autoReg：三线表格及森林图

2024-06-14 21:49| 来源: 网络整理| 查看: 265

首先致敬前辈科研行者

介绍一下最近的新宠「autoReg包」，不仅可以快捷完成基线表的制作，还可以直接一行代码输出回归分析（支持线性模型、广义线性模型和比例风险模型）的表格，我们还是以上次的示例数据来做演示。

安装并加载需要用的R包 install.packages("devtools") install.packages("remotes) # 如果devtools包是旧有的，可能需要更新，否则有可能报错 library(remotes) update(package_deps("devtools")) #更新devtools的依赖包 devtools::install_github("cardiomoon/autoReg") #从github上下载autoReg包 install.package("survival") #获取survival包中内置示例数据 library(autoReg) library(survival) 读取示例数据 data(pbc, package = "survival") head(pbc) #查看数据概况 id time status trt age sex ascites hepato spiders edema bili chol albumin copper alk.phos ast trig platelet protime stage 1 1 400 2 1 58.76523 f 1 1 1 1.0 14.5 261 2.60 156 1718.0 137.95 172 190 12.2 4 2 2 4500 0 1 56.44627 f 0 1 1 0.0 1.1 302 4.14 54 7394.8 113.52 88 221 10.6 3 3 3 1012 2 1 70.07255 m 0 0 0 0.5 1.4 176 3.48 210 516.0 96.10 55 151 12.0 4 4 4 1925 2 1 54.74059 f 0 1 1 0.5 1.8 244 2.54 64 6121.8 60.63 92 183 10.3 4 5 5 1504 1 2 38.10541 f 0 1 1 0.0 3.4 279 3.53 143 671.0 113.15 72 136 10.9 3 6 6 2503 2 2 66.25873 f 0 1 0 0.0 0.8 248 3.98 50 944.0 93.00 63 NA 11.0 3 整理数据 pbc=na.omit(pbc) #删掉缺失数据，方便演示 pbc$trt=factor(pbc$trt,levels = c(1,2), labels = c("Surgery","Chemotherapy")) #设定治疗手段为分类变量 pbc$sex=factor(pbc$sex, levels = c("f","m"),labels =c("Female", "Male")) #设定为性别分类变量 pbc$ascites=factor(pbc$ascites, levels = c(0,1),labels = c("with ascites", "no ascites")) #设定为腹水分类变量 pbc$edema=factor(pbc$edema, levels = c(0,0.5,1),labels =c("light","moderate","severe")) #设定为水肿分类变量 pbc$stage=factor(pbc$stage,levels = c(1,2,3,4), labels = c("I","II","III","IV")) #设定分期为分类变量 pbc$status=factor(pbc$status,levels = c(0,1,2), labels = c("alive","death","recurrance")) #设定状态为分类变量基线统计表 baseline_table1=gaze(trt~.,data=pbc) print(baseline_table1) # 以trt作为表头分类统计。 .表示使用其他所有变量。可以人为指定想要统计的变量。 # 比如gaze(trt+sex~ascites+edema+stage,data=pbc) —————————————————————————————————————————————————————————————————————————— Dependent:trt levels Surgery Chemotherapy p (N) (N=136) (N=140) —————————————————————————————————————————————————————————————————————————— id Mean ± SD 162.4 ± 90.3 154.9 ± 93.1 .495 time Mean ± SD 1957.4 ± 1088.4 2000.3 ± 1138.7 .749 status Mean ± SD 0.9 ± 1.0 0.8 ± 1.0 .553 age Mean ± SD 51.2 ± 11.0 48.5 ± 9.9 .033 sex Female 116 (85.3%) 126 (90%) .314 Male 20 (14.7%) 14 (10%) ascites with ascites 125 (91.9%) 132 (94.3%) .588 no ascites 11 (8.1%) 8 (5.7%) hepato Mean ± SD 0.5 ± 0.5 0.6 ± 0.5 .151 spiders Mean ± SD 0.3 ± 0.5 0.3 ± 0.5 .878 edema light 112 (82.4%) 122 (87.1%) .490 moderate 15 (11%) 10 (7.1%) severe 9 (6.6%) 8 (5.7%) bili