其他
使用compareGroups包1行代码生成基线资料表
这个包可用一句代码生成基线资料表、单因素分析表、多因素分析表等，可直接把结果导出为csv、Excel、Word、Markdown、LaTeX、PDF，而且十分美观，大大提高工作效率。
在我看来只有一个小小的问题：供选择的统计方法有限。
安装
使用
参数调整
安装
# 2选1
install.packages("compareGroups")
# library(devtools)
devtools::install_github(repo = "isubirana/compareGroups")
使用
现在使用只要一句代码就可以生成基线资料表了。使用自带的predimed数据集进行演示。
“PREDIMED 是在西班牙进行的一项多中心试验，将具有血管风险，但在参加试验时没有心血管疾病的参与者，随机分配到以下三种饮食中的一种：地中海饮食加特级初榨橄榄油 (MedDiet+VOO)， 地中海饮食补充混合坚果（MedDiet+Nuts），或控制饮食（建议减少饮食脂肪）。主要终点是心血管事件（心肌梗死、中风或心血管原因死亡）的发生。
这个数据可以说是非常临床了！对于搞临床的来说非常亲切，这样方便大家换成自己的数据。
library(compareGroups)
## Warning: 程辑包'compareGroups'是用R版本4.1.3 来建造的
data("predimed")
dim(predimed)
## [1] 6324 15
str(predimed)
