# phm-class: Methods for 'phm' objects. In mada: Meta-Analysis of Diagnostic Accuracy

## Description

Objects of the class phm are output by the function with the same name. Apart from standard methods the function sroc provides SROC curves and confidence bands for model fits.

## Usage

  1 2 3 4 5 6 7 8 9 10 ## S3 method for class 'phm' print(x, ...) ## S3 method for class 'phm' summary(object, level = 0.95, ...) ## S3 method for class 'phm' sroc(fit, fpr = 1:99/100, ...) ## S3 method for class 'phm' plot(x, extrapolate = FALSE, confband = TRUE, level = 0.95, ylim = c(0,1), xlim = c(0,1), sroclty = 1, sroclwd = 1, confbandlty = 2, confbandlwd = 0.5, ...) 

## Arguments

 x a phm object. object a phm object. fit a phm object. level numeric, the confidence level for calculations of confidence intervals (summary) or confidence bands (plot). fpr numeric, the false positives rates for which to calculate the predicted sensitivities. extrapolate logical, should the sroc curve be plotted beyond the observed false positive rates? confband logical, should confidence bands be plotted? ylim numeric of length 2, which section of the sensitivities to plot? xlim numeric of length 2, which section of the false positive rates to plot? sroclty integer, line type of the SROC curve sroclwd integer, line width of the SROC curve confbandlty integer, line type of the SROC curve's confidence band confbandlwd integer, line width of the SROC curve's confidence band ... arguments to be passed on to other functions

## Details

The SROC curve is derived from the model formula. The confidence bands are calculated from the bounds of the confidence interval for the diagnostic accuracy parameter θ. The parameter and its confidence interval are then also used to calculate the AUC and partial AUC using the formulae

AUC(a,b) = \int_a^bu^θ\mathrm{d}u = \frac{1}{θ+1}[b^{θ+1}-a^{θ+1}],

AUC = AUC(0,1)

and

pAUC = \frac{1}{b-a}AUC(a,b),

where a is the lower bound of the observed false positive rates and b the upper.

## Value

The sroc function returns a matrix ready for plotting. Each row corresponds to one point in ROC space.

## References

Holling, H., Boehning D., Boehning, W. (2012) “Meta-Analysis of Diagnostic Studies based upon SROC-Curves: a Mixed Model Approach using a Proportional Hazards Model.” Statistical Modelling, 12, 347–375.

phm
  1 2 3 4 5 6 7 8 9 10 # load data data(AuditC) # fit model fit <- phm(AuditC) #calculate a SROC curve, but do not plot it sroc.AuditC <- sroc(fit) # plot the SROC curve in ROC space as a line plot(sroc.AuditC, type = "l") # Fancy version using plot plot(fit)