
## Mean :35.47 Mean : 50.50 Mean :35 Mean :40.25
## 3rd Qu.:35.68 3rd Qu.: 75.25 3rd Qu.:35 3rd Qu.:42.35
## Max. :35.98 Max. :100.00 Max. :35 Max. :48.64
## CW
## Min. :1972
## 1st Qu.:2550
## Median :2828
## Mean :2847
## 3rd Qu.:3122
## Max. :3954
Gestational age between 36 and 37 weeks
#Gestational age simulation with uniform distribution betwwen entre 36 et 37 weeks
set.seed(2345)
gen_terme_36 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 36, max = 37)) }
terme_36 <- 1:1 %>% map_df(gen_terme_36)
# Birth weigh simulation with truncated normal distribution (gestational age between 36 and 37 weeks)
set.seed(2345)
gen_poids_36 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=2042, b=3289, mean = 2650, sd = 509)) }
poids_36 <- 1:1 %>% map_df(gen_poids_36)
# creatinine simulation with truncated normal distribution (gestational age between 36 and 37 weeks)
set.seed(2345)
gen_creat_36 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_36 <- 1:1 %>% map_df(gen_creat_36)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 36 and 37 weeks)
set.seed(2345)
gen_infection_36 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_36 <- 1:1 %>% map_df(gen_infection_36)
# weight gain simulation with truncated normal distribution (gestational age between 36 and 37 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf36 <- poids_36 %>% bind_cols(creat_36) %>% bind_cols(infection_36)%>%bind_cols(terme_36)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 36)
summary(data_demog_inf36)
## PN CREA infection GA
## Min. :2094 Min. : 40.03 Min. : 3.031 Min. :36.01
## 1st Qu.:2417 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:36.23
## Median :2650 Median : 70.70 Median :27.495 Median :36.50
## Mean :2647 Mean : 77.25 Mean :33.466 Mean :36.47
## 3rd Qu.:2820 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:36.68
## Max. :3267 Max. :144.55 Max. :89.937 Max. :36.98
## ID weeks
## Min. : 1.00 Min. :36
## 1st Qu.: 25.75 1st Qu.:36
## Median : 50.50 Median :36
## Mean : 50.50 Mean :36
## 3rd Qu.: 75.25 3rd Qu.:36
## Max. :100.00 Max. :36
data_demog_inf36<- data_demog_inf36%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf36_inf15 <- data_demog_inf36%>% filter(periode == "inf15")
data_demog_inf36_sup15 <- data_demog_inf36%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_36_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_36_inf15 <- 1:1 %>% map_df(gen_gain_36_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_36_inf15 <- as_tibble(gain_36_inf15)
gain_36_inf15_bis <- gain_36_inf15 %>% bind_cols(data_demog_inf36_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_36_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_36_sup15 <- 1:1 %>% map_df(gen_gain_36_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_36_sup15 <- as_tibble(gain_36_sup15)
gain_36_sup15_bis <- gain_36_sup15 %>% bind_cols(data_demog_inf36_sup15)%>%dplyr:: select(-ID6)
data_demog_inf36_final <-gain_36_inf15_bis%>% bind_rows(gain_36_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf36_final <- data_demog_inf36_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf36_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :2094 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:2417 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :2650 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :2647 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:2820 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :3267 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :36.01 Min. : 1.00 Min. :36 inf15:20 Min. :36.45
## 1st Qu.:36.23 1st Qu.: 25.75 1st Qu.:36 sup15:80 1st Qu.:38.79
## Median :36.50 Median : 50.50 Median :36 Median :40.43
## Mean :36.47 Mean : 50.50 Mean :36 Mean :41.25
## 3rd Qu.:36.68 3rd Qu.: 75.25 3rd Qu.:36 3rd Qu.:43.35
## Max. :36.98 Max. :100.00 Max. :36 Max. :49.64
## CW
## Min. :2286
## 1st Qu.:2787
## Median :3055
## Mean :3126
## 3rd Qu.:3457
## Max. :4566
Gestational age between 37 and 38 weeks
#Gestational age simulation with uniform distribution betwwen entre 37 et 38 weeks
