install.packages("tableone")
# install.packages("devtools")
devtools::install_github(repo = "kaz-yos/tableone", ref = "develop")46 tableone绘制三线表
临床研究中基线资料表必不可少，通常也是你文章中的第一张表！
一般我们会通过Word或者Excel进行绘制基线资料表，但是这种方法很麻烦，需要不停的复制粘贴。。。
今天介绍的这个tableone，就是专门为这个基线资料表而生的！和之前介绍过的comparegroups有点像哦~但是功能不止于基线资料表，还可以做非常多统计描述的工作！比如前面介绍倾向性评分匹配时就用到了这个包。
支持连续性变量及分类变量，支持自动标注P值，加权数据也是支持的，真的是一步到位了！
46.1 安装
两种安装方式任选一种即可：
R包安装有问题的小伙伴可以加我微信或者评论区留言~
46.2 使用
为了方便比较，还是使用compareGroups包自带的regicor数据集。
为了说明这个软件包是如何工作的，我们从REGICOR研究中取了一部分数据。REGICOR是一个 对来自西班牙东北部的参与者进行的横断面研究，包括：人口统计学信息（年龄、性别、身高、体重、腰围等）、血脂特征（总胆固醇和胆固醇、甘油三酯等）、问卷调查信息（体育活动，生活质量，…）等。此外，心血管事件和 死亡信息来自医院和官方登记处。
library(compareGroups)
library(tableone)
data("regicor")
dim(regicor)
## [1] 2294 25
str(regicor)
