The Effects of Malpractice Pressure and Liability Reforms on Physicians’ Perceptions of Medical Care

Considerable evidence suggests that the medical malpractice liability system neither provides compensation to patients who suffer negligent medical injury nor seeks to penalize physicians whose negligence causes patient injury. The relationship between liability reforms, malpractice pressure and physician perceptions of medical care is examined

On the monotonicity of the correction term in Ramanujan's factorial approximation

We present two new proofs of the monotonicity of the correction term $\theta_n$ in Ramanujan's refinement of Stirling's formula.Comment: Latex, 5 page

Empirical Efficiency Maximization

It has long been recognized that covariate adjustment can increase precision, even when it is not strictly necessary. The phenomenon is particularly emphasized in clinical trials, whether using continuous, categorical, or censored time-to-event outcomes. Adjustment is often straightforward when a discrete covariate partitions the sample into a handful of strata, but becomes more involved when modern studies collect copious amounts of baseline information on each subject. The dilemma helped motivate locally efficient estimation for coarsened data structures, as surveyed in the books of van der Laan and Robins (2003) and Tsiatis (2006). Here one fits a relatively small working model for the full data distribution, often with maximum likelihood, giving a nuisance parameter fit in an estimating equation for the parameter of interest. The usual advertisement is that the estimator is asymptotically efficient if the working model is correct, but otherwise is still consistent and asymptotically Normal. However, the working model will almost always be misspecified in practice. By applying standard likelihood based fits, one can poorly estimate the parameter of interest. We propose a new method, empirical efficiency maximization, to target the element of a working model minimizing asymptotic variance for the resulting parameter estimate, whether or not the working model is correctly specified. Our procedure is illustrated in three examples. It is shown to be a potentially major improvement over existing covariate adjustment methods for estimating disease prevalence in two-phase epidemiological studies, treatment effects in two-arm randomized trials, and marginal survival curves. Numerical asymptotic efficiency calculations demonstrate gains relative to standard locally efficient estimators

Doubly Robust Ecological Inference

The ecological inference problem is a famous longstanding puzzle that arises in many disciplines. The usual formulation in epidemiology is that we would like to quantify an exposure-disease association by obtaining disease rates among the exposed and unexposed, but only have access to exposure rates and disease rates for several regions. The problem is generally intractable, but can be attacked under the assumptions of King\u27s (1997) extended technique if we can correctly specify a model for a certain conditional distribution. We introduce a procedure that it is a valid approach if either this original model is correct or if we can pose a correct model for a different conditional distribution. The new method is illustrated on data concerning risk factors for diabetes

Covariate Adjustment for the Intention-to-Treat Parameter with Empirical Efficiency Maximization

In randomized experiments, the intention-to-treat parameter is defined as the difference in expected outcomes between groups assigned to treatment and control arms. There is a large literature focusing on how (possibly misspecified) working models can sometimes exploit baseline covariate measurements to gain precision, although covariate adjustment is not strictly necessary. In Rubin and van der Laan (2008), we proposed the technique of empirical efficiency maximization for improving estimation by forming nonstandard fits of such working models. Considering a more realistic randomization scheme than in our original article, we suggest a new class of working models for utilizing covariate information, show our method can be implemented by adding weights to standard regression algorithms, and demonstrate benefits over existing estimators through numerical asymptotic efficiency calculations and simulations

Liquid Metering Centrifuge Sticks (LMCS): A Centrifugal Approach to Metering Known Sample Volumes for Colorimetric Solid Phase Extraction (C-SPE)

Phase separation is one of the most significant obstacles encountered during the development of analytical methods for water quality monitoring in spacecraft environments. Removing air bubbles from water samples prior to analysis is a routine task on earth; however, in the absence of gravity, this routine task becomes extremely difficult. This paper details the development and initial ground testing of liquid metering centrifuge sticks (LMCS), devices designed to collect and meter a known volume of bubble-free water in microgravity. The LMCS uses centrifugal force to eliminate entrapped air and reproducibly meter liquid sample volumes for analysis with Colorimetric Solid Phase Extraction (C-SPE). C-SPE is a sorption-spectrophotometric platform that is being developed as a potential spacecraft water quality monitoring system. C-SPE utilizes solid phase extraction membranes impregnated with analyte-specific colorimetric reagents to concentrate and complex target analytes in spacecraft water samples. The mass of analyte extracted from the water sample is determined using diffuse reflectance (DR) data collected from the membrane surface and an analyte-specific calibration curve. The analyte concentration can then be calculated from the mass of extracted analyte and the volume of the sample analyzed. Previous flight experiments conducted in microgravity conditions aboard the NASA KC-135 aircraft demonstrated that the inability to collect and meter a known volume of water using a syringe was a limiting factor in the accuracy of C-SPE measurements. Herein, results obtained from ground based C-SPE experiments using ionic silver as a test analyte and either the LMCS or syringes for sample metering are compared to evaluate the performance of the LMCS. These results indicate very good agreement between the two sample metering methods and clearly illustrate the potential of utilizing centrifugal forces to achieve phase separation and metering of water samples in microgravity

Spatially resolved spectroscopy of the globular cluster RZ 2109 and the nature of its black hole

We present optical HST/STIS spectroscopy of RZ 2109, a globular cluster in the elliptical galaxy NGC 4472. This globular cluster is notable for hosting an ultraluminous X-ray source as well as associated strong and broad [OIII] 4959, 5007 emission. We show that the HST/STIS spectroscopy spatially resolves the [OIII] emission in RZ 2109. While we are unable to make a precise determination of the morphology of the emission line nebula, the best fitting models all require that the [OIII] 5007 emission has a half light radius in the range 3-7 pc. The extended nature of the [OIII] 5007 emission is inconsistent with published models that invoke an intermediate mass black hole origin. It is also inconsistent with the ionization of ejecta from a nova in the cluster. The spatial scale of the nebula could be produced via the photoionization of a strong wind driven from a stellar mass black hole accreting at roughly its Eddington rate.Comment: 7 pages, 4 figures - accepted for publication in Ap