set.seed(2345)
gen_terme_37 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 37, max = 38)) }
terme_37 <- 1:1 %>% map_df(gen_terme_37)
# Birth weigh simulation with truncated normal distribution (gestational age between 37 and 38 weeks)
set.seed(2345)
gen_poids_37 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=2211, b=3716, mean = 2940, sd = 720)) }
poids_37 <- 1:1 %>% map_df(gen_poids_37)
# creatinine simulation with truncated normal distribution (gestational age between 37 and 38 weeks)
set.seed(2345)
gen_creat_37 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_37 <- 1:1 %>% map_df(gen_creat_37)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 37 and 38 weeks)
set.seed(2345)
gen_infection_37 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_37 <- 1:1 %>% map_df(gen_infection_36)
# weight gain simulation with truncated normal distribution (gestational age between 37 and 38 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf37 <- poids_37 %>% bind_cols(creat_37) %>% bind_cols(infection_37)%>%bind_cols(terme_37)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 37)
summary(data_demog_inf37)
## PN CREA infection GA
## Min. :2273 Min. : 40.03 Min. : 3.031 Min. :37.01
## 1st Qu.:2666 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:37.23
## Median :2954 Median : 70.70 Median :27.495 Median :37.50
## Mean :2959 Mean : 77.25 Mean :33.466 Mean :37.47
## 3rd Qu.:3253 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:37.68
## Max. :3690 Max. :144.55 Max. :89.937 Max. :37.98
## ID weeks
## Min. : 1.00 Min. :37
## 1st Qu.: 25.75 1st Qu.:37
## Median : 50.50 Median :37
## Mean : 50.50 Mean :37
## 3rd Qu.: 75.25 3rd Qu.:37
## Max. :100.00 Max. :37
data_demog_inf37<- data_demog_inf37%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf37_inf15 <- data_demog_inf37%>% filter(periode == "inf15")
data_demog_inf37_sup15 <- data_demog_inf37%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_37_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_37_inf15 <- 1:1 %>% map_df(gen_gain_37_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_37_inf15 <- as_tibble(gain_37_inf15)
gain_37_inf15_bis <- gain_37_inf15 %>% bind_cols(data_demog_inf37_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_37_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_37_sup15 <- 1:1 %>% map_df(gen_gain_37_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_37_sup15 <- as_tibble(gain_37_sup15)
gain_37_sup15_bis <- gain_37_sup15 %>% bind_cols(data_demog_inf37_sup15)%>% dplyr::select(-ID6)
data_demog_inf37_final <-gain_37_inf15_bis%>% bind_rows(gain_37_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf37_final <- data_demog_inf37_final %>% mutate (PMA = GA+infection/7)%>% mutate(CW= PN+gain*infection)
summary(data_demog_inf37_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :2273 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:2666 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :2954 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :2959 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:3253 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :3690 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :37.01 Min. : 1.00 Min. :37 inf15:20 Min. :37.45
## 1st Qu.:37.23 1st Qu.: 25.75 1st Qu.:37 sup15:80 1st Qu.:39.79
## Median :37.50 Median : 50.50 Median :37 Median :41.43
## Mean :37.47 Mean : 50.50 Mean :37 Mean :42.25
## 3rd Qu.:37.68 3rd Qu.: 75.25 3rd Qu.:37 3rd Qu.:44.35
## Max. :37.98 Max. :100.00 Max. :37 Max. :50.64
## CW
## Min. :2313
## 1st Qu.:3050
## Median :3433
## Mean :3438
## 3rd Qu.:3749
## Max. :4816
Gestational age between 38 and 39 weeks
#Gestational age simulation with uniform distribution betwwen entre 38 et 39 weeks
set.seed(2345)
gen_terme_38 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 38, max = 39)) }
terme_38 <- 1:1 %>% map_df(gen_terme_38)
# Birth weigh simulation with truncated normal distribution (gestational age between 38 et 39 weeks)
set.seed(2345)
gen_poids_38 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=2389, b=3876, mean =3100, sd = 590)) }