## 'data.frame': 2294 obs. of 25 variables:
## $ id : num 2.26e+03 1.88e+03 3.00e+09 3.00e+09 3.00e+09 ...
## ..- attr(*, "label")= Named chr "Individual id"
## .. ..- attr(*, "names")= chr "id"
## $ year : Factor w/ 3 levels "1995","2000",..: 3 3 2 2 2 2 2 1 3 1 ...
## ..- attr(*, "label")= Named chr "Recruitment year"
## .. ..- attr(*, "names")= chr "year"
## $ age : int 70 56 37 69 70 40 66 53 43 70 ...
## ..- attr(*, "label")= Named chr "Age"
## .. ..- attr(*, "names")= chr "age"
## $ sex : Factor w/ 2 levels "Male","Female": 2 2 1 2 2 2 1 2 2 1 ...
## ..- attr(*, "label")= chr "Sex"
## $ smoker : Factor w/ 3 levels "Never smoker",..: 1 1 2 1 NA 2 1 1 3 3 ...
## ..- attr(*, "label")= Named chr "Smoking status"
## .. ..- attr(*, "names")= chr "smoker"
## $ sbp : int 138 139 132 168 NA 108 120 132 95 142 ...
## ..- attr(*, "label")= Named chr "Systolic blood pressure"
## .. ..- attr(*, "names")= chr "sbp"
## $ dbp : int 75 89 82 97 NA 70 72 78 65 78 ...
## ..- attr(*, "label")= Named chr "Diastolic blood pressure"
## .. ..- attr(*, "names")= chr "dbp"
## $ histhtn : Factor w/ 2 levels "Yes","No": 2 2 2 2 2 2 1 2 2 2 ...
## ..- attr(*, "label")= Named chr "History of hypertension"
## .. ..- attr(*, "names")= chr "histbp"
## $ txhtn : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 2 1 1 1 ...
## ..- attr(*, "label")= chr "Hypertension treatment"
## $ chol : num 294 220 245 168 NA NA 298 254 194 188 ...
## ..- attr(*, "label")= Named chr "Total cholesterol"
## .. ..- attr(*, "names")= chr "chol"
## $ hdl : num 57 50 59.8 53.2 NA ...
## ..- attr(*, "label")= Named chr "HDL cholesterol"
## .. ..- attr(*, "names")= chr "hdl"
## $ triglyc : num 93 160 89 116 NA 94 71 NA 68 137 ...
## ..- attr(*, "label")= Named chr "Triglycerides"
## .. ..- attr(*, "names")= chr "triglyc"
## $ ldl : num 218.4 138 167.4 91.6 NA ...
## ..- attr(*, "label")= Named chr "LDL cholesterol"
## .. ..- attr(*, "names")= chr "ldl"
## $ histchol: Factor w/ 2 levels "Yes","No": 2 2 2 2 NA 2 1 2 2 2 ...
## ..- attr(*, "label")= chr "History of hyperchol."
## $ txchol : Factor w/ 2 levels "No","Yes": 1 1 1 1 NA 1 1 1 1 1 ...
## ..- attr(*, "label")= Named chr "Cholesterol treatment"
## .. ..- attr(*, "names")= chr "txchol"
## $ height : num 160 163 170 147 NA ...
## ..- attr(*, "label")= Named chr "Height (cm)"
## .. ..- attr(*, "names")= chr "height"
## $ weight : num 64 67 70 68 NA 43.5 79.2 45.8 53 62 ...
## ..- attr(*, "label")= Named chr "Weight (Kg)"
## .. ..- attr(*, "names")= chr "weight"
## $ bmi : num 25 25.2 24.2 31.5 NA ...
## ..- attr(*, "label")= Named chr "Body mass index"
## .. ..- attr(*, "names")= chr "bmi"
## $ phyact : num 304 160 553 522 NA ...
## ..- attr(*, "label")= Named chr "Physical activity (Kcal/week)"
## .. ..- attr(*, "names")= chr "phyact"
## $ pcs : num 54.5 58.2 43.4 54.3 NA ...
## ..- attr(*, "label")= Named chr "Physical component"
## .. ..- attr(*, "names")= chr "pcs"
## $ mcs : num 58.9 48 62.6 57.9 NA ...
## ..