Stereotyping and the treatment of missing data for drug and alcohol clinical trials

Stigma and stereotyping of marginalized groups often is insidious and shows up in unlikely places, for instance in how clinical trials consider dropouts in treatment research. A surprising number of studies presume that people who do not complete the study protocol relapse and code their data as if they had been observed. There is no good statistical rationale for this treatment of missing data and numerous and more defensible alternative methods are available. We need to be mindful about our attitudes and preconceptions about the people we are intending to help. There is no good reason to continue to support science built on this scientifically indefensible stereotyping, however unintentional

Future increased risk from extratropical windstorms in northern Europe

This is the final version. Available on open access from Nature Research via the DOI in this recordData availability@ The ERA5 data were available from the Copernicus data store, https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview, and the CMIP6 model data were available from the Earth System Grid Federation. The generated storm footprints from the current study are available in the GitHub repository, https://github.com/alexslittle/cyclonic-wind-impacts, along with instructions to generate these from the cyclone tracks and the wind speeds.Code availability: The objective feature tracking code belongs to Kevin Hodges and is available from the GitLab repository, https://gitlab.act.reading.ac.uk/track/track. The code to calculate the storm footprints is available from the GitHub repository, https://github.com/alexslittle/cyclonic-wind-impacts.European windstorms cause socioeconomic losses due to wind damage. Projections of future losses from such storms are subject to uncertainties from the frequency and tracks of the storms, their intensities and definitions thereof, and socio-economic scenarios. We use two storm severity indices applied to objectively identified extratropical cyclone footprints from a multi-model ensemble of state-of-the-art climate models under different future socio-economic scenarios. Here we show storm frequency increases across northern and central Europe, where the meteorological storm severity index more than doubles. The population-weighted storm severity index more than triples, due to projected population increases. Adapting to the increasing wind speeds using future damage thresholds, the population weighted storm severity index increases are only partially offset, despite a reduction in the meteorological storm severity through adaptation. Through following lower emissions scenarios, the future increase in risk is reduced, with the population-weighted storm severity index increase more than halved.Natural Environment Research Council (NERC

Meningeal carcinomatosis diagnosed during stroke evaluation in the emergency department

A 70-year-old female presented to the emergency department with a 3-day history of intermittent dysphasia and right facial droop. Computed tomography (CT) and magnetic resonance imaging (MRI) were obtained, and the patient was found to have meningeal carcinomatosis, also known as leptomeningeal metastases. Meningeal carcinomatosis is a rare metastatic complication of some solid tumors and hematopoietic neoplasms, and has a median survival rate of 2.4 months. The role of the emergency physician is to appropriately diagnose this condition, treat emergent side effects, provide symptomatic relief, and ensure multi-disciplinary management

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Comparison of methods for handling missing data on immunohistochemical markers in survival analysis of breast cancer

Background:Tissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are particularly prone to missingness. A variety of methods to deal with missing data are available. However, the validity of the various approaches is dependent on the structure of the missing data and there are few empirical studies dealing with missing data from molecular pathology. The purpose of this study was to investigate the results of four commonly used approaches to handling missing data from a large, multi-centre study of the molecular pathological determinants of prognosis in breast cancer.Patients and Methods:We pooled data from over 11 000 cases of invasive breast cancer from five studies that collected information on seven prognostic indicators together with survival time data. We compared the results of a multi-variate Cox regression using four approaches to handling missing data-complete case analysis (CCA), mean substitution (MS) and multiple imputation without inclusion of the outcome (MI) and multiple imputation with inclusion of the outcome (MI). We also performed an analysis in which missing data were simulated under different assumptions and the results of the four methods were compared.Results:Over half the cases had missing data on at least one of the seven variables and 11 percent had missing data on 4 or more. The multi-variate hazard ratio estimates based on multiple imputation models were very similar to those derived after using MS, with similar standard errors. Hazard ratio estimates based on the CCA were only slightly different, but the estimates were less precise as the standard errors were large. However, in data simulated to be missing completely at random (MCAR) or missing at random (MAR), estimates for MI were least biased and most accurate, whereas estimates for CCA were most biased and least accurate.Conclusion:In this study, empirical results from analyses using CCA, MS, MI and MI were similar, although results from CCA were less precise. The results from simulations suggest that in general MI is likely to be the best. Given the ease of implementing MI in standard statistical software, the results of MI and CCA should be compared in any multi-variate analysis where missing data are a problem. © 2011 Cancer Research UK. All rights reserved

Higher cost of implementing Xpert(®) MTB/RIF in Ugandan peripheral settings: implications for cost-effectiveness.