poids_38 <- 1:1 %>% map_df(gen_poids_38)
# creatinine simulation with truncated normal distribution (gestational age between 38 and 39 weeks)
set.seed(2345)
gen_creat_38 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_38 <- 1:1 %>% map_df(gen_creat_38)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 38 and 39 weeks)
set.seed(2345)
gen_infection_38 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_38 <- 1:1 %>% map_df(gen_infection_38)
# weight gain simulation with truncated normal distribution (gestational age between 38 and 39 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf38 <- poids_38 %>% bind_cols(creat_38) %>% bind_cols(infection_38)%>%bind_cols(terme_38)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 38)
summary(data_demog_inf38)
## PN CREA infection GA
## Min. :2450 Min. : 40.03 Min. : 3.031 Min. :38.01
## 1st Qu.:2821 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:38.23
## Median :3100 Median : 70.70 Median :27.495 Median :38.50
## Mean :3100 Mean : 77.25 Mean :33.466 Mean :38.47
## 3rd Qu.:3302 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:38.68
## Max. :3850 Max. :144.55 Max. :89.937 Max. :38.98
## ID weeks
## Min. : 1.00 Min. :38
## 1st Qu.: 25.75 1st Qu.:38
## Median : 50.50 Median :38
## Mean : 50.50 Mean :38
## 3rd Qu.: 75.25 3rd Qu.:38
## Max. :100.00 Max. :38
data_demog_inf38<- data_demog_inf38%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf38_inf15 <- data_demog_inf38%>% filter(periode == "inf15")
data_demog_inf38_sup15 <- data_demog_inf38%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_38_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_38_inf15 <- 1:1 %>% map_df(gen_gain_38_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_38_inf15 <- as_tibble(gain_38_inf15)
gain_38_inf15_bis <- gain_38_inf15 %>% bind_cols(data_demog_inf38_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_38_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_38_sup15 <- 1:1 %>% map_df(gen_gain_38_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_38_sup15 <- as_tibble(gain_38_sup15)
gain_38_sup15_bis <- gain_38_sup15 %>% bind_cols(data_demog_inf38_sup15)%>% dplyr::select(-ID6)
data_demog_inf38_final <-gain_38_inf15_bis%>% bind_rows(gain_38_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf38_final <- data_demog_inf38_final %>% mutate (PMA = GA+infection/7)%>% mutate(CW= PN+gain*infection)
summary(data_demog_inf38_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :2450 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:2821 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :3100 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :3100 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:3302 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :3850 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :38.01 Min. : 1.00 Min. :38 inf15:20 Min. :38.45
## 1st Qu.:38.23 1st Qu.: 25.75 1st Qu.:38 sup15:80 1st Qu.:40.79
## Median :38.50 Median : 50.50 Median :38 Median :42.43
## Mean :38.47 Mean : 50.50 Mean :38 Mean :43.25
## 3rd Qu.:38.68 3rd Qu.: 75.25 3rd Qu.:38 3rd Qu.:45.35
## Max. :38.98 Max. :100.00 Max. :38 Max. :51.64
## CW
## Min. :2620
## 1st Qu.:3267
## Median :3594
## Mean :3579
## 3rd Qu.:3848
## Max. :4809
Gestational age between 39 and 40 weeks
#Gestational age simulation with uniform distribution betwwen entre 39 and 40 weeks
set.seed(2345)
gen_terme_39 <- function (x) {tibble(ID1 = x, GA = runif(100, min = 39, max = 40)) }
terme_39 <- 1:1 %>% map_df(gen_terme_39)
# Birth weigh simulation with truncated normal distribution (gestational age between 39 and 40 weeks )
set.seed(2345)
gen_poids_39 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=2565, b=4034, mean =3400, sd = 610)) }
poids_39 <- 1:1 %>% map_df(gen_poids_39)
# creatinine simulation with truncated normal distribution (gestational age between 39 and 40 weeks )
set.seed(2345)
gen_creat_39 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_39 <- 1:1 %>% map_df(gen_creat_39)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 39 and 40 weeks )