- attr(*, "label")= chr "Mental component"
## $ cv : Factor w/ 2 levels "No","Yes": 1 1 1 1 NA 1 1 1 1 1 ...
## ..- attr(*, "label")= chr "Cardiovascular event"
## $ tocv : num 1025 2757 1906 1055 NA ...
## ..- attr(*, "label")= chr "Days to cardiovascular event or end of follow-up"
## $ death : Factor w/ 2 levels "No","Yes": 2 1 1 1 NA 1 2 1 1 1 ...
## ..- attr(*, "label")= chr "Overall death"
## $ todeath : num 1299.2 39.3 858.4 1833.1 NA ...
## ..- attr(*, "label")= chr "Days to overall death or end of follow-up"各个变量的信息如下：
| Name | Label | Codes |
|---|---|---|
| id | Individual id | |
| year | Recruitment year | 1995; 2000; 2005 |
| age | Age | |
| sex | Sex | Male; Female |
| smoker | Smoking status | Never smoker; Current or former < 1y; Former $\geq$ 1y |
| sbp | Systolic blood pressure | |
| dbp | Diastolic blood pressure | |
| histhtn | History of hypertension | Yes; No |
| txhtn | Hypertension treatment | No; Yes |
| chol | Total cholesterol | |
| hdl | HDL cholesterol | |
| triglyc | Triglycerides | |
| ldl | LDL cholesterol | |
| histchol | History of hyperchol. | Yes; No |
| txchol | Cholesterol treatment | No; Yes |
| height | Height (cm) | |
| weight | Weight (Kg) | |
| bmi | Body mass index | |
| phyact | Physical activity (Kcal/week) | |
| pcs | Physical component | |
| mcs | Mental component | |
| cv | Cardiovascular event | No; Yes |
| tocv | Days to cardiovascular event or end of follow-up | |
| death | Overall death | No; Yes |
| todeath | Days to overall death or end of follow-up |
为了防止这个数据集再次消失，把它保存到本地。。。
save(regicor,file = "./datasets/regicor.rdata")46.2.1 基本描述功能
首先是基本的统计描述功能，使用CreateTableOne()函数可以给出数据的基本情况：
tab <- CreateTableOne(data = regicor)
tab
##
## Overall
## n 2294
## id (mean (SD)) 1215817623.64 (1339538685.65)
## year (%)
## 1995 431 (18.8)
## 2000 786 (34.3)
## 2005 1077 (46.9)
## age (mean (SD)) 54.74 (11.05)
## sex = Female (%) 1193 (52.0)
## smoker (%)
## Never smoker 1201 (53.8)
## Current or former < 1y 593 (26.6)
## Former >= 1y 439 (19.7)
## sbp (mean (SD)) 131.17 (20.31)
## dbp (mean (SD)) 79.66 (10.55)
## histhtn = No (%) 1563 (68.4)
## txhtn = Yes (%) 428 (19.0)
## chol (mean (SD)) 218.76 (45.25)
## hdl (mean (SD)) 52.69 (14.75)
## triglyc (mean (SD)) 115.58 (73.94)
## ldl (mean (SD)) 143.25 (39.69)
## histchol = No (%) 1564 (68.8)
## txchol = Yes (%) 228 (10.2)
## height (mean (SD)) 162.92 (9.22)
## weight (mean (SD)) 73.44 (13.68)
## bmi (mean (SD)) 27.64 (4.56)
## phyact (mean (SD)) 398.83 (388.16)
## pcs (mean (SD)) 49.62 (9.01)
## mcs (mean (SD)) 47.98 (10.98)
## cv = Yes (%) 92 ( 4.3)
## tocv (mean (SD)) 1754.67 (1080.62)
## death = Yes (%) 173 ( 8.1)
## todeath (mean (SD)) 1721.31 (1051.05)46.2.2 选择变量&指定变量类型
通过vars()函数指定保留哪些变量，通过factorVars()函数指定哪些是分类变量。
但其实regicor这个数据集已经把分类变量因子化了，因此这里不用factorVars()也是可以的。
CreateTableOne(data = regicor,
vars = c("age","sex","bmi","smoker","height","weight"),
factorVars = c("smoker","sex")
)
