We present a new compilation of Type Ia supernovae (SNe Ia), a new data set of low-redshift nearby-Hubble-flow SNe, and new analysis procedures to work with these heterogeneous compilations. This ''Union'' compilation of 414 SNe Ia, which reduces to 307 SNe after selection cuts, includes the recent large samples of SNe Ia from the Supernova Legacy Survey and ESSENCE Survey, the older data sets, as well as the recently extended data set of distant supernovae observed with the Hubble Space Telescope (HST ). A single, consistent, and blind analysis procedure is used for all the various SN Ia subsamples, and a new procedure is implemented that consistently weights the heterogeneous data sets and rejects outliers. We present the latest results from this Union compilation and discuss the cosmological constraints from this new compilation and its combination with other cosmological measurements (CMB and BAO). The constraint we obtain from supernovae on the dark energy density is à ¼ 0:713 þ0:027 À0:029 (stat) þ0:036 À0:039 (sys), for a flat, ÃCDM universe. Assuming a constant equation of state parameter, w, the combined constraints from SNe, BAO, and A CMB give w ¼ À0:969 þ0:059 À0:063 (stat) þ0:063 À0:066 (sys). While our results are consistent with a cosmological constant, we obtain only relatively weak constraints on a w that varies with redshift. In particular, the current SN data do not yet significantly constrain w at z > 1. With the addition of our new nearby Hubble-flow SNe Ia, these resulting cosmological constraints are currently the tightest available.

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The coefficient of variation is often used as a guide of the repeatability of measurements in clinical trials and other medical work. When possible, one makes repeated measurements on a set of individuals to calculate the relative variability of the test with the understanding that a reliable clinical test should give similar results when repeated on the same patient. There are times, however, when repeated measurements on the same patient are not possible. Under these circumstances, to combine results from different clinical trials or test sites, it is necessary to compare the coefficients of variation of several clinical trials. Using the work of Miller, we develop a general statistic for testing the hypothesis that the coefficients of variation are the same for k populations, with unequal sample sizes. This statistic is invariant under the choice of the order of the populations, and is asymptotically chi 2. We provide an example using data from Yang and HayGlass. We compare the size and the power of the test to that of Bennett, Doornbos and Dijkstra and a statistic based on Hedges and Olkin.

Background The Beers list of potentially inappropriate medications (PIMs) provides a key indicator of medication prescribing quality. The criteria were updated in 2012, adding new drugs and assessing evidence strength. Objectives To use the most recently available population-based data to estimate PIM prevalence under the 2012 update and to provide a benchmark from which to measure future changes. Design and Setting Retrospective cohort study using nationally representative data from the 2006–2010 Medical Expenditure Panel Survey (MEPS). Participants Community-dwelling sample of US older adults (n = 18,475). Measurements We operationalized the updated Beers criteria, generating a “broad” PIM definition that incorporated form, route or dose restrictions where clearly specified and a “qualified” definition that applied specific exceptions where mentioned in the rationale associated with each drug category. Bivariate analyses described PIM prevalence, comparing the two operational definitions, and examined time trends. Results Among older adults with prescription medications, 42.6% had at least one medication fill that met the broad definition, with non-steroidal anti-inflammatory drugs (NSAIDs) having the highest (10.9%) prevalence. The rate declined from 45.5% in 2006–2007 to 40.8% in 2009–2010. The categories with the largest absolute decline were NSAIDs, selected sulfonylureas, and estrogens. PIM prevalence was 30.7% using the qualified definition. Conclusion Despite the overall high use of PIMs, there has been a decline observed in recent years. Future studies should test the effect of educational and clinical interventions on changes in PIM use and patient outcomes. The current study results can aid in targeting these interventions.

Our results suggest that physicians attempt to avoid PIM use in the oldest old but have inadequate focus on the high-PIM-risk conditions. Educational programs targeted to physician practice regarding high-PIM-risk conditions and patient literacy regarding medication use are potential responses.