PIM3 missing value
Yuko Shiima
2024-12-23
- Set up
パッケージをアクティベート
knitr::opts_chunk$set(echo = TRUE, comment = "")
library(dplyr)
library(psych)
library(readr)
library(lubridate)
library(summarytools)
library(tidyverse)
library(here)
library(naniar)
library(mice)- Import
RDataをインポート。
load("hyogo_defined.RData")hyogo_defined_RData の中には、df：KCHとamagasaki全てを統合したものが入っている
dfを下記のように分類
df_2019
兵庫こどもと尼崎合わせて、2019年1月1日から2023年12月31日までで16歳未満でPICUのみ
df_amagasaki 上記で尼崎のみ
df_kch 上記で兵庫こども
#2019年1月1日〜2023年12月31日
df_2019 <- df %>%
filter((date_icu_adm >= as.Date("2019-01-01") &
date_icu_adm <= as.Date("2023-12-31")) &
age_adm_y <16 &
unit_dic == "PICU")
#尼崎のみ
df_amagasaki <- df_2019 %>%
filter(Amagasaki_DB == "Amagasaki")
#兵庫こどものみ
df_kch <- df_2019 %>%
filter(Amagasaki_DB == "KCH")・PIM3（欠損値141）
df_2019 %>%
group_by(Amagasaki_DB) %>%
summarize(
mean_PIM3 = mean(PIM3_death_rate, na.rm = TRUE), # 欠損値を除く平均
count_total = n(), # 全体のデータ数
count_NA = sum(is.na(PIM3_death_rate)) # 欠損値の数
)# A tibble: 2 × 4
Amagasaki_DB mean_PIM3 count_total count_NA
<fct> <dbl> <int> <int>
1 Amagasaki 3.40 1272 143
2 KCH 2.80 2830 0・PIM3の欠損値年度毎評価
df_2019 %>%
group_by(adm_year, Amagasaki_DB) %>%
summarize(
mean_PIM3 = mean(PIM3_death_rate, na.rm = TRUE), # 欠損値を除く平均
count_total = n(), # 全体のデータ数
count_NA = sum(is.na(PIM3_death_rate)) # 欠損値の数
) %>%
arrange(adm_year, Amagasaki_DB) # adm_yearとAmagasaki_DBで並べ替え`summarise()` has grouped output by 'adm_year'. You can override using the
`.groups` argument.# A tibble: 10 × 5
# Groups: adm_year [5]
adm_year Amagasaki_DB mean_PIM3 count_total count_NA
<dbl> <fct> <dbl> <int> <int>
1 2019 Amagasaki 2.43 195 0
2 2019 KCH 2.17 612 0
3 2020 Amagasaki 2.85 287 10
4 2020 KCH 2.91 536 0
5 2021 Amagasaki 2.33 250 17
6 2021 KCH 2.27 565 0
7 2022 Amagasaki 3.76 271 46
8 2022 KCH 3.44 499 0
9 2023 Amagasaki 5.97 269 70
10 2023 KCH 3.28 618 0# PIM3の欠損値パターンを視覚化
# PIM3の変数を指定してデータを抽出
PIM3_variables <- c("PIM_cardiac", "PIM_CPB", "PIM_hr",
"PIM_lr", "PIM_MV", "PIM_pupils", "PIM_sBP",
"PIM_vhr", "BE", "FIO2_ped", "PaO2_ped", "PIM2_recovery")
# 指定した変数の欠損割合を棒グラフで可視化
gg_miss_var(df_amagasaki[PIM3_variables])
# 欠損値の個数と割合を計算
missing_summary_PIM3 <- df_amagasaki %>%
summarise(across(all_of(PIM3_variables), list(
count_NA = ~sum(is.na(.)),
percent_NA = ~mean(is.na(.)) * 100
), .names = "{.col}_{.fn}"))
# 結果を表示
#missing_summary_PIM3
# 欠損パターンを確認
library(mice)
md.pattern(df_amagasaki[PIM3_variables], rotate.names = TRUE)
PIM_lr PIM_hr PIM2_recovery PIM_CPB PIM_vhr PIM_cardiac PIM_pupils PIM_MV
1062 1 1 1 1 1 1 1 1
45 1 1 1 1 1 1 1 1
3 1 1 1 1 1 1 1 1
2 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
29 1 1 1 1 1 1 1 1
6 1 1 1 1 1 1 1 0
4 1 1 1 1 1 1 1 0
1 1 1 1 1 1 1 1 0
1 1 1 1 1 1 1 1 0
8 1 1 1 1 1 1 1 0
1 1 1 1 1 1 1 0 0
1 1 1 1 1 1 1 0 0
1 1 1 1 1 1 1 0 0
1 1 1 1 1 1 0 0 0
1 1 1 1 1 1 0 0 0
34 1 1 1 1 0 1 1 1
2 1 1 1 1 0 1 1 1
2 1 1 1 1 0 1 1 0
1 1 1 1 0 1 0 0 0
2 1 1 0 0 1 0 0 0
35 1 1 0 0 1 0 0 0
20 1 0 1 1 1 1 1 1
1 1 0 1 1 1 1 1 0
5 0 1 1 1 1 1 1 1
1 0 1 1 1 1 1 1 0
6 21 37 38 38 40 43 67
FIO2_ped PaO2_ped BE PIM_sBP
1062 1 1 1 1 0
45 1 1 1 0 1
3 1 1 0 1 1
2 1 0 1 1 1
1 1 0 0 1 2
1 0 1 1 1 1
1 0 0 0 1 3
29 0 0 0 0 4
6 1 1 1 1 1
4 1 1 1 0 2
1 1 1 0 1 2
1 0 0 0 1 4
8 0 0 0 0 5
1 1 1 1 0 3
1 1 1 0 0 4
1 0 0 0 0 6
1 1 1 1 1 3
1 0 0 0 0 7
34 1 1 1 1 1
2 0 0 0 0 5
2 0 0 0 1 5
1 0 0 0 0 8
2 1 1 1 0 6
35 0 0 0 0 9
20 1 1 1 1 1
1 0 0 0 0 6
5 1 1 1 1 1
1 1 1 1 1 2
83 85 88 131 677library(knitr)
pattern <- md.pattern(df_amagasaki[PIM3_variables], plot = FALSE)
kable(pattern)| PIM_lr | PIM_hr | PIM2_recovery | PIM_CPB | PIM_vhr | PIM_cardiac | PIM_pupils | PIM_MV | FIO2_ped | PaO2_ped | BE | PIM_sBP | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1062 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
| 45 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 |
| 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 |
| 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 2 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 3 |
| 29 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 4 |
| 6 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 |
| 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 2 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 2 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 4 |
| 8 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 3 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 4 |
| 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
| 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 3 |
| 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
| 34 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 2 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 5 |
| 2 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 5 |
| 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
| 2 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 6 |
| 35 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
| 20 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 6 |
| 5 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 2 |
| 6 | 21 | 37 | 38 | 38 | 40 | 43 | 67 | 83 | 85 | 88 | 131 | 677 |