##
## Overall
## n 2294
## age (mean (SD)) 54.74 (11.05)
## sex = Female (%) 1193 (52.0)
## bmi (mean (SD)) 27.64 (4.56)
## smoker (%)
## Never smoker 1201 (53.8)
## Current or former < 1y 593 (26.6)
## Former >= 1y 439 (19.7)
## height (mean (SD)) 162.92 (9.22)
## weight (mean (SD)) 73.44 (13.68)46.2.3 显示所有水平
在第一个表中我们可以发现很多分类变量被折叠了，都只显示了yes或者no的一部分，比如sex/txhtn/cv等，我们在print()中添加showAllLevels = T显示所有分类！
print(tab, showAllLevels = T)
##
## level Overall
## n 2294
## id (mean (SD)) 1215817623.64 (1339538685.65)
## year (%) 1995 431 (18.8)
## 2000 786 (34.3)
## 2005 1077 (46.9)
## age (mean (SD)) 54.74 (11.05)
## sex (%) Male 1101 (48.0)
## Female 1193 (52.0)
## smoker (%) Never smoker 1201 (53.8)
## Current or former < 1y 593 (26.6)
## Former >= 1y 439 (19.7)
## sbp (mean (SD)) 131.17 (20.31)
## dbp (mean (SD)) 79.66 (10.55)
## histhtn (%) Yes 723 (31.6)
## No 1563 (68.4)
## txhtn (%) No 1823 (81.0)
## Yes 428 (19.0)
## chol (mean (SD)) 218.76 (45.25)
## hdl (mean (SD)) 52.69 (14.75)
## triglyc (mean (SD)) 115.58 (73.94)
## ldl (mean (SD)) 143.25 (39.69)
## histchol (%) Yes 709 (31.2)
## No 1564 (68.8)
## txchol (%) No 2011 (89.8)
## Yes 228 (10.2)
## height (mean (SD)) 162.92 (9.22)
## weight (mean (SD)) 73.44 (13.68)
## bmi (mean (SD)) 27.64 (4.56)
## phyact (mean (SD)) 398.83 (388.16)
## pcs (mean (SD)) 49.62 (9.01)
## mcs (mean (SD)) 47.98 (10.98)
## cv (%) No 2071 (95.7)
## Yes 92 ( 4.3)
## tocv (mean (SD)) 1754.67 (1080.62)
## death (%) No 1975 (91.9)
## Yes 173 ( 8.1)
## todeath (mean (SD)) 1721.31 (1051.05)对于二分类变量，还可以使用cramVars参数达到类似的效果，但要注意，此时不同类别是显示在一行中的：
print(tab, cramVars = c("sex","histchol","txchol","cv","death"))
##
## Overall
## n 2294
## id (mean (SD)) 1215817623.64 (1339538685.65)
## year (%)
## 1995 431 (18.8)
## 2000 786 (34.3)
## 2005 1077 (46.9)
## age (mean (SD)) 54.74 (11.05)
## sex = Male/Female (%) 1101/1193 (48.0/52.0)
## smoker (%)
## Never smoker 1201 (53.8)
## Current or former < 1y 593 (26.6)
## Former >= 1y 439 (19.7)
## sbp (mean (SD)) 131.17 (20.31)
## dbp (mean (SD)) 79.66 (10.55)
## histhtn = No (%) 1563 (68.4)
## txhtn = Yes (%) 428 (19.0)
## chol (mean (SD)) 218.76 (45.25)
## hdl (mean (SD)) 52.69 (14.75)
## triglyc (mean (SD)) 115.58 (73.94)
## ldl (mean (SD)) 143.25 (39.69)
## histchol = Yes/No (%) 709/1564 (31.2/68.8)
## txchol = No/Yes (%) 2011/228 (89.8/10.2)
## height (mean (SD)) 162.92 (9.22)
## weight (mean (SD)) 73.44 (13.68)
## bmi (mean (SD)) 27.64 (4.56)
## phyact (mean (SD)) 398.83 (388.16)
## pcs (mean (SD)) 49.62 (9.01)
## mcs (mean (SD)) 47.98 (10.98)
## cv = No/Yes (%) 2071/92 (95.7/4.3)
## tocv (mean (SD)) 1754.67 (1080.62)
## death = No/Yes (%) 1975/173 (91.9/8.1)
## todeath (mean (SD)) 1721.31 (1051.05)46.2.4 非正态分布变量
对于正态分布的变量使用的是均值±标准差的方式进行展示，对于非正态变量则通过中位数（四分位距）表示。可以通过nonnormal参数指定。
print(tab,
showAllLevels = T,
nonnormal = c("todeath")
)
