Stata 15 help for roctab

[R] roctab -- Nonparametric ROC analysis

Syntax

roctab refvar classvar [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- Main lorenz report Gini and Pietra indices binomial calculate exact binomial confidence intervals nolabel display numeric codes rather than value labels detail show details on sensitivity/specificity for each cutpoint table display the raw data in a 2 x k contingency table bamber calculate standard errors by using the Bamber method hanley calculate standard errors by using the Hanley method graph graph the ROC curve norefline suppress plotting the 45-degree reference line summary report the area under the ROC curve specificity graph sensitivity versus specificity level(#) set confidence level; default is level(95)

Plot plotopts(plot_options) affect rendition of the ROC curve

Reference line rlopts(cline_options) affect rendition of the reference line

Add plots addplot(plot) add other plots to generated graph

Y axis, X axis, Titles, Legend, Overall twoway_options any options other than by() documented in [G-3] twoway_options ------------------------------------------------------------------------- fweights are allowed; see weight.

plot_options Description ------------------------------------------------------------------------- marker_options change look of markers (color, size, etc.) marker_label_options add marker labels; change look or position cline_options change look of the line -------------------------------------------------------------------------

Menu

Statistics > Epidemiology and related > ROC analysis > Nonparametric ROC analysis without covariates

Description

The above command is used to perform receiver operating characteristic (ROC) analyses with rating and discrete classification data.

The two variables refvar and classvar must be numeric. The reference variable indicates the true state of the observation, such as diseased and nondiseased or normal and abnormal, and must be coded as 0 and 1. The rating or outcome of the diagnostic test or test modality is recorded in classvar, which must be at least ordinal, with higher values indicating higher risk.

roctab performs nonparametric ROC analyses. By default, roctab calculates the area under the ROC curve. Optionally, roctab can plot the ROC curve, display the data in tabular form, and produce Lorenz-like plots.

See [R] rocfit for a command that fits maximum-likelihood ROC models.

Options

+------+ ----+ Main +-------------------------------------------------------------

lorenz specifies that the Gini and Pietra indices be reported. Optionally, graph will plot the Lorenz-like curve.

binomial specifies that exact binomial confidence intervals be calculated.

nolabel specifies that numeric codes be displayed rather than value labels.

detail outputs a table displaying the sensitivity, specificity, the percentage of subjects correctly classified, and two likelihood ratios for each possible cutpoint of classvar.

table outputs a 2 x k contingency table displaying the raw data.

bamber specifies that the standard error for the area under the ROC curve be calculated using the method suggested by Bamber (1975). Otherwise, standard errors are obtained as suggested by DeLong, DeLong, and Clarke-Pearson (1988).

hanley specifies that the standard error for the area under the ROC curve be calculated using the method suggested by Hanley and McNeil (1982). Otherwise, standard errors are obtained as suggested by DeLong, DeLong, and Clarke-Pearson (1988).

graph produces graphical output of the ROC curve. If lorenz is specified, the graphical output of a Lorenz-like curve will be produced.

norefline suppresses plotting the 45-degree reference line from the graphical output of the ROC curve.

summary reports the area under the ROC curve, its standard error, and its confidence interval. If lorenz is specified, Lorenz indices are reported. This option is needed only when also specifying graph.

specificity produces a graph of sensitivity versus specificity instead of sensitivity versus (1 - specificity). specificity implies graph.

level(#) specifies the confidence level, as a percentage, for the confidence intervals. The default is level(95) or as set by set level.

+------+ ----+ Plot +-------------------------------------------------------------

plotopts(plot_options) affects the rendition of the plotted ROC curve -- the curve's plotted points connected by lines. The plot_options can affect the size and color of markers, whether and how the markers are labeled, and whether and how the points are connected; see [G-3] marker_options, [G-3] marker_label_options, and [G-3] cline_options.

+----------------+ ----+ Reference line +---------------------------------------------------

rlopts(cline_options) affects the rendition of the reference line; see [G-3] cline_options.

+-----------+ ----+ Add plots +--------------------------------------------------------

addplot(plot) provides a way to add other plots to the generated graph; see [G-3] addplot_option.

+-----------------------------------------+ ----+ Y axis, X axis, Titles, Legend, Overall +--------------------------

twoway_options are any of the options documented in [G-3] twoway_options, excluding by(). These include options for titling the graph (see [G-3] title_options) and for saving the graph to disk (see [G-3] saving_option).

Examples

Nonparametric ROC analysis example . webuse hanley . roctab disease rating . roctab disease rating, graph . roctab disease rating, graph summary . roctab disease rating [fw=pop] . roctab disease rating, table detail . roctab disease rating, lorenz . roctab disease rating, lorenz graph

Stored results

roctab stores the following in r():

Scalars r(N) number of observations r(se) standard error for the area under the ROC curve r(lb) lower bound of CI for the area under the ROC curve r(ub) upper bound of CI for the area under the ROC curve r(level) confidence level r(area) area under the ROC curve r(pietra) Pietra index r(gini) Gini index

Macros r(cutpoints) description of cutpoints (detail only)

Matrices r(detail) matrix with details on sensitivity and specificity for each cutpoint (detail only)

References

Bamber, D. 1975. The area above the ordinal dominance graph and the area below the receiver operating characteristic graph. Journal of Mathematical Psychology 12: 387-415.

DeLong, E. R., D. M. DeLong, and D. L. Clarke-Pearson. 1988. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics 44: 837-845.

Hanley, J. A., and B. J. McNeil. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143: 29-36.


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