Bayesian variable selection using cost-adjusted BIC, with application to cost-effective measurement of quality of health care

In the field of quality of health care measurement, one approach to assessing patient sickness at admission involves a logistic regression of mortality within 30 days of admission on a fairly large number of sickness indicators (on the order of 100) to construct a sickness scale, employing classical variable selection methods to find an ``optimal'' subset of 10--20 indicators. Such ``benefit-only'' methods ignore the considerable differences among the sickness indicators in cost of data collection, an issue that is crucial when admission sickness is used to drive programs (now implemented or under consideration in several countries, including the U.S. and U.K.) that attempt to identify substandard hospitals by comparing observed and expected mortality rates (given admission sickness). When both data-collection cost and accuracy of prediction of 30-day mortality are considered, a large variable-selection problem arises in which costly variables that do not predict well enough should be omitted from the final scale. In this paper (a) we develop a method for solving this problem based on posterior model odds, arising from a prior distribution that (1) accounts for the cost of each variable and (2) results in a set of posterior model probabilities that corresponds to a generalized cost-adjusted version of the Bayesian information criterion (BIC), and (b) we compare this method with a decision-theoretic cost-benefit approach based on maximizing expected utility. We use reversible-jump Markov chain Monte Carlo (RJMCMC) methods to search the model space, and we check the stability of our findings with two variants of the MCMC model composition ($\mathit{MC}^3$) algorithm.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS207 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Solid state microelectronics tolerant to radiation and high temperature

The 300 C electronics technology based on JFET thick film hybrids was tested up to 10 to the 9th power rad gamma (Si) and 10 to the 15th power neutrons/sq cm. Circuits and individual components from this technology all survived this total dose although some devices required 1 hour of annealing at 200 or 300 C to regain functionality. This technology used with real time annealing should function to levels greater than 10 to the 10th power rad gamma and 10 to the 16th power n/sq cm

The Ethics of Education

As members of the veterinary profession, we are constantly confronted with the subjects of ethics and of education. These two topics are experienced in some manner every day of our lives. In addition to each individual\u27s personal moral code. the veteriarian must live by his professional ethical code which indicates various principles by which he is to act

The Geologic History of Subsurface Arkosic Sedimentary Rocks in the San Andreas Fault Observatory at Depth (SAFOD) Borehole, Central California

The aim of the San Andreas Fault Observatory at Depth (SAFOD) project, a component of the NSF Earthscope Initiative, is to directly observe active fault processes at seismogenic depths through the drilling of a 3 km deep (true vertical depth) inclined borehole across San Andreas fault. Preliminary subsurface models based on surface mapping and geophysical data predicted different lithologies than were actually encountered. At 1920 meters measured depth (mmd), a sequence of well-indurated, interbedded arkosic conglomerates, sandstones, and siltstones was encountered. We present a detailed lithologic and structural characterization as a step toward understanding the complex geologic history of this fault-bounded block of arkosic sedimentary rocks. We divide the arkosic section into three lithologic units with different compositional, structural, and sedimentary features: the upper arkose, 1920-2530 mmd, the clay-rich zone, 2530-2680 Illtlld, and the lower arkose, 2680-3150 mmd. We interpret the section to have been deposited in a Salinian transtensional basin, in either a subaqueous or subaerial fan setting. We suggest four different possibly equivalent sedimentary units to the SAFOD arkoses, the locations of which are dependent on how the San Andreas fault system has evolved over time in the vicinity of the SAFOD site. Detailed analysis of three subsidiary faults encountered in the arkosic section at 1920 mmd, 2530 mmd, and 3060 mmd, shows that subsurface faults have similar microstructures and composition as exhumed faults at the surface, with less evidence of alteration from extensive fluid flow

NASA Johnson Space Center's Planetary Sample Analysis and Mission Science (PSAMS) Laboratory: A National Facility for Planetary Research

NASA Johnson Space Center's (JSC's) Astromaterials Research and Exploration Science (ARES) Division, part of the Exploration Integration and Science Directorate, houses a unique combination of laboratories and other assets for conducting cutting edge planetary research. These facilities have been accessed for decades by outside scientists, most at no cost and on an informal basis. ARES has thus provided substantial leverage to many past and ongoing science projects at the national and international level. Here we propose to formalize that support via an ARES/JSC Plane-tary Sample Analysis and Mission Science Laboratory (PSAMS Lab). We maintain three major research capa-bilities: astromaterial sample analysis, planetary process simulation, and robotic-mission analog research. ARES scientists also support planning for eventual human ex-ploration missions, including astronaut geological training. We outline our facility's capabilities and its potential service to the community at large which, taken together with longstanding ARES experience and expertise in curation and in applied mission science, enable multi-disciplinary planetary research possible at no other institution. Comprehensive campaigns incorporating sample data, experimental constraints, and mission science data can be conducted under one roof