SETTING: Initial cost-effectiveness evaluations of Xpert(®) MTB/RIF for tuberculosis (TB) diagnosis have not fully accounted for the realities of implementation in peripheral settings. OBJECTIVE: To evaluate costs and diagnostic outcomes of Xpert testing implemented at various health care levels in Uganda. DESIGN: We collected empirical cost data from five health centers utilizing Xpert for TB diagnosis, using an ingredients approach. We reviewed laboratory and patient records to assess outcomes at these sites and10 sites without Xpert. We also estimated incremental cost-effectiveness of Xpert testing; our primary outcome was the incremental cost of Xpert testing per newly detected TB case. RESULTS: The mean unit cost of an Xpert test was US$21 based on a mean monthly volume of 54 tests per site, although unit cost varied widely (US$16-58) and was primarily determined by testing volume. Total diagnostic costs were 2.4-fold higher in Xpert clinics than in non-Xpert clinics; however, Xpert only increased diagnoses by 12%. The diagnostic costs of Xpert averaged US$119 per newly detected TB case, but were as high as US$885 at the center with the lowest volume of tests. CONCLUSION: Xpert testing can detect TB cases at reasonable cost, but may double diagnostic budgets for relatively small gains, with cost-effectiveness deteriorating with lower testing volumes

Periodic trends and easy estimation of relative stabilities in 11-vertex nido-p-block-heteroboranes and -borates

Density functional theory computations were carried out for 11-vertex nido-p-block-hetero(carba)boranes and -borates containing silicon, germanium, tin, arsenic, antimony, sulfur, selenium and tellurium heteroatoms. A set of quantitative values called “estimated energy penalties” was derived by comparing the energies of two reference structures that differ with respect to one structural feature only. These energy penalties behave additively, i.e., they allow us to reproduce the DFT-computed relative stabilities of 11-vertex nido-heteroboranes in general with good accuracy and to predict the thermodynamic stabilities of unknown structures easily. Energy penalties for neighboring heteroatoms (HetHet and HetHet′) decrease down the group and increase along the period (indirectly proportional to covalent radii). Energy penalties for a five- rather than four-coordinate heteroatom, [Het5k(1) and Het5k(2)], generally, increase down group 14 but decrease down group 16, while there are mixed trends for group 15 heteroatoms. The sum of HetHet′ energy penalties results in different but easily predictable open-face heteroatom positions in the thermodynamically most stable mixed heterocarbaboranes and -borates with more than two heteroatoms

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Structural and biochemical characterization of the exopolysaccharide deacetylase Agd3 required for Aspergillus fumigatus biofilm formation

The exopolysaccharide galactosaminogalactan (GAG) is an important virulence factor of the fungal pathogen Aspergillus fumigatus. Deletion of a gene encoding a putative deacetylase, Agd3, leads to defects in GAG deacetylation, biofilm formation, and virulence. Here, we show that Agd3 deacetylates GAG in a metal-dependent manner, and is the founding member of carbohydrate esterase family CE18. The active site is formed by four catalytic motifs that are essential for activity. The structure of Agd3 includes an elongated substrate-binding cleft formed by a carbohydrate binding module (CBM) that is the founding member of CBM family 87. Agd3 homologues are encoded in previously unidentified putative bacterial exopolysaccharide biosynthetic operons and in other fungal genomes. The exopolysaccharide galactosaminogalactan (GAG) is an important virulence factor of the fungal pathogen Aspergillus fumigatus. Here, the authors study an A. fumigatus enzyme that deacetylates GAG in a metal-dependent manner and constitutes a founding member of a new carbohydrate esterase family.Bio-organic Synthesi

Systems biological and mechanistic modelling of radiation-induced cancer

This paper summarises the five presentations at the First International Workshop on Systems Radiation Biology that were concerned with mechanistic models for carcinogenesis. The mathematical description of various hypotheses about the carcinogenic process, and its comparison with available data is an example of systems biology. It promises better understanding of effects at the whole body level based on properties of cells and signalling mechanisms between them. Of these five presentations, three dealt with multistage carcinogenesis within the framework of stochastic multistage clonal expansion models, another presented a deterministic multistage model incorporating chromosomal aberrations and neoplastic transformation, and the last presented a model of DNA double-strand break repair pathways for second breast cancers following radiation therapy