set.seed(2345)
gen_infection_39 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_39 <- 1:1 %>% map_df(gen_infection_39)
# weight gain simulation with truncated normal distribution (gestational age between 39 and 40 weeks )
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf39 <- poids_39 %>% bind_cols(creat_39) %>% bind_cols(infection_39)%>%bind_cols(terme_39)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 39)
summary(data_demog_inf39)
## PN CREA infection GA
## Min. :2684 Min. : 40.03 Min. : 3.031 Min. :39.01
## 1st Qu.:3036 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:39.23
## Median :3305 Median : 70.70 Median :27.495 Median :39.50
## Mean :3318 Mean : 77.25 Mean :33.466 Mean :39.47
## 3rd Qu.:3615 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:39.68
## Max. :4009 Max. :144.55 Max. :89.937 Max. :39.98
## ID weeks
## Min. : 1.00 Min. :39
## 1st Qu.: 25.75 1st Qu.:39
## Median : 50.50 Median :39
## Mean : 50.50 Mean :39
## 3rd Qu.: 75.25 3rd Qu.:39
## Max. :100.00 Max. :39
data_demog_inf39<- data_demog_inf38%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf39_inf15 <- data_demog_inf39%>% filter(periode == "inf15")
data_demog_inf39_sup15 <- data_demog_inf39%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_39_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_39_inf15 <- 1:1 %>% map_df(gen_gain_39_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_39_inf15 <- as_tibble(gain_39_inf15)
gain_39_inf15_bis <- gain_39_inf15 %>% bind_cols(data_demog_inf39_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_39_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_39_sup15 <- 1:1 %>% map_df(gen_gain_39_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_39_sup15 <- as_tibble(gain_39_sup15)
gain_39_sup15_bis <- gain_39_sup15 %>% bind_cols(data_demog_inf39_sup15)%>% dplyr::select(-ID6)
data_demog_inf39_final <-gain_39_inf15_bis%>% bind_rows(gain_39_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf39_final <- data_demog_inf39_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf39_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :2450 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:2821 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :3100 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :3100 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:3302 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :3850 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :38.01 Min. : 1.00 Min. :38 inf15:20 Min. :38.45
## 1st Qu.:38.23 1st Qu.: 25.75 1st Qu.:38 sup15:80 1st Qu.:40.79
## Median :38.50 Median : 50.50 Median :38 Median :42.43
## Mean :38.47 Mean : 50.50 Mean :38 Mean :43.25
## 3rd Qu.:38.68 3rd Qu.: 75.25 3rd Qu.:38 3rd Qu.:45.35
## Max. :38.98 Max. :100.00 Max. :38 Max. :51.64
## CW
## Min. :2620
## 1st Qu.:3267
## Median :3594
## Mean :3579
## 3rd Qu.:3848
## Max. :4809
Gestational age between 40 and 41 weeks
#Gestational age simulation with uniform distribution betwwen entre 40 et 41 weeks
set.seed(2345)
gen_terme_40<- function (x) {tibble(ID1 = x, GA = runif(100, min = 40, max = 41)) }
terme_40 <- 1:1 %>% map_df(gen_terme_40)
# Birth weigh simulation with truncated normal distribution (gestational age between 40 and 41 weeks)
set.seed(2345)
gen_poids_40 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=2702, b=4181, mean =3450, sd = 580)) }
poids_40 <- 1:1 %>% map_df(gen_poids_40)
# creatinine simulation with truncated normal distribution (gestational age between 40 and 41 weeks)
set.seed(2345)
gen_creat_40 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_40 <- 1:1 %>% map_df(gen_creat_40)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 40 and 41 weeks)
set.seed(2345)
gen_infection_40 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_40 <- 1:1 %>% map_df(gen_infection_40)
# weight gain simulation with truncated normal distribution (gestational age between 40 and 41 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf40<- poids_40 %>% bind_cols(creat_40) %>% bind_cols(infection_40)%>%bind_cols(terme_40)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 40)
summary(data_demog_inf40)
