PIPC Analysis

library(glmnet)

## Loading required package: Matrix

## Loaded glmnet 5.0

library(pROC)

## Type 'citation("pROC")' for a citation.

## 
## Attaching package: 'pROC'

## The following objects are masked from 'package:stats':
## 
##     cov, smooth, var

dat <- read.csv("PIPC_SYNTHETIC_75.csv")
dat_model <- na.omit(dat)
y <- dat_model$outcome_hosp_6m
x <- model.matrix(outcome_hosp_6m ~ ., dat_model)[,-1]
set.seed(123)
cv_fit <- cv.glmnet(x, y, family="binomial", alpha=1)
plot(cv_fit)

pred <- predict(cv_fit, newx=x, s="lambda.1se", type="response")
roc_obj <- roc(y, as.vector(pred))

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

plot(roc_obj, print.auc=TRUE)

auc(roc_obj)

## Area under the curve: 1