pooledROC.emp {AROC} R Documentation

## Empirical estimation of the pooled ROC curve.

### Description

Estimates the pooled ROC curve using the empirical estimator proposed by Hsieh and Turnbull (1996).

### Usage

```pooledROC.emp(y0, y1, p = seq(0, 1, l = 101), B = 500,
method = c("ncoutcome", "coutcome"))
```

### Arguments

 `y0` Diagnostic test outcomes in the healthy group. `y1` Diagnostic test outcomes in the diseased group. `p` Set of false positive fractions (FPF) at which to estimate the covariate-adjusted ROC curve. `B` An integer value specifying the number of bootstrap resamples for the construction of the confidence intervals. By default 500. `method` A character string specifying if bootstrap resampling (for the confidence intervals) should be done with or without regard to the disease status (“coutcome” or “noutcome”). In both cases, a naive bootstrap is used. By default, the resampling is done conditionally on the disease status.

### Value

As a result, the function provides a list with the following components:

 `call` the matched call. `p` Set of false positive fractions (FPF) at which the pooled ROC curve has been estimated `ROC` Estimated pooled ROC curve, and corresponding 95% confidence intervals (if required) `AUC` Estimated pooled AUC, and corresponding 95% confidence intervals (if required).

### References

Hsieh, F., and Turnbull, B.W. (1996). Nonparametric and semiparametric estimation of the receiver operating characteristic curve, The Annals of Statistics, 24, 25-40.

`AROC.bnp`, `AROC.bsp`, `AROC.sp`, `AROC.kernel`, `pooledROC.BB` or `pooledROC.emp`.

### Examples

```library(AROC)
data(psa)
# Select the last measurement
newpsa <- psa[!duplicated(psa\$id, fromLast = TRUE),]

# Log-transform the biomarker
newpsa\$l_marker1 <- log(newpsa\$marker1)

m0_emp <- pooledROC.emp(newpsa\$l_marker1[newpsa\$status == 0],
newpsa\$l_marker1[newpsa\$status == 1], p = seq(0,1,l=101), B = 500)

summary(m0_emp)

plot(m0_emp)

```

[Package AROC version 1.0-3 Index]