## PN CREA infection GA
## Min. :2763 Min. : 40.03 Min. : 3.031 Min. :40.01
## 1st Qu.:3147 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:40.23
## Median :3423 Median : 70.70 Median :27.495 Median :40.50
## Mean :3420 Mean : 77.25 Mean :33.466 Mean :40.47
## 3rd Qu.:3624 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:40.68
## Max. :4155 Max. :144.55 Max. :89.937 Max. :40.98
## ID weeks
## Min. : 1.00 Min. :40
## 1st Qu.: 25.75 1st Qu.:40
## Median : 50.50 Median :40
## Mean : 50.50 Mean :40
## 3rd Qu.: 75.25 3rd Qu.:40
## Max. :100.00 Max. :40
data_demog_inf40<- data_demog_inf40%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf40_inf15 <- data_demog_inf40%>% filter(periode == "inf15")
data_demog_inf40_sup15 <- data_demog_inf40%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_40_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_40_inf15 <- 1:1 %>% map_df(gen_gain_40_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_40_inf15 <- as_tibble(gain_40_inf15)
gain_40_inf15_bis <- gain_40_inf15 %>% bind_cols(data_demog_inf40_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_40_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_40_sup15 <- 1:1 %>% map_df(gen_gain_40_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_40_sup15 <- as_tibble(gain_40_sup15)
gain_40_sup15_bis <- gain_40_sup15 %>% bind_cols(data_demog_inf40_sup15)%>% dplyr::select(-ID6)
data_demog_inf40_final <-gain_40_inf15_bis%>% bind_rows(gain_40_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf40_final <- data_demog_inf40_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf40_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :2763 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:3147 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :3423 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :3420 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:3624 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :4155 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :40.01 Min. : 1.00 Min. :40 inf15:20 Min. :40.45
## 1st Qu.:40.23 1st Qu.: 25.75 1st Qu.:40 sup15:80 1st Qu.:42.79
## Median :40.50 Median : 50.50 Median :40 Median :44.43
## Mean :40.47 Mean : 50.50 Mean :40 Mean :45.25
## 3rd Qu.:40.68 3rd Qu.: 75.25 3rd Qu.:40 3rd Qu.:47.35
## Max. :40.98 Max. :100.00 Max. :40 Max. :53.64
## CW
## Min. :2981
## 1st Qu.:3553
## Median :3837
## Mean :3899
## 3rd Qu.:4271
## Max. :5414
Gestational age between 41 and 42 weeks
#Gestational age simulation with uniform distribution betwwen entre 41 et 42 weeks
set.seed(2345)
gen_terme_41<- function (x) {tibble(ID1 = x, GA = runif(100, min = 41, max = 42)) }
terme_41 <- 1:1 %>% map_df(gen_terme_41)
# Birth weigh simulation with truncated normal distribution (gestational age between 41 and 42 weeks)
set.seed(2345)
gen_poids_41 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=2779, b=4311, mean =3650, sd = 400)) }
poids_41 <- 1:1 %>% map_df(gen_poids_41)
# creatinine simulation with truncated normal distribution (gestational age between 41 and 42 weeks)
set.seed(2345)
gen_creat_41 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_41 <- 1:1 %>% map_df(gen_creat_41)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 41 and 42 weeks)
set.seed(2345)
gen_infection_41 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_41 <- 1:1 %>% map_df(gen_infection_41)
# weight gain simulation with truncated normal distribution (gestational age between 41 and 42 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf41 <- poids_41 %>% bind_cols(creat_41) %>% bind_cols(infection_41)%>%bind_cols(terme_41)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 41)
summary(data_demog_inf41)
