80,258 research outputs found
Estimating risks of identification disclosure in partially synthetic data
No full textTo limit disclosures, statistical agencies and other data disseminators can release partially synthetic, public use microdata sets. These comprise the units originally surveyed, but some collected values, for example sensitive values at high risk of disclosure or values of key identifiers, are replaced with multiple draws from statistical models. Because the original records are on the file, there remain risks of identifications. In this paper, we describe how to evaluate identification disclosure risks in partially synthetic data, accounting for released information from the multiple datasets, the model used to generate synthetic values, and the approach used to select values to synthesize. We illustrate the computations using the Survey of Youths in Custody
Estimating propensity scores with missing covariate data using general location mixture models
No full textIn many observational studies, researchers estimate causal effects using propensity scores, e.g., by matching or sub-classifying on the scores. Estimation of propensity scores is complicated when some values of the covariates aremissing. We propose to use multiple imputation to create completed datasets, from which propensity scores can be estimated, with a general location mixture model. The model assumes that the control units are a latent mixture of (i)units whose covariates are drawn from the same distributions as the treated units’ covariates and (ii) units whose covariates are drawn from different distributions. This formulation reduces the influence of control units outside the treated units’ region of the covariate space on the estimation of parameters in the imputation model, which can result in more plausible imputations and better balance in the true covariate distributions. We illustrate the benefits of 1 the latent class modeling approach with simulations and with an observationalstudy of the effect of breast feeding on children’s cognitive abilities
Propensity score matching with missing covariates via iterated, sequential multiple imputation
No full textIn many observational studies, analysts estimate causal effects using propensity score matching. Estimation of propensity scores is complicated when covariate values intended for collection are in fact missing. To handle the missing data, one approach is to use multiple imputation to create completed datasets, and compute propensity scores from these datasets. However, inaccurate imputation models can result in ineffective matching, thereby limiting reductions in bias. We propose a multiple imputation approach based on chained equations in which the researcher gradually reduces the set of control units used to estimate the imputation models. This approach can reduce the influence of control records far from the treated units’ region of the covariate space on the estimation of parameters in the imputation model, which can result in more plausible imputations and better balance in the true covariate distributions. This approach can be conveniently implemented with standard multiple imputation software for missing data. Using simulations, we find that the approach can improve estimation when imputation models are mis-specified; however, it can be ineffective when imputation models are correctly specified. This suggests using the approach as part of sensitivity analysis in causal inference. We apply the approach to an observational study of the effect of breast-feeding on the child’s educational outcomes later in life
A comparison of two methods of estimating propensity scores after multiple imputation
No full textIn many observational studies, analysts estimate treatment effects using propensity scores, e.g., by matching or sub classifying on the scores. When some values of the covariates are missing, analysts can use multiple imputation to fill in the missing data, estimate propensity scores based on the m completed datasets, and use the propensity scores to estimate treatment effects. We compare two approaches to implementing this process. In the first, the analyst estimates the treatment effect using propensity score matching within each completed data set, and averages the m treatment effect estimates. In the second approach, the analyst averages the m propensity scores for each record across the completed datasets, and performs propensity score matching with these averaged scores to estimate the treatment effect. We compare properties of both methods via simulation studies using artificial and real data. The simulations suggest that the second method has greater potential to produce substantial bias reductions than the first
Bilateral disciform keratitis: A rare feature of Reiter′s syndrome
No full textReiter′s syndrome is a relatively rare seronegative spondyloarthropathy characterized by a triad of urethritis, arthritis, and conjunctivitis. Human leukocyte antigen B27 (HLA B27) is positive in over two-thirds of the patients. Involvement of the cornea in the form of a bilateral disciform keratitis in a first episode of Reiter′s is an extremely rare feature, with only one previous report. Other report indicates the occurrence of disciform keratitis in patients with chronic recurring episodes of Reiter′s syndrome. We report acase of a young girl who developed bilateral disciform keratitis against a clinical background of arthritis of the left knee. There was preceding history of acute infective diarrhea, 1 month earlier. Initially, the keratitis was thought to be viral, but response to antiviral treatment was poor. A clinical suspicion of Reiter′s syndrome was confirmed by a positive HLA B27 test. Definitive treatment with steroids and sulfasalazine resulted in resolution of the keratitis
