Elucidating the aetiology of human Campylobacter coli infections

Peer reviewedPublisher PD

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

How do you say ‘hello’? Personality impressions from brief novel voices

On hearing a novel voice, listeners readily form personality impressions of that speaker. Accurate or not, these impressions are known to affect subsequent interactions; yet the underlying psychological and acoustical bases remain poorly understood. Furthermore, hitherto studies have focussed on extended speech as opposed to analysing the instantaneous impressions we obtain from first experience. In this paper, through a mass online rating experiment, 320 participants rated 64 sub-second vocal utterances of the word ‘hello’ on one of 10 personality traits. We show that: (1) personality judgements of brief utterances from unfamiliar speakers are consistent across listeners; (2) a two-dimensional ‘social voice space’ with axes mapping Valence (Trust, Likeability) and Dominance, each driven by differing combinations of vocal acoustics, adequately summarises ratings in both male and female voices; and (3) a positive combination of Valence and Dominance results in increased perceived male vocal Attractiveness, whereas perceived female vocal Attractiveness is largely controlled by increasing Valence. Results are discussed in relation to the rapid evaluation of personality and, in turn, the intent of others, as being driven by survival mechanisms via approach or avoidance behaviours. These findings provide empirical bases for predicting personality impressions from acoustical analyses of short utterances and for generating desired personality impressions in artificial voices

Novel Role of Y1 Receptors in the Coordinated Regulation of Bone and Energy Homeostasis

The importance of neuropeptide Y (NPY) and Y2 receptors in the regulation of bone and energy homeostasis has recently been demonstrated. However, the contributions of the other Y recep- tors are less clear. Here we show that Y1 receptors are expressed on osteoblastic cells. Moreover, bone and adipose tissue mass are elevated in Y1/ mice with a generalized increase in bone formation on cortical and cancellous surfaces. Importantly, the inhibitory effects of NPY on bone marrow stromal cells in vitro are absent in cells derived from Y1/ mice, indicating a direct action of NPY on bone cells via this Y receptor. Interestingly, in contrast to Y2 receptor or germ line Y1 receptor deletion, con- ditional deletion of hypothalamic Y1 receptors in adult mice did not alter bone homeostasis, food intake, or adiposity. Further- more, deletion of both Y1 and Y2 receptors did not produce additive effects in bone or adiposity. Thus Y1 receptor pathways act powerfully to inhibit bone production and adiposity by non- hypothalamic pathways, with potentially direct effects on bone tissue through a single pathway with Y2 receptors

Is there evidence for accelerated polyethylene wear in uncemented compared to cemented acetabular components? A systematic review of the literature

Joint arthroplasty registries show an increased rate of aseptic loosening in uncemented acetabular components as compared to cemented acetabular components. Since loosening is associated with particulate wear debris, we postulated that uncemented acetabular components demonstrate a higher polyethylene wear rate than cemented acetabular components in total hip arthroplasty. We performed a systematic review of the peer-reviewed literature, comparing the wear rate in uncemented and cemented acetabular components in total hip arthroplasty. Studies were identified using MEDLINE (PubMed), EMBASE and the Cochrane Central Register of Controlled Trials. Study quality was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. The search resulted in 425 papers. After excluding duplicates and selection based on title and abstracts, nine studies were found eligible for further analysis: two randomised controlled trials, and seven observational studies. One randomised controlled trial found a higher polyethylene wear rate in uncemented acetabular components, while the other found no differences. Three out of seven observational studies showed a higher polyethylene wear in uncemented acetabular component fixation; the other four studies did not show any differences in wear rates. The available evidence suggests that a higher annual wear rate may be encountered in uncemented acetabular components as compared to cemented components

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

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

Wake up, wake up! It's me! It's my life! patient narratives on person-centeredness in the integrated care context: a qualitative study

Person-centered care emphasizes a holistic, humanistic approach that puts patients first, at the center of medical care. Person-centeredness is also considered a core element of integrated care. Yet typologies of integrated care mainly describe how patients fit within integrated services, rather than how services fit into the patient's world. Patient-centeredness has been commonly defined through physician's behaviors aimed at delivering patient-centered care. Yet, it is unclear how 'person-centeredness' is realized in integrated care through the patient voice. We aimed to explore patient narratives of person-centeredness in the integrated care context

Prefracture functional level evaluated by the New Mobility Score predicts in-hospital outcome after hip fracture surgery

BACKGROUND AND PURPOSE: Clinicians need valid and easily applicable predictors of outcome in patients with hip fracture. Adjusting for previously established predictors, we determined the predictive value of the New Mobility score (NMS) for in-hospital outcome in patients with hip fracture. PATIENTS AND METHODS: We studied 280 patients with a median age of 81 (interquartile range 72-86) years who were admitted from their own homes to a special hip fracture unit. Main outcome was the regain of independence in basic mobility, defined as. independence in getting in and out of bed, sitting down and standing up from a chair, and walking with an appropriate walking aid. The Cumulated Ambulation score was used to evaluate basic mobility. Predictor variables were NMS functional level before fracture, age, sex, fracture type, and mental and health status. RESULTS: Except for sex, all predictor variables were statistically significant in univariate testing. In multiple logistic regression analysis, only age, NMS functional level before fracture, and fracture type were significant. Thus, patients with a low prefracture NMS and/or an intertrochanteric fracture would be 18 and 4 times more likely not to regain independence in basic mobility during the hospital stay, respectively, than patients with a high prefracture level and a cervical fracture, respectively. The model was statistically stable and correctly classified 84% of cases. INTERPRETATION: The NMS functional level before fracture, age, and fracture type facilitate prediction of the in-hospital rehabilitation potential after hip fracture surgery