##
## level Overall
## n 2294
## id (mean (SD)) 1215817623.64 (1339538685.65)
## year (%) 1995 431 (18.8)
## 2000 786 (34.3)
## 2005 1077 (46.9)
## age (mean (SD)) 54.74 (11.05)
## sex (%) Male 1101 (48.0)
## Female 1193 (52.0)
## smoker (%) Never smoker 1201 (53.8)
## Current or former < 1y 593 (26.6)
## Former >= 1y 439 (19.7)
## sbp (mean (SD)) 131.17 (20.31)
## dbp (mean (SD)) 79.66 (10.55)
## histhtn (%) Yes 723 (31.6)
## No 1563 (68.4)
## txhtn (%) No 1823 (81.0)
## Yes 428 (19.0)
## chol (mean (SD)) 218.76 (45.25)
## hdl (mean (SD)) 52.69 (14.75)
## triglyc (mean (SD)) 115.58 (73.94)
## ldl (mean (SD)) 143.25 (39.69)
## histchol (%) Yes 709 (31.2)
## No 1564 (68.8)
## txchol (%) No 2011 (89.8)
## Yes 228 (10.2)
## height (mean (SD)) 162.92 (9.22)
## weight (mean (SD)) 73.44 (13.68)
## bmi (mean (SD)) 27.64 (4.56)
## phyact (mean (SD)) 398.83 (388.16)
## pcs (mean (SD)) 49.62 (9.01)
## mcs (mean (SD)) 47.98 (10.98)
## cv (%) No 2071 (95.7)
## Yes 92 ( 4.3)
## tocv (mean (SD)) 1754.67 (1080.62)
## death (%) No 1975 (91.9)
## Yes 173 ( 8.1)
## todeath (median [IQR]) 1668.40 [787.63, 2662.54]46.2.5 分层显示
但是在实际写论文的时候，经常需要分组显示，分别展示不同组间的统计资料，然后计算组间有没有差别！
可以通过strata参数实现，自动给出P值。
tab_s <- CreateTableOne(data = regicor,
vars = colnames(regicor)[-1], # id就不要了
strata = "year")
# 全部展开展示:
print(tab_s, showAllLevels = T)
## Stratified by year
## level 1995
## n 431
## year (%) 1995 431 (100.0)
## 2000 0 ( 0.0)
## 2005 0 ( 0.0)
## age (mean (SD)) 54.10 (11.72)
## sex (%) Male 206 ( 47.8)
## Female 225 ( 52.2)
## smoker (%) Never smoker 234 ( 56.4)
## Current or former < 1y 109 ( 26.3)
## Former >= 1y 72 ( 17.3)
## sbp (mean (SD)) 132.61 (19.17)
## dbp (mean (SD)) 77.04 (10.54)
## histhtn (%) Yes 111 ( 25.8)
## No 320 ( 74.2)
## txhtn (%) No 360 ( 83.5)
## Yes 71 ( 16.5)
## chol (mean (SD)) 225.32 (43.13)
## hdl (mean (SD)) 51.87 (14.46)
## triglyc (mean (SD)) 114.15 (74.37)
## ldl (mean (SD)) 151.73 (38.41)
## histchol (%) Yes 97 ( 22.5)
## No 334 ( 77.5)
## txchol (%) No 403 ( 93.5)
## Yes 28 ( 6.5)
## height (mean (SD)) 163.50 (9.21)
## weight (mean (SD)) 72.29 (12.61)
## bmi (mean (SD)) 27.02 (4.15)
## phyact (mean (SD)) 490.78 (419.04)
## pcs (mean (SD)) 49.33 (8.08)
## mcs (mean (SD)) 49.25 (11.35)
## cv (%) No 388 ( 97.5)
## Yes 10 ( 2.5)
## tocv (mean (SD)) 1783.62 (1101.17)
## death (%) No 369 ( 95.3)
## Yes 18 ( 4.7)
## todeath (mean (SD)) 1713.31 (1042.18)
## Stratified by year
## 2000 2005 p test
## n 786 1077
## year (%) 0 ( 0.0) 0 ( 0.0) <0.001
## 786 (100.0) 0 ( 0.0)
## 0 ( 0.0) 1077 (100.0)
## age (mean (SD)) 54.34 (11.22) 55.28 (10.63) 0.078
## sex (%) 390 ( 49.6) 505 ( 46.9) 0.506
## 396 ( 50.4) 572 ( 53.1)
## smoker (%) 414 ( 54.6) 553 ( 52.2) <0.001
## 267 ( 35.2) 217 ( 20.5)
## 77 ( 10.2) 290 ( 27.4)
## sbp (mean (SD)) 133.04 (21.31) 129.26 (19.85) <0.001
## dbp (mean (SD)) 80.80 (10.31) 79.88 (10.55) <0.001
## histhtn (%) 233 ( 29.6) 379 ( 35.5) <0.001
## 553 ( 70.4) 690 ( 64.5)
## txhtn (%) 659 ( 83.8) 804 ( 77.8) 0.002
## 127 ( 16.2) 230 ( 22.2)
## chol (mean (SD)) 223.67 (44.37) 213.03 (45.92) <0.001
## hdl (mean (SD)) 52.34 (15.60) 53.24 (14.23) 0.208
## triglyc (mean (SD)) 113.94 (70.69) 117.27 (76.01) 0.582
## ldl (mean (SD)) 149.03 (38.61) 136.32 (39.68) <0.001
## histchol (%) 256 ( 33.2) 356 ( 33.2) <0.001
## 515 ( 66.8) 715 ( 66.8)
## txchol (%) 705 ( 91.2) 903 ( 87.2) <0.001
## 