Berliner Papyrus mit Kurzkatalogen berühmter Personen, Kunstwerke und Naturdenkmäler (P. Berl. inv. 13044 Rekto Kol. VI 10 – XII)
No full textIl contributo descrive contenuto e rilevanza del papiro letterario P. Berol. 13044 per la storia culturale
A comparison of two methods of estimating propensity scores after multiple imputation
No full textIn many observational studies, analysts estimate treatment effects using propensity scores, e.g. by matching or sub-classifying on the scores. When some values of the covariates are missing, analysts can use multiple imputation to fill in the missing data, estimate propensity scores based on the m completed datasets, and use the propensity scores to estimate treatment effects. We compare two approaches to implement this process. In the first, the analyst estimates the treatment effect using propensity score matching within each completed data set, and averages the m treatment effect estimates. In the second approach, the analyst averages the m propensity scores for each record across the completed datasets, and performs propensity score matching with these averaged scores to estimate the treatment effect. We compare properties of both methods via simulation studies using artificial and real data. The simulations suggest that the second method has greater potential to produce substantial bias reductions than the first, particularly when the missing values are predictive of treatment assignment
Chromosome Centromeres: Structural and Analytical Investigations with High Resolution Scanning Electron Microscopy in Combination with Focused Ion Beam Milling
No full textWhole mount mitotic metaphase chromosomes of different plants and animals were investigated with high resolution field emission scanning electron microscopy (FESEM) to study the ultrastructural organization of centromeres, including metacentric, acrocentric, telocentric, and holocentric chromosome variants. It could be shown that, in general, primary constrictions have distinctive ultrastructural features characterized by parallel matrix fibrils and fewer smaller chromomeres. Exposure of these structures depends on cell cycle synchronization prior to chromosome isolation, chromosome size, and chromosome isolation technique. Chromosomes without primary constrictions, small chromosomes, and holocentric chromosomes do not exhibit distinct ultrastructural elements that could be directly correlated to centromere function. Putative spindle structures, although rarely observed, spread over the primary constriction to the bordering pericentric regions. Analytical FESEM techniques, including specific DNA staining with Pt blue, staining of protein as a substance class with silver-colloid, and artificial loosening of fixed chromosomes with proteinase K, were applied, showing that centromere variants and ultrastructural elements in the centromere differ in DNA and protein distribution. Immunogold localization allowed high-resolution comparison between chromosomes with different centromere orientations of the distribution of centromere-related histone variants, phosphorylated histone H3 (ser10), and CENH3. A novel application of FESEM combined with focused ion beam milling (FIB) provided new insights into the spatial distribution of these histone variants in barley chromosomes. Copyright (C) 2009 S. Karger AG, Base
Asellia italosomalica De Beaux 1931
No full text<i>Asellia italosomalica</i> De Beaux, 1931 <p>— Somalia (16): 1 ♀ (MSNG 32582 [S+B]), Bender-Cassim, January–February 1932, leg. I. Zanetti; – 1 ♀ (MZUF 8277 [S+A]), Callis, 20 October 1973, leg. Granchi and B. Lanza; – 2 ♂♂, 2 ♀♀ (MSNG 12232a–d [S+B], incl. the paratype of A. tridens italosomalica De Beaux, 1931), Dolo, May–July 1911, leg. C. Citerni; – 2 ♂♂ (MZUF 9940, 9941 [S+A]), Mahas, 12 February 1977, leg. A. Simonetta; – 1 ♀ (MSNG 30942 [S+B], holotype of A. tridens italosomalica De Beaux, 1931), Oddur, 1929, leg. N. Mosconi Bronzi; – 1 ♀ (MZUF 9942 [S+A]), Pozzi di Mahas, 11 April 1977, leg. A. Simonetta; – 1 ♂, 1 ♀ (MZUF 6291 [S+B], 6305 [S+A]), Run, 15 and 18 August 1969, leg. B. Lanza; – 2 ♂♂, 2 ♀♀ (MZUF 13099, 13100 [S+A], 15728, 15734 [S+A]), Showli Berdi, 15 March 1984 and 15 November 1985, leg. L. Chellazi and Messana. – Yemen, Socotra (15): 10 ♀♀ (BCSU pb2718, 2719, 2721, BMNH 54.1010., NMP 90571–90575 [S+A], BCSU pb2720, NMP 90570 [A]), Kam, date unlisted, leg. G. B. Popov, 5 May 2004, leg. P. Benda and A. Reiter; – 4 ♂♂ (BCSU pb2749, NMP 90590, 90592 [S+A], NMP 90591 [A]), Mazaaba, 14 May 2004, leg. P. Benda and A. Reiter; – 1 ♂ (NMP 90579 [S+A]), Suq, 7 May 2004, leg. P. Benda and A. Reiter.</p>Published as part of <i>Benda, Petr, Vallo, Peter & Reiter, Antonín, 2011, Taxonomic revision of the genus Asellia (Chiroptera: Hipposideridae) with a description of a new species from southern Arabia, pp. 245-270 in Acta Chiropterologica 13 (2)</i> on page 266, DOI: 10.3161/150811011X624749, <a href="http://zenodo.org/record/3943317">http://zenodo.org/record/3943317</a>
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