## PN CREA infection GA
## Min. :2956 Min. : 40.03 Min. : 3.031 Min. :41.01
## 1st Qu.:3343 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:41.23
## Median :3610 Median : 70.70 Median :27.495 Median :41.50
## Mean :3599 Mean : 77.25 Mean :33.466 Mean :41.47
## 3rd Qu.:3764 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:41.68
## Max. :4222 Max. :144.55 Max. :89.937 Max. :41.98
## ID weeks
## Min. : 1.00 Min. :41
## 1st Qu.: 25.75 1st Qu.:41
## Median : 50.50 Median :41
## Mean : 50.50 Mean :41
## 3rd Qu.: 75.25 3rd Qu.:41
## Max. :100.00 Max. :41
data_demog_inf41<- data_demog_inf41%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf41_inf15 <- data_demog_inf41%>% filter(periode == "inf15")
data_demog_inf41_sup15 <- data_demog_inf41%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_41_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_41_inf15 <- 1:1 %>% map_df(gen_gain_41_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_41_inf15 <- as_tibble(gain_41_inf15)
gain_41_inf15_bis <- gain_41_inf15 %>% bind_cols(data_demog_inf41_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_41_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_41_sup15 <- 1:1 %>% map_df(gen_gain_41_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_41_sup15 <- as_tibble(gain_41_sup15)
gain_41_sup15_bis <- gain_41_sup15 %>% bind_cols(data_demog_inf41_sup15)%>% dplyr::select(-ID6)
data_demog_inf41_final <-gain_41_inf15_bis%>% bind_rows(gain_41_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf41_final <- data_demog_inf41_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf41_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :2956 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:3343 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :3610 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :3599 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:3764 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :4222 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :41.01 Min. : 1.00 Min. :41 inf15:20 Min. :41.45
## 1st Qu.:41.23 1st Qu.: 25.75 1st Qu.:41 sup15:80 1st Qu.:43.79
## Median :41.50 Median : 50.50 Median :41 Median :45.43
## Mean :41.47 Mean : 50.50 Mean :41 Mean :46.25
## 3rd Qu.:41.68 3rd Qu.: 75.25 3rd Qu.:41 3rd Qu.:48.35
## Max. :41.98 Max. :100.00 Max. :41 Max. :54.64
## CW
## Min. :3010
## 1st Qu.:3767
## Median :4069
## Mean :4078
## 3rd Qu.:4364
## Max. :5469
Gestational age between 42 and 43 weeks
#Gestational age simulation with uniform distribution betwwen entre 42 et 43 weeks
set.seed(2345)
gen_terme_42<- function (x) {tibble(ID1 = x, GA = runif(100, min = 42, max = 43)) }
terme_42 <- 1:1 %>% map_df(gen_terme_42)
# Birth weigh simulation with truncated normal distribution (gestational age between 42 and 43 weeks)
set.seed(2345)
gen_poids_42 <- function (x) {tibble(ID2 = x, PN = rtruncnorm(100, a=2862, b=4500, mean =3740, sd = 560)) }
poids_42 <- 1:1 %>% map_df(gen_poids_42)
# creatinine simulation with truncated normal distribution (gestational age between 42 and 43 weeks)
set.seed(2345)
gen_creat_42 <- function (x) {tibble(ID3 = x, CREA = rtruncnorm(100, a=40, b=170, mean = 60, sd = 40)) }
creat_42 <- 1:1 %>% map_df(gen_creat_42)
# days of infection = post natal age (PNA) simulation with truncated normal distribution (gestational age between 42 and 43 weeks)
set.seed(2345)
gen_infection_42 <- function (x) {tibble(ID4 = x, infection = rtruncnorm(100, a=3, b=90, mean = 18, sd = 35)) }
infection_42 <- 1:1 %>% map_df(gen_infection_42)
# weight gain simulation with truncated normal distribution (gestational age between 42 and 43 weeks)
# 2 periods for weight gain :if PNA is under 15 days or if PNA is after 15 days
data_demog_inf42 <- poids_42 %>% bind_cols(creat_42) %>% bind_cols(infection_42)%>%bind_cols(terme_42)%>% dplyr::select(-ID1,-ID2,-ID3,-ID4) %>%mutate(ID = 1:100) %>% mutate(weeks = 42)
summary(data_demog_inf42)
## PN CREA infection GA
## Min. :2887 Min. : 40.03 Min. : 3.031 Min. :42.01
## 1st Qu.:3374 1st Qu.: 57.46 1st Qu.:17.458 1st Qu.:42.23
## Median :3698 Median : 70.70 Median :27.495 Median :42.50
## Mean :3687 Mean : 77.25 Mean :33.466 Mean :42.47
## 3rd Qu.:3902 3rd Qu.: 95.34 3rd Qu.:48.921 3rd Qu.:42.68
## Max. :4495 Max. :144.55 Max. :89.937 Max. :42.98
## ID weeks
## Min. : 1.00 Min. :42
## 1st Qu.: 25.75 1st Qu.:42
## Median : 50.50 Median :42
## Mean : 50.50 Mean :42
## 3rd Qu.: 75.25 3rd Qu.:42
## Max. :100.00 Max. :42
data_demog_inf42<- data_demog_inf41%>% mutate (periode = factor(case_when(infection <= 15 ~ "inf15",
