In LarsenLab/immuntools: Larsen lab data analysis toolbox

# setup chunk options
knitr::opts_chunk$set(
  echo = FALSE
)

# load packages
library(tableone)
library(survival)

# other packages

Descriptive statistics

• continuous variables
• categorical variables

# use Load Mayo Clinic Primary Biliary Cirrhosis Data as example
catVars <- c("status","trt","ascites","hepato","spiders","edema","stage")
listVars <- colnames(pbc)[!(colnames(pbc) %in% c("id"))]
table1 <- CreateTableOne(vars = listVars, data = pbc, factorVars = catVars,strata = c("trt"))

---

Descriptive statistics - continuous variables

print(table1$ContTable,contDigits = 1,pDigits=2)

---

Descriptive statistics - categorical variables

print(table1$CatTable,catDigits = 1,pDigits=2)

Survival analysis

• Survival curve
• Cox model

---

Survival analysis - Kaplan-Meier curve

surv <- survfit(Surv(time, status) ~ x, data = aml) 
plot(surv, lty = 1:2) 

---

Survival analysis - cox model result

test1 <- list(start=c(1,2,5,2,1,7,3,4,8,8), 
              stop=c(2,3,6,7,8,9,9,9,14,17), 
              event=c(1,1,1,1,1,1,1,0,0,0), 
              x=c(1,0,0,1,0,1,1,1,0,0)) 
summary(coxph(Surv(start, stop, event) ~ x, test1))

Other statistical analysis result

LarsenLab/immuntools documentation built on July 19, 2023, 9:43 a.m.