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

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results: Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies

Frontotemporal dementia caused by CHMP2B mutation is characterised by neuronal lysosomal storage pathology

Mutations in the charged multivesicular body protein 2B (CHMP2B) cause frontotemporal dementia (FTD). We report that mice which express FTD-causative mutant CHMP2B at physiological levels develop a novel lysosomal storage pathology characterised by large neuronal autofluorescent aggregates. The aggregates are an early and progressive pathology that occur at 3 months of age and increase in both size and number over time. These autofluorescent aggregates are not observed in mice expressing wild-type CHMP2B, or in non-transgenic controls, indicating that they are a specific pathology caused by mutant CHMP2B. Ultrastructural analysis and immuno- gold labelling confirmed that they are derived from the endolysosomal system. Consistent with these findings, CHMP2B mutation patient brains contain morphologically similar autofluorescent aggregates. These aggregates occur significantly more frequently in human CHMP2B mutation brain than in neurodegenerative disease or age-matched control brains. These data suggest that lysosomal storage pathology is the major neuronal pathology in FTD caused by CHMP2B mutation. Recent evidence suggests that two other genes associated with FTD, GRN and TMEM106B are important for lysosomal function. Our identification of lysosomal storage pathology in FTD caused by CHMP2B mutation now provides evidence that endolysosomal dysfunction is a major degenerative pathway in FTD

A correlation between the intrinsic brightness and average decay rate of Swift/UVOT gamma-ray burst optical/ultraviolet light curves

We examine a sample of 48 Swift/UVOT long gamma-ray burst light curves and find a correlation between the logarithmic luminosity at 200 s and average decay rate determined from 200 s onwards, with a Spearman rank coefficient of −0.58 at a significance of 99.998 per cent (4.2σ). We discuss the causes of the log L200 s–α>200 s correlation, finding it to be an intrinsic property of long gamma-ray bursts, and not resulting from the selection criteria. We find two ways to produce the correlation. One possibility is that there is some property of the central engine, outflow or external medium that affects the rate of energy release so that the bright afterglows release their energy more quickly and decay faster than the fainter afterglows. Alternatively, the correlation may be produced by variation of the observer’s viewing angle, with observers at large viewing angles observing fainter and slower decaying light curves

Comparison of imputation methods for handling missing covariate data when fitting a Cox proportional hazards model: a resampling study

<p>Abstract</p> <p>Background</p> <p>The appropriate handling of missing covariate data in prognostic modelling studies is yet to be conclusively determined. A resampling study was performed to investigate the effects of different missing data methods on the performance of a prognostic model.</p> <p>Methods</p> <p>Observed data for 1000 cases were sampled with replacement from a large complete dataset of 7507 patients to obtain 500 replications. Five levels of missingness (ranging from 5% to 75%) were imposed on three covariates using a missing at random (MAR) mechanism. Five missing data methods were applied; a) complete case analysis (CC) b) single imputation using regression switching with predictive mean matching (SI), c) multiple imputation using regression switching imputation, d) multiple imputation using regression switching with predictive mean matching (MICE-PMM) and e) multiple imputation using flexible additive imputation models. A Cox proportional hazards model was fitted to each dataset and estimates for the regression coefficients and model performance measures obtained.</p> <p>Results</p> <p>CC produced biased regression coefficient estimates and inflated standard errors (SEs) with 25% or more missingness. The underestimated SE after SI resulted in poor coverage with 25% or more missingness. Of the MI approaches investigated, MI using MICE-PMM produced the least biased estimates and better model performance measures. However, this MI approach still produced biased regression coefficient estimates with 75% missingness.</p> <p>Conclusions</p> <p>Very few differences were seen between the results from all missing data approaches with 5% missingness. However, performing MI using MICE-PMM may be the preferred missing data approach for handling between 10% and 50% MAR missingness.</p

IgA in the horse: cloning of equine polymeric Ig receptor and J chain and characterization of recombinant forms of equine IgA

As in other mammals, immunoglobulin A (IgA) in the horse has a key role in immune defense. To better dissect equine IgA function, we isolated complementary DNA (cDNA) clones for equine J chain and polymeric Ig receptor (pIgR). When coexpressed with equine IgA, equine J chain promoted efficient IgA polymerization. A truncated version of equine pIgR, equivalent to secretory component, bound with nanomolar affinity to recombinant equine and human dimeric IgA but not with monomeric IgA from either species. Searches of the equine genome localized equine J chain and pIgR to chromosomes 3 and 5, respectively, with J chain and pIgR coding sequence distributed across 4 and 11 exons, respectively. Comparisons of transcriptional regulatory sequences suggest that horse and human pIgR expression is controlled through common regulatory mechanisms that are less conserved in rodents. These studies pave the way for full dissection of equine IgA function and open up possibilities for immune-based treatment of equine diseases

Millisecond Oscillations in X-Ray Binaries

The first millisecond X-ray variability phenomena from accreting compact objects have recently been discovered with the Rossi X-ray Timing Explorer. Three new phenomena are observed from low-mass X-ray binaries containing low-magnetic-field neutron stars: millisecond pulsations, burst oscillations and kiloHertz quasi-periodic oscillations. Models for these new phenomena involve the neutron star spin, and orbital motion closely around the neutron star and rely explicitly on our understanding of strong gravity and dense matter. I review the observations of these new neutron-star phenomena and possibly related ones in black-hole candidates, and describe the attempts to use them to perform measurements of fundamental physical interest in these systems.Comment: 40 pages, 17 figures, 4 tables - submitted to the Annual Review of Astronomy and Astrophysics; to appear September 200

Dental management considerations for the patient with an acquired coagulopathy. Part 1: Coagulopathies from systemic disease

Current teaching suggests that many patients are at risk for prolonged bleeding during and following invasive dental procedures, due to an acquired coagulopathy from systemic disease and/or from medications. However, treatment standards for these patients often are the result of long-standing dogma with little or no scientific basis. The medical history is critical for the identification of patients potentially at risk for prolonged bleeding from dental treatment. Some time-honoured laboratory tests have little or no use in community dental practice. Loss of functioning hepatic, renal, or bone marrow tissue predisposes to acquired coagulopathies through different mechanisms, but the relationship to oral haemostasis is poorly understood. Given the lack of established, science-based standards, proper dental management requires an understanding of certain principles of pathophysiology for these medical conditions and a few standard laboratory tests. Making changes in anticoagulant drug regimens are often unwarranted and/or expensive, and can put patients at far greater risk for morbidity and mortality than the unlikely outcome of postoperative bleeding. It should be recognised that prolonged bleeding is a rare event following invasive dental procedures, and therefore the vast majority of patients with suspected acquired coagulopathies are best managed in the community practice setting