Mean ± SD 3.0 ± 3.7 3.7 ± 5.3 .178 chol Mean ± SD 366.1 ± 212.1 376.3 ± 255.5 .719 albumin Mean ± SD 3.5 ± 0.4 3.5 ± 0.4 .375 copper Mean ± SD 103.3 ± 94.7 98.3 ± 81.8 .633 alk.phos Mean ± SD 2016.7 ± 2132.4 1977.1 ± 2106.4 .877 ast Mean ± SD 121.8 ± 52.6 126.4 ± 60.6 .501 trig Mean ± SD 123.8 ± 71.6 126.1 ± 58.8 .769 platelet Mean ± SD 258.1 ± 97.8 265.4 ± 88.5 .515 protime Mean ± SD 10.7 ± 0.9 10.8 ± 1.1 .239 stage I 9 (6.6%) 3 (2.1%) .262 II 31 (22.8%) 28 (20%) III 51 (37.5%) 60 (42.9%) IV 45 (33.1%) 49 (35%) —————————————————————————————————————————————————————————————————————————— baseline_table2=gaze(trt+sex~.,data=pbc) #trt再细分不同性别。 print(baseline_table2) ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— trt (N) Surgery (N=136) Chemotherapy (N=140) ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— Dependent:sex levels Female Male p Female Male p (N) (N=116) (N=20) (N=126) (N=14) ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— id Mean ± SD 163.6 ± 90.0 155.7 ± 93.9 .719 157.9 ± 94.4 128.2 ± 78.2 .260 time Mean ± SD 2008.6 ± 1080.8 1660.2 ± 1112.7 .187 1975.5 ± 1102.1 2223.4 ± 1457.8 .442 status Mean ± SD 0.8 ± 1.0 1.6 ± 0.8 .001 0.8 ± 0.9 1.0 ± 1.0 .500 age Mean ± SD 50.3 ± 10.6 56.1 ± 12.7 .029 47.5 ± 9.3 57.2 ± 10.4 glm(status~trt+sex+ascites+edema+stage+bili+chol+albumin+ast,data = pbc, family = "binomial") Call: glm(formula = status ~ trt + sex + ascites + edema + stage + bili + chol + albumin + ast, family = "binomial", data = pbc) Coefficients: (Intercept) trtChemotherapy sexMale ascitesno ascites edemamoderate edemasevere -2.216433 -0.305154 1.210329 1.088021 0.686944 1.119620 stageII stageIII stageIV bili chol albumin 1.614696 2.125737 2.498416 0.278962 0.000418 -0.406884 ast 0.003200 Degrees of Freedom: 275 Total (i.e. Null); 263 Residual Null Deviance: 381.4 Residual Deviance: 282.5 AIC: 308.5 回归分析统计表 autoReg(fit) #只显示多因素回归 ———————————————————————————————————————————————————————————————————————————————————————————————————————————————— Dependent: status alive (N=147) death (N=18) recurrance (N=111) OR (multivariable) ———————————————————————————————————————————————————————————————————————————————————————————————————————————————— trt Surgery 70 (47.6%) 9 (50%) 57 (51.4%) Chemotherapy 77 (52.4%) 9 (50%) 54 (48.6%) 0.74 (0.41-1.31, p=.301) sex Female 137 (93.2%) 15 (83.3%) 90 (81.1%) Male 10 (6.8%) 3 (16.7%) 21 (18.9%) 3.35 (1.39-8.09, p=.007) ascites with ascites 146 (99.3%) 18 (100%) 93 (83.8%) no ascites 1 (0.7%) 0 (0%) 18 (16.2%) 2.97 (0.20-44.35, p=.430) edema light 139 (94.6%) 16 (88.9%) 79 (71.2%) moderate 7 (4.8%) 