68 ( 8.8) 132 ( 12.8)
## height (mean (SD)) 162.01 (9.39) 163.34 (9.05) 0.003
## weight (mean (SD)) 73.84 (13.95) 73.60 (13.87) 0.150
## bmi (mean (SD)) 28.10 (4.62) 27.56 (4.63) <0.001
## phyact (mean (SD)) 421.74 (377.13) 351.16 (378.05) <0.001
## pcs (mean (SD)) 49.01 (9.63) 50.14 (8.91) 0.032
## mcs (mean (SD)) 48.90 (10.95) 46.87 (10.75) <0.001
## cv (%) 706 ( 95.3) 977 ( 95.4) 0.161
## 35 ( 4.7) 47 ( 4.6)
## tocv (mean (SD)) 1685.62 (1079.72) 1793.38 (1071.81) 0.099
## death (%) 657 ( 89.0) 949 ( 92.8) <0.001
## 81 ( 11.0) 74 ( 7.2)
## todeath (mean (SD)) 1674.37 (1050.09) 1758.19 (1054.68) 0.252但是tableone并没有提供直接导出到Word的途径，只能导入到csv文件中，这是有点差劲的地方。
46.2.6 导出
tab_sv <- print(tab_s,showAllLevels = T,printToggle = F)
write.csv(tab_sv,file = "tab_sv.csv")46.3 不同包的比较
可以看到tableone做出一张表需要2行代码：
tab_s <- CreateTableOne(data = regicor,vars = colnames(regicor)[-1],
strata = "year")
print(tab_s,showAllLevels = T)
## Stratified by year
## level 1995
## n 431
## year (%) 1995 431 (100.0)
## 2000 0 ( 0.0)
## 2005 0 ( 0.0)
## age (mean (SD)) 54.10 (11.72)
## sex (%) Male 206 ( 47.8)
## Female 225 ( 52.2)
## smoker (%) Never smoker 234 ( 56.4)
## Current or former < 1y 109 ( 26.3)
## Former >= 1y 72 ( 17.3)
## sbp (mean (SD)) 132.61 (19.17)
## dbp (mean (SD)) 77.04 (10.54)
## histhtn (%) Yes 111 ( 25.8)
## No 320 ( 74.2)
## txhtn (%) No 360 ( 83.5)
## Yes 71 ( 16.5)
## chol (mean (SD)) 225.32 (43.13)
## hdl (mean (SD)) 51.87 (14.46)
## triglyc (mean (SD)) 114.15 (74.37)
## ldl (mean (SD)) 151.73 (38.41)
## histchol (%) Yes 97 ( 22.5)
## No 334 ( 77.5)
## txchol (%) No 403 ( 93.5)
## Yes 28 ( 6.5)
## height (mean (SD)) 163.50 (9.21)
## weight (mean (SD)) 72.29 (12.61)
## bmi (mean (SD)) 27.02 (4.15)
## phyact (mean (SD)) 490.78 (419.04)
## pcs (mean (SD)) 49.33 (8.08)
## mcs (mean (SD)) 49.25 (11.35)
## cv (%) No 388 ( 97.5)
## Yes 10 ( 2.5)
## tocv (mean (SD)) 1783.62 (1101.17)
## death (%) No 369 ( 95.3)
## Yes 18 ( 4.7)
## todeath (mean (SD)) 1713.31 (1042.18)
## Stratified by year
## 2000 2005 p test
## n 786 1077
## year (%) 0 ( 0.0) 0 ( 0.0) <0.001
## 786 (100.0) 0 ( 0.0)
## 0 ( 0.0) 1077 (100.0)
## age (mean (SD)) 54.34 (11.22) 55.28 (10.63) 0.078
## sex (%) 390 ( 49.6) 505 ( 46.9) 0.506
## 396 ( 50.4) 572 ( 53.1)
## smoker (%) 414 ( 54.6) 553 ( 52.2) <0.001
## 267 ( 35.2) 217 ( 20.5)
## 77 ( 10.2) 290 ( 27.4)
## sbp (mean (SD)) 133.04 (21.31) 129.26 (19.85) <0.001
## dbp (mean (SD)) 80.80 (10.31) 79.88 (10.55) <0.001
## histhtn (%) 233 ( 29.6) 379 ( 35.5) <0.001
## 553 ( 70.4) 690 ( 64.5)
## txhtn (%) 659 ( 83.8) 804 ( 77.8) 0.002
## 127 ( 16.2) 230 ( 22.2)
## chol (mean (SD)) 223.67 (44.37) 213.03 (45.92) <0.001
## hdl (mean (SD)) 52.34 (15.60) 53.24 (14.23) 0.208
## triglyc (mean (SD)) 113.94 (70.69) 117.27 (76.01) 0.582
## ldl (mean (SD)) 149.03 (38.61) 136.32 (39.68) <0.001
## histchol (%) 256 ( 33.2) 356 ( 33.2) <0.001
## 515 ( 66.8) 715 ( 66.8)
## txchol (%) 705 ( 91.2) 903 ( 87.2) <0.001
## 68 ( 8.8) 132 ( 12.8)
## height (mean (SD)) 162.01 (9.39) 163.34 (9.05) 0.003
