The functional response of a generalist predator

Peer reviewedPublisher PD

Computer modeling of diabetes and Its transparency: a report on the Eighth Mount Hood Challenge

Objectives The Eighth Mount Hood Challenge (held in St. Gallen, Switzerland, in September 2016) evaluated the transparency of model input documentation from two published health economics studies and developed guidelines for improving transparency in the reporting of input data underlying model-based economic analyses in diabetes. Methods Participating modeling groups were asked to reproduce the results of two published studies using the input data described in those articles. Gaps in input data were filled with assumptions reported by the modeling groups. Goodness of fit between the results reported in the target studies and the groups’ replicated outputs was evaluated using the slope of linear regression line and the coefficient of determination (R2). After a general discussion of the results, a diabetes-specific checklist for the transparency of model input was developed. Results Seven groups participated in the transparency challenge. The reporting of key model input parameters in the two studies, including the baseline characteristics of simulated patients, treatment effect and treatment intensification threshold assumptions, treatment effect evolution, prediction of complications and costs data, was inadequately transparent (and often missing altogether). Not surprisingly, goodness of fit was better for the study that reported its input data with more transparency. To improve the transparency in diabetes modeling, the Diabetes Modeling Input Checklist listing the minimal input data required for reproducibility in most diabetes modeling applications was developed. Conclusions Transparency of diabetes model inputs is important to the reproducibility and credibility of simulation results. In the Eighth Mount Hood Challenge, the Diabetes Modeling Input Checklist was developed with the goal of improving the transparency of input data reporting and reproducibility of diabetes simulation model results

Design and Key Features of the PIAAC Survey of Adults

This chapter gives an overview of the most important features of the Programme for the International Assessment of Adult Competencies (PIAAC) survey as it pertains to two main goals. First, only a well-designed survey will lead to accurate and comparable test scores across different countries and languages both within and across assessment cycles. Second, only an understanding of its complex survey design will lead to proper use of the PIAAC data in secondary analyses and meaningful interpretation of results by psychometricians, data analysts, scientists, and policymakers. The chapter begins with a brief introduction to the PIAAC survey followed by an overview of the background questionnaire and the cognitive measures. The cognitive measures are then compared to what was assessed in previous international adult surveys. Key features of the assessment design are discussed followed by a section describing what could be done to improve future PIAAC cycles

The battle over Syria's reconstruction

Reconstruction is becoming the new battleground in the Syrian conflict—its continuation by other means. It is instrumentalized by the regime as a way to reconsolidate its control over the country and by rival regional and international powers to shape the internal balance of power and establish spheres of influence in the country. The paper examines the Asad regime’s practices, including co-optation of militia leaders via reconstruction concessions and use of reconstruction to clear strategic areas of opposition-dominated urban settlements. The paper then surveys how the geopolitical struggle in Syria has produced an asymmetry as regards reconstruction: those powers that lost the geo-political contest on the ground seek to use geo-economic superiority to reverse the geo-political outcome. Then the impact of proxy wars and spheres of influence in the country on the security context for reconstruction is examined. Finally, the reconstruction initiatives of the various external parties are assessed, including Russia, Iran and Turkey as well as the spoiler role by which the US seeks to obstruct reconstruction that would spell victory in Syria for its Russian and Iranian rivals.PostprintPeer reviewe

Evaluating the ability of economic models of diabetes to simulate new cardiovascular outcomes trials : a report on the Ninth Mount Hood Diabetes Challenge

Objectives The cardiovascular outcomes challenge examined the predictive accuracy of 10 diabetes models in estimating hard outcomes in 2 recent cardiovascular outcomes trials (CVOTs) and whether recalibration can be used to improve replication. Methods Participating groups were asked to reproduce the results of the Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients (EMPA-REG OUTCOME) and the Canagliflozin Cardiovascular Assessment Study (CANVAS) Program. Calibration was performed and additional analyses assessed model ability to replicate absolute event rates, hazard ratios (HRs), and the generalizability of calibration across CVOTs within a drug class. Results Ten groups submitted results. Models underestimated treatment effects (ie, HRs) using uncalibrated models for both trials. Calibration to the placebo arm of EMPA-REG OUTCOME greatly improved the prediction of event rates in the placebo, but less so in the active comparator arm. Calibrating to both arms of EMPA-REG OUTCOME individually enabled replication of the observed outcomes. Using EMPA-REG OUTCOME–calibrated models to predict CANVAS Program outcomes was an improvement over uncalibrated models but failed to capture treatment effects adequately. Applying canagliflozin HRs directly provided the best fit. Conclusions The Ninth Mount Hood Diabetes Challenge demonstrated that commonly used risk equations were generally unable to capture recent CVOT treatment effects but that calibration of the risk equations can improve predictive accuracy. Although calibration serves as a practical approach to improve predictive accuracy for CVOT outcomes, it does not extrapolate generally to other settings, time horizons, and comparators. New methods and/or new risk equations for capturing these CV benefits are needed