Funnel plots for institutional comparisons

We introduce -funnelcompar-, a Stata routine that performs the analysis suggested by David J. Spiegelhalter (Funnel plots for comparing institutional performance, Statistics in Medicine, Volume 24 Issue 8, 1185-1202). The basic idea in funnel plot is to plot performance indicators against a measure of their precision in order to detect outliers. A scatter plot of an indicator level is plotted together with a baseline and control limits, that shrinks as the sample size gets bigger. Our command performs funnel plots for binomial (proportion) poisson (crude and standardized rates) and normal (means) distributed variables. The baseline (and stan- dard errors in case of normal variables) can either be specified by the user (for instance as literature reference) or be estimated from the data as a weighted or non-weighted mean of the data. By default confidence limits are plotted at 2 and 3 standard error, in order to detect alarm and alert signals, as recommended by statistical process control theory. Options have been implemented to mark single institutions, groups of institutions or those institutions lying outside control limits. These plots are increasingly used to report performance indicators at institutional level. Classical league tables imply the existence of ranking between institutions and implicitly support the idea that some of them are worse/better than others. A different approach is possible using statistical process control theory: all institutions are part of a single system and perform at the same level. Observed differences can never be completely eliminated and are explained by chance (common cause variation). If ob- served variation exceed that expected, special-cause variation exists and requires further explanation to identify its cause.

SAI, a Sensible Artificial Intelligence that plays Go

We propose a multiple-komi modification of the AlphaGo Zero/Leela Zero paradigm. The winrate as a function of the komi is modeled with a two-parameters sigmoid function, so that the neural network must predict just one more variable to assess the winrate for all komi values. A second novel feature is that training is based on self-play games that occasionally branch -- with changed komi -- when the position is uneven. With this setting, reinforcement learning is showed to work on 7x7 Go, obtaining very strong playing agents. As a useful byproduct, the sigmoid parameters given by the network allow to estimate the score difference on the board, and to evaluate how much the game is decided.Comment: Updated for IJCNN 2019 conferenc

Reduction of Vaisman structures in complex and quaternionic geometry

We consider locally conformal Kaehler geometry as an equivariant (homothetic) Kaehler geometry: a locally conformal Kaehler manifold is, up to equivalence, a pair (K,\Gamma) where K is a Kaehler manifold and \Gamma a discrete Lie group of biholomorphic homotheties acting freely and properly discontinuously. We define a new invariant of a locally conformal Kaehler manifold (K,\Gamma) as the rank of a natural quotient of \Gamma, and prove its invariance under reduction. This equivariant point of view leads to a proof that locally conformal Kaehler reduction of compact Vaisman manifolds produces Vaisman manifolds and is equivalent to a Sasakian reduction. Moreover we define locally conformal hyperkaehler reduction as an equivariant version of hyperkaehler reduction and in the compact case we show its equivalence with 3-Sasakian reduction. Finally we show that locally conformal hyperkaehler reduction induces hyperkaehler with torsion (HKT) reduction of the associated HKT structure and the two reductions are compatible, even though not every HKT reduction comes from a locally conformal hyperkaehler reduction.Comment: 29 pages; Section 4 changed (and accordingly the Introduction); Remark 8.2 added; References update

The impact of different rehabilitation strategies after major events in the elderly: the case of stroke and hip fracture in the Tuscany region

<p>Abstract</p> <p>Background</p> <p>On a regional level, our aims were to describe rehabilitation patterns for elderly patients with stroke and hip fracture and to investigate mortality risk during the 6-month post acute period.</p> <p>Methods</p> <p>Data sources included administrative data relative to patients aged 65+ resident in Tuscany admitted in hospital for stroke or hip fracture between 2001 and 2003, traced up to 3 years before and 6 months following index admission. The study design involves computerized linkage of administrative data, and an exploratory analysis of the association between rehabilitation patterns and 6-month mortality, adjusting for clinical, demographic, and acute-related care characteristics using multivariate Cox regression.</p> <p>Results</p> <p>Rehabilitation patterns vary greatly across Tuscany with considerable cost implications. Six month mortality risk for stroke patients is significantly lower among residents of Local Health Authorities where patients are more frequently rehabilitated, specifically in extra-hospital settings.</p> <p>Conclusion</p> <p>Our study, targeting two crucial conditions for elderly patients, found a high variability of rehabilitation patterns across a region, albeit coherent between the two pathologies, associated with remarkable differences in average expenditure. Differences in hazard rates for 6-month mortality after stroke at population level were also found. These results need to be confirmed and further investigated through a more robust information framework.</p

Development and validation of a case-finding algorithm for the identification of non-small cell lung cancers in a region-wide Italian pathology registry

PurposeTo develop and validate a case-finding algorithm for the identification of Non-Small Cell Lung Cancer (NSCLC) cases in a region-wide Italian pathology registry (PR).Materials and methodsData collected between 2009 and 2017 in the PR and the Pharmacy Database of the University Hospital of Siena and the PR of Tuscany region were used. A NSCLC-identification algorithm based on free-text keywords and SNOMED morphology and topography codes was designed and tested on data from Siena: indication for drug use (i.e. NSCLC) was the reference standard for sensitivity (SE); positive predictive value (PPV) was estimated through manual review. Algorithm modifications were then tested to improve algorithm performance: PPV was calculated against validated dataset from PR of Siena; a range of SE [min-max] was estimated in PR of Tuscany using analytical formulae that assumed NSCLC incidence equal either to 80% or 90% of overall lung cancer incidence recorded in Tuscany. The algorithm modification with the best performance was chosen as the final version of the algorithm. A random sample of 200 cases was extracted from the PR of Tuscany for manual review.ResultsThe first version of the algorithm showed a PPV of 74.7% and SE of 79% in PR of Siena. The final version of the algorithm had a SE in PR of Tuscany that grew with calendar time (2009 = [24.7%-28%]; 2017 = [57.9%-65.1%]) and a PPV of 93%.ConclusionsThe final NSCLC-finding algorithm showed with very high PPV. SE was in line with the expected contribution of PR to overall cases captured in the regional Cancer Registry, with a trend of increase over calendar time. Given the promising algorithm validity and the wide use of SNOMED terminology in electronic pathology records, the proposed algorithm is expected to be easily adapted to other electronic databases for (pharmaco)epidemiology purposes