cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R

'Optimal cutpoints' for binary classification tasks are often established by testing which cutpoint yields the best discrimination, for example the Youden index, in a specific sample. This results in 'optimal' cutpoints that are highly variable and systematically overestimate the out-of-sample performance. To address these concerns, the cutpointr package offers robust methods for estimating optimal cutpoints and the out-of-sample performance. The robust methods include bootstrapping and smoothing based on kernel estimation, generalized additive models, smoothing splines, and local regression. These methods can be applied to a wide range of binary-classification and cost-based metrics. cutpointr also provides mechanisms to utilize user-defined metrics and estimation methods. The package has capabilities for parallelization of the bootstrapping, including reproducible random number generation. Furthermore, it is pipe-friendly, for example for compatibility with functions from tidyverse. Various functions for plotting receiver operating characteristic curves, precision recall graphs, bootstrap results and other representations of the data are included. The package contains example data from a study on psychological characteristics and suicide attempts suitable for applying binary classification algorithms.Comment: 27 pages, 2 tables, 6 figures. To be published in the Journal of Statistical Softwar

Developing a combined quantitative benchmarking system for the performance of local health authorities: The case of the Tuscany Region in Italy

This paper proposes an integrated quantitative benchmarking approach for the measurement of the performance of Local Health Authorities (LHAs). It is based on a sound balanced scorecard approach developed and implemented in the Tuscany Region by the Management and Health Laboratory of Sant’Anna School combined with a bias corrected measure of technical efficiency, estimated using a bootstrap based Data Envelopment Analysis. The empirical results show that the typical LHA in Tuscany experienced 14% bias-corrected inefficiency in 2007. Using correlation analysis and mapping quadrants, the paper shows the relationships among technical efficiency and quality and appropriateness as well as analyses the impact of organizational factors on the performance of LHAs. Finally, this combined benchmarking approach is illustrated as a useful and important managerial tool both for regional and local authorities.appropriateness, bias correction, data envelopment analysis, local health authorities, performance evaluation system

Bayesian astrostatistics: a backward look to the future

This perspective chapter briefly surveys: (1) past growth in the use of Bayesian methods in astrophysics; (2) current misconceptions about both frequentist and Bayesian statistical inference that hinder wider adoption of Bayesian methods by astronomers; and (3) multilevel (hierarchical) Bayesian modeling as a major future direction for research in Bayesian astrostatistics, exemplified in part by presentations at the first ISI invited session on astrostatistics, commemorated in this volume. It closes with an intentionally provocative recommendation for astronomical survey data reporting, motivated by the multilevel Bayesian perspective on modeling cosmic populations: that astronomers cease producing catalogs of estimated fluxes and other source properties from surveys. Instead, summaries of likelihood functions (or marginal likelihood functions) for source properties should be reported (not posterior probability density functions), including nontrivial summaries (not simply upper limits) for candidate objects that do not pass traditional detection thresholds.Comment: 27 pp, 4 figures. A lightly revised version of a chapter in "Astrostatistical Challenges for the New Astronomy" (Joseph M. Hilbe, ed., Springer, New York, forthcoming in 2012), the inaugural volume for the Springer Series in Astrostatistics. Version 2 has minor clarifications and an additional referenc