## weight (mean (SD)) 73.84 (13.95) 73.60 (13.87) 0.150
## bmi (mean (SD)) 28.10 (4.62) 27.56 (4.63) <0.001
## phyact (mean (SD)) 421.74 (377.13) 351.16 (378.05) <0.001
## pcs (mean (SD)) 49.01 (9.63) 50.14 (8.91) 0.032
## mcs (mean (SD)) 48.90 (10.95) 46.87 (10.75) <0.001
## cv (%) 706 ( 95.3) 977 ( 95.4) 0.161
## 35 ( 4.7) 47 ( 4.6)
## tocv (mean (SD)) 1685.62 (1079.72) 1793.38 (1071.81) 0.099
## death (%) 657 ( 89.0) 949 ( 92.8) <0.001
## 81 ( 11.0) 74 ( 7.2)
## todeath (mean (SD)) 1674.37 (1050.09) 1758.19 (1054.68) 0.252而comparegroups只需要1行：
descrTable(year ~ . - id, data = regicor)
##
## --------Summary descriptives table by 'Recruitment year'---------
##
## ______________________________________________________________________________________________
## 1995 2000 2005 p.overall
## N=431 N=786 N=1077
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
## Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
## Sex: 0.506
## Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
## Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
## Smoking status: <0.001
## Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
## Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
## Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
## Systolic blood pressure 133 (19.2) 133 (21.3) 129 (19.8) <0.001
## Diastolic blood pressure 77.0 (10.5) 80.8 (10.3) 79.9 (10.6) <0.001
## History of hypertension: <0.001
## Yes 111 (25.8%) 233 (29.6%) 379 (35.5%)
## No 320 (74.2%) 553 (70.4%) 690 (64.5%)
## Hypertension treatment: 0.002
## No 360 (83.5%) 659 (83.8%) 804 (77.8%)
## Yes 71 (16.5%) 127 (16.2%) 230 (22.2%)
## Total cholesterol 225 (43.1) 224 (44.4) 213 (45.9) <0.001
## HDL cholesterol 51.9 (14.5) 52.3 (15.6) 53.2 (14.2) 0.208
## Triglycerides 114 (74.4) 114 (70.7) 117 (76.0) 0.582
## LDL cholesterol 152 (38.4) 149 (38.6) 136 (39.7) <0.001
## History of hyperchol.: <0.001
## Yes 97 (22.5%) 256 (33.2%) 356 (33.2%)
## No 334 (77.5%) 515 (66.8%) 715 (66.8%)
## Cholesterol treatment: <0.001
## No 403 (93.5%) 705 (91.2%) 903 (87.2%)
## Yes 28 (6.50%) 68 (8.80%) 132 (12.8%)
## Height (cm) 163 (9.21) 162 (9.39) 163 (9.05) 0.003
## Weight (Kg) 72.3 (12.6) 73.8 (14.0) 73.6 (13.9) 0.150
## Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) <0.001
## Physical activity (Kcal/week) 491 (419) 422 (377) 351 (378) <0.001
## Physical component 49.3 (8.08) 49.0 (9.63) 50.1 (8.91) 0.032
## Mental component 49.2 (11.3) 48.9 (11.0) 46.9 (10.8) <0.001
## Cardiovascular event: 0.161
## No 388 (97.5%) 706 (95.3%) 977 (95.4%)
## Yes 10 (2.51%) 35 (4.72%) 47 (4.59%)
## Days to cardiovascular event or end of follow-up 1784 (1101) 1686 (1080) 1793 (1072) 0.099
## Overall death: <0.001
## No 369 (95.3%) 657 (89.0%) 949 (92.8%)
## Yes 18 (4.65%) 81 (11.0%) 74 (7.23%)
## Days to overall death or end of follow-up 1713 (1042) 1674 (1050) 1758 (1055) 0.252
## ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯而且还能直接导出到Word，直接变成三线表！
如果是简单的做个三线表的话还是compareGroups更简单，但是tableone的功能貌似更多一点，比如除了这个三线表，它还可以用于加权数据中，还能用于倾向性评分中。