2 (11.1%) 16 (14.4%) 1.99 (0.68-5.77, p=.207) severe 1 (0.7%) 0 (0%) 16 (14.4%) 3.06 (0.22-43.27, p=.407) stage I 11 (7.5%) 0 (0%) 1 (0.9%) II 42 (28.6%) 3 (16.7%) 14 (12.6%) 5.03 (0.44-57.65, p=.195) III 62 (42.2%) 8 (44.4%) 41 (36.9%) 8.38 (0.76-92.62, p=.083) IV 32 (21.8%) 7 (38.9%) 55 (49.5%) 12.16 (1.08-137.02, p=.043) bili Mean ± SD 1.6 ± 1.8 3.2 ± 2.0 5.7 ± 6.2 1.32 (1.12-1.57, p=.001) chol Mean ± SD 326.9 ± 168.1 439.5 ± 335.5 418.9 ± 277.9 1.00 (1.00-1.00, p=.634) albumin Mean ± SD 3.6 ± 0.3 3.6 ± 0.4 3.4 ± 0.5 0.67 (0.29-1.52, p=.334) ast Mean ± SD 110.2 ± 54.4 130.2 ± 38.0 141.5 ± 57.7 1.00 (1.00-1.01, p=.287) ———————————————————————————————————————————————————————————————————————————————————————————————————————————————— > fit2=glm(trt~sex+ascites+edema+stage+bili+chol+albumin+ast,data = pbc, family = "binomial") > autoReg(fit2, uni=TRUE) #uni=TRUE, 显示单因素,先进行单因素挑选统计意义显著的解释变量进入多因素分析 ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— Dependent: trt Surgery (N=136) Chemotherapy (N=140) OR (univariable) OR (multivariable) ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— sex Female 116 (85.3%) 126 (90%) Male 20 (14.7%) 14 (10%) 0.64 (0.31-1.33, p=.237) ascites with ascites 125 (91.9%) 132 (94.3%) no ascites 11 (8.1%) 8 (5.7%) 0.69 (0.27-1.77, p=.438) edema light 112 (82.4%) 122 (87.1%) moderate 15 (11%) 10 (7.1%) 0.61 (0.26-1.42, p=.252) severe 9 (6.6%) 8 (5.7%) 0.82 (0.30-2.19, p=.686) stage I 9 (6.6%) 3 (2.1%) II 31 (22.8%) 28 (20%) 2.71 (0.67-11.02, p=.164) 2.65 (0.65-10.77, p=.174) III 51 (37.5%) 60 (42.9%) 3.53 (0.91-13.74, p=.069) 3.34 (0.86-13.05, p=.083) IV 45 (33.1%) 49 (35%) 3.27 (0.83-12.83, p=.090) 2.92 (0.73-11.64, p=.128) bili Mean ± SD 3.0 ± 3.7 3.7 ± 5.3 1.04 (0.98-1.09, p=.184) 1.03 (0.98-1.09, p=.266) chol Mean ± SD 366.1 ± 212.1 376.3 ± 255.5 1.00 (1.00-1.00, p=.719) albumin Mean ± SD 3.5 ± 0.4 3.5 ± 0.4 1.31 (0.73-2.35, p=.373) ast Mean ± SD 121.8 ± 52.6 126.4 ± 60.6 1.00 (1.00-1.01, p=.500) ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #当然也可以设定所有的因素全部进入多变量回归分析,设置参数threshold=1 autoReg(fit2, uni=TRUE, threshold=1) %>% myft() #myft()函数生成发表级别图片

另外，设置参数imputed=TRUE可以应用于多重插补数据的模型比较。这个我们后面再学习。

表格与森林图导出 install.packages("rrtable") library(rrtable) result=autoReg(fit2, uni=TRUE, threshold=1) %>% myft() table2pptx(result) #导出到ppt，可编辑数据 table2docx(result) #导出到docx，可编辑数据

#多因素回归统计森林图 modelPlot(fit2)

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modelPlot(fit2,uni=TRUE,threshold=1,show.ref=FALSE)

#将图片导出至ppt编辑 p1=modelPlot(fit2) rrtable::plot2pptx(print(p1))

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