## 'data.frame': 6324 obs. of 15 variables:
## $ group : Factor w/ 3 levels "Control","MedDiet + Nuts",..: 1 1 3 2 3 1 3 3 1 1 ...
## ..- attr(*, "label")= chr "Intervention group"
## $ sex : Factor w/ 2 levels "Male","Female": 1 1 2 1 2 1 2 1 1 1 ...
## ..- attr(*, "label")= chr "Sex"
## $ age : num 58 77 72 71 79 63 75 66 71 76 ...
## ..- attr(*, "label")= chr "Age"
## $ smoke : Factor w/ 3 levels "Never","Current",..: 3 2 3 3 1 3 1 1 3 2 ...
## ..- attr(*, "label")= chr "Smoking"
## $ bmi : num 33.5 31.1 30.9 27.7 35.9 ...
## ..- attr(*, "label")= chr "Body mass index"
## $ waist : num 122 119 106 118 129 143 88 85 90 79 ...
## ..- attr(*, "label")= chr "Waist circumference"
## $ wth : num 0.753 0.73 0.654 0.694 0.806 ...
## ..- attr(*, "label")= chr "Waist-to-height ratio"
## $ htn : Factor w/ 2 levels "No","Yes": 1 2 1 2 2 2 1 2 2 2 ...
## ..- attr(*, "label")= chr "Hypertension"
## $ diab : Factor w/ 2 levels "No","Yes": 1 2 2 1 1 2 2 2 1 2 ...
## ..- attr(*, "label")= chr "Type-2 diabetes"
## $ hyperchol: Factor w/ 2 levels "No","Yes": 2 1 1 2 2 2 2 1 2 1 ...
## ..- attr(*, "label")= chr "Dyslipidemia"
## $ famhist : Factor w/ 2 levels "No","Yes": 1 1 2 1 1 1 1 2 1 1 ...
## ..- attr(*, "label")= chr "Family history of premature CHD"
## $ hormo : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 NA NA 1 1 1 ...
## ..- attr(*, "label")= chr "Hormone-replacement therapy"
## $ p14 : num 10 10 8 8 9 9 8 9 14 9 ...
## ..- attr(*, "label")= chr "MeDiet Adherence score"
## $ toevent : num 5.37 6.1 5.95 2.91 4.76 ...
## ..- attr(*, "label")= chr "follow-up to main event (years)"
## $ event : Factor w/ 2 levels "No","Yes": 2 1 1 2 1 2 1 1 2 1 ...
## ..- attr(*, "label")= Named chr "AMI, stroke, or CV Death"
## .. ..- attr(*, "names")= chr "varlabel"
数据就是这样的，大部分都是常见的基线资料，比如性别、年龄、BMI、腰围、是否有糖尿病、是否有高血压、是否有家族史等等，还有一列是 是否发生了心血管事件。
“分类变量要因子化！
restab <- descrTable(group ~ ., data = predimed)
restab
##
## --------Summary descriptives table by 'Intervention group'---------
##
## ____________________________________________________________________________________
## Control MedDiet + Nuts MedDiet + VOO p.overall
## N=2042 N=2100 N=2182
## ˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉ
## Sex: <0.001
## Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
## Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
## Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
## Smoking: 0.444
## Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
## Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
## Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
## Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) <0.001
## Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
## Waist-to-height ratio 0.63 (0.07) 0.62 (0.06) 0.63 (0.06) <0.001
## Hypertension: 0.249
## No 331 (16.2%) 362 (17.2%) 396 (18.1%)
## Yes 1711 (83.8%) 1738 (82.8%) 1786 (81.9%)
## Type-2 diabetes: 0.017
## No 1072 (52.5%) 1150 (54.8%) 1100 (50.4%)
## Yes 970 (47.5%) 950 (45.2%) 1082 (49.6%)
## Dyslipidemia: 0.423
## No 563 (27.6%) 561 (26.7%) 622 (28.5%)
## Yes 1479 (72.4%) 1539 (73.3%) 1560 (71.5%)
## Family history of premature CHD: 0.581
## No 1580 (77.4%) 1640 (78.1%) 1675 (76.8%)
## Yes 462 (22.6%) 460 (21.9%) 507 (23.2%)
## Hormone-replacement therapy: 0.850
## No 1811 (98.3%) 1835 (98.4%) 1918 (98.2%)
## Yes 31 (1.68%) 30 (1.61%) 36 (1.84%)
## MeDiet Adherence score 8.44 (1.94) 8.81 (1.90) 8.77 (1.97) <0.001
## follow-up to main event (years) 4.09 (1.74) 4.31 (1.70) 4.64 (1.60) <0.001
## AMI, stroke, or CV Death: 0.064
## No 1945 (95.2%) 2030 (96.7%) 2097 (96.1%)
## Yes 97 (4.75%) 70 (3.33%) 85 (3.90%)
## ˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉˉ
这样一张表基本上就很全面了，可以直接导出到Word里面微调。
export2word(restab, file='table1.docx') # 直接是三线表的格式
参数调整
有非常多参数可以调整，这里简单介绍下几个常用的。
descrTable(formula, data, subset, na.action = NULL, y = NULL, Xext = NULL,
selec = NA, method = 1, timemax = NA, alpha = 0.05, min.dis = 5, max.ylev = 5,
max.xlev = 10, include.label = TRUE, Q1 = 0.25, Q3 = 0.75, simplify = TRUE,
ref = 1, ref.no = NA, fact.ratio = 1, ref.y = 1, p.corrected = TRUE,
compute.ratio = TRUE, include.miss = FALSE, oddsratio.method = "midp",
chisq.test.perm = FALSE, byrow = FALSE, chisq.test.B = 2000, chisq.test.seed = NULL,
Date.format = "d-mon-Y", var.equal = TRUE, conf.level = 0.95, surv = FALSE,
riskratio = FALSE, riskratio.method = "wald", compute.prop = FALSE,
lab.missing = "'Missing'",
hide = NA, digits = NA, type = NA, show.p.overall = TRUE,
show.all, show.p.trend, show.p.mul = FALSE, show.n, show.ratio =
FALSE, show.descr = TRUE, show.ci = FALSE, hide.no = NA, digits.ratio = NA,
show.p.ratio = show.ratio, digits.p = 3, sd.type = 1, q.type = c(1, 1),
extra.labels = NA, all.last = FALSE)
可以看到参数非常多，这其实是整合了createTable()和compareGroups()两个函数的参数，本来是要2步才能创建一个表的，现在只需要一个descrTable()函数即可。
formula：可以为空，如果是空则对整体进行描述，也不进行统计检验 subset和selec：用来选择部分数据，比如subset = sex == "Female"，selec = list(hormo = sex == "Female", waist = waist > 20) method：默认连续型变量为正态分布，使用anova法进行检验，有以下取值： alpha：决定shapiro-wilks法的显著性界值 min.dis：当个数少于这个值时，则转化为分类变量 比如method = c(waist = 2)，waist这个变量为非正态分布 1：正态分布（默认值），使用anova和t-test，多重比较使用Turkey法 2：非正态分布，使用kruskal法进行检验 3：分类变量，使用卡方或者Fisher精确概率法 NA：使用shapiro-wilks法检验是否是正态分布，然后选择合适的方法，提供另外2个参数： max.ylev和max.xlev：因变量和自变量最大的水平数量 simplify：默认是TRUE，删除因子水平为0的 ref和ref.no：更改参考水平 fact.ratio：计算OR值或RR值的增加单位，比如fact.ratio = c(age = 10, bmi = 2)，表示age每增加10，bmi每增加2 digits：小数点位数
这个R包也可用于生存分析，只要用Surv()函数构建号表达式即可，还自带了一些可视化方法，不过并不是很好看。另外，还提供了一个图形化的使用方式。
更多精彩，可以查看官方教程[1]
参考资料
comparegroups官方教程：: https://cran.r-project.org/web/packages/compareGroups/vignettes/compareGroups_vignette.html
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