between(infection,15.01,90)~"sup15")))
data_demog_inf42_inf15 <- data_demog_inf42%>% filter(periode == "inf15")
data_demog_inf42_sup15 <- data_demog_inf42%>% filter(periode == "sup15")
set.seed(2345)
gen_gain_42_inf15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(20, a=0, b=30, mean = 6.8, sd = 4.8)) }
gain_42_inf15 <- 1:1 %>% map_df(gen_gain_42_inf15) %>% mutate(ID6 =(1:20))%>% dplyr::select(-ID5)
gain_42_inf15 <- as_tibble(gain_42_inf15)
gain_42_inf15_bis <- gain_42_inf15 %>% bind_cols(data_demog_inf42_inf15)%>% dplyr::select(-ID6)
set.seed(2345)
gen_gain_42_sup15 <- function (x) {tibble(ID5 = x, gain = rtruncnorm(80, a=5, b=20, mean = 14.9, sd = 2.8)) }
gain_42_sup15 <- 1:1 %>% map_df(gen_gain_42_sup15)%>% mutate(ID6 =(21:100))%>% dplyr::select(-ID5)
gain_42_sup15 <- as_tibble(gain_42_sup15)
gain_42_sup15_bis <- gain_42_sup15 %>% bind_cols(data_demog_inf42_sup15)%>% dplyr::select(-ID6)
data_demog_inf42_final <-gain_42_inf15_bis%>% bind_rows(gain_42_sup15_bis)%>% mutate (ID=1:100)
### calculation PMA = GA + PNA and CW = Birth Weight + gain weight*PNA
data_demog_inf42_final <- data_demog_inf42_final %>% mutate (PMA = GA+infection/7) %>% mutate(CW= PN+gain*infection)
summary(data_demog_inf42_final)
## gain PN CREA infection
## Min. : 0.08977 Min. :2956 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.55891 1st Qu.:3343 1st Qu.: 57.46 1st Qu.:17.458
## Median :14.02559 Median :3610 Median : 70.70 Median :27.495
## Mean :13.15874 Mean :3599 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.65722 3rd Qu.:3764 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.64722 Max. :4222 Max. :144.55 Max. :89.937
## GA ID weeks periode PMA
## Min. :41.01 Min. : 1.00 Min. :41 inf15:20 Min. :41.45
## 1st Qu.:41.23 1st Qu.: 25.75 1st Qu.:41 sup15:80 1st Qu.:43.79
## Median :41.50 Median : 50.50 Median :41 Median :45.43
## Mean :41.47 Mean : 50.50 Mean :41 Mean :46.25
## 3rd Qu.:41.68 3rd Qu.: 75.25 3rd Qu.:41 3rd Qu.:48.35
## Max. :41.98 Max. :100.00 Max. :41 Max. :54.64
## CW
## Min. :3010
## 1st Qu.:3767
## Median :4069
## Mean :4078
## 3rd Qu.:4364
## Max. :5469
creation final file
data_demogfinal <- data_demog_inf24_final %>% bind_rows(data_demog_inf25_final)%>% bind_rows(data_demog_inf26_final) %>% bind_rows(data_demog_inf27_final)%>% bind_rows(data_demog_inf28_final)%>% bind_rows(data_demog_inf29_final)%>% bind_rows(data_demog_inf30_final)%>% bind_rows(data_demog_inf31_final)%>% bind_rows(data_demog_inf32_final)%>% bind_rows(data_demog_inf33_final)%>% bind_rows(data_demog_inf34_final)%>% bind_rows(data_demog_inf35_final)%>% bind_rows(data_demog_inf36_final)%>% bind_rows(data_demog_inf37_final)%>% bind_rows(data_demog_inf38_final)%>% bind_rows(data_demog_inf39_final)%>% bind_rows(data_demog_inf40_final)%>% bind_rows(data_demog_inf41_final)%>% bind_rows(data_demog_inf42_final)%>% select(-ID)%>% mutate(ID=1:1900)
summary(data_demogfinal)
## gain PN CREA infection
## Min. : 0.004318 Min. : 496.1 Min. : 40.03 Min. : 3.031
## 1st Qu.:11.558906 1st Qu.:1068.3 1st Qu.: 57.46 1st Qu.:17.458
## Median :13.783885 Median :1896.4 Median : 70.70 Median :27.495
## Mean :12.902280 Mean :2038.0 Mean : 77.25 Mean :33.466
## 3rd Qu.:15.543687 3rd Qu.:2958.2 3rd Qu.: 95.34 3rd Qu.:48.921
## Max. :19.647220 Max. :4221.7 Max. :144.55 Max. :89.937
## GA weeks periode PMA CW
## Min. :24.01 Min. :24.00 inf15: 380 Min. :24.45 Min. : 583.9
## 1st Qu.:28.70 1st Qu.:28.00 sup15:1520 1st Qu.:33.31 1st Qu.:1561.6
## Median :33.50 Median :33.00 Median :38.26 Median :2392.5
## Mean :33.34 Mean :32.89 Mean :38.12 Mean :2508.4
## 3rd Qu.:38.12 3rd Qu.:38.00 3rd Qu.:42.74 3rd Qu.:3429.1
## Max. :41.98 Max. :41.00 Max. :54.64 Max. :5468.6
## ID
## Min. : 1.0
## 1st Qu.: 475.8
## Median : 950.5
## Mean : 950.5
## 3rd Qu.:1425.2
## Max. :1900.0