Parametric methods outperformed non-parametric methods in comparisons of discrete numerical variables

<p>Abstract</p> <p>Background</p> <p>The number of events per individual is a widely reported variable in medical research papers. Such variables are the most common representation of the general variable type called discrete numerical. There is currently no consensus on how to compare and present such variables, and recommendations are lacking. The objective of this paper is to present recommendations for analysis and presentation of results for discrete numerical variables.</p> <p>Methods</p> <p>Two simulation studies were used to investigate the performance of hypothesis tests and confidence interval methods for variables with outcomes {0, 1, 2}, {0, 1, 2, 3}, {0, 1, 2, 3, 4}, and {0, 1, 2, 3, 4, 5}, using the difference between the means as an effect measure.</p> <p>Results</p> <p>The Welch U test (the T test with adjustment for unequal variances) and its associated confidence interval performed well for almost all situations considered. The Brunner-Munzel test also performed well, except for small sample sizes (10 in each group). The ordinary T test, the Wilcoxon-Mann-Whitney test, the percentile bootstrap interval, and the bootstrap-<it>t </it>interval did not perform satisfactorily.</p> <p>Conclusions</p> <p>The difference between the means is an appropriate effect measure for comparing two independent discrete numerical variables that has both lower and upper bounds. To analyze this problem, we encourage more frequent use of parametric hypothesis tests and confidence intervals.</p

Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis

Objective To examine the dose-response associations between accelerometer assessed total physical activity, different intensities of physical activity, and sedentary time and all cause mortality. Design Systematic review and harmonised meta-analysis. Data sources PubMed, PsycINFO, Embase, Web of Science, Sport Discus from inception to 31 July 2018. Eligibility criteria Prospective cohort studies assessing physical activity and sedentary time by accelerometry and associations with all cause mortality and reported effect estimates as hazard ratios, odds ratios, or relative risks with 95% confidence intervals. Data extraction and analysis Guidelines for meta-analyses and systematic reviews for observational studies and PRISMA guidelines were followed. Two authors independently screened the titles and abstracts. One author performed a full text review and another extracted the data. Two authors independently assessed the risk of bias. Individual level participant data were harmonised and analysed at study level. Data on physical activity were categorised by quarters at study level, and study specific associations with all cause mortality were analysed using Cox proportional hazards regression analyses. Study specific results were summarised using random effects meta-analysis. Main outcome measure All cause mortality. Results 39 studies were retrieved for full text review; 10 were eligible for inclusion, three were excluded owing to harmonisation challenges (eg, wrist placement of the accelerometer), and one study did not participate. Two additional studies with unpublished mortality data were also included. Thus, individual level data from eight studies (n=36 383; mean age 62.6 years; 72.8% women), with median follow-up of 5.8 years (range 3.0-14.5 years) and 2149 (5.9%) deaths were analysed. Any physical activity, regardless of intensity, was associated with lower risk of mortality, with a non-linear dose-response. Hazards ratios for mortality were 1.00 (referent) in the first quarter (least active), 0.48 (95% confidence interval 0.43 to 0.54) in the second quarter, 0.34 (0.26 to 0.45) in the third quarter, and 0.27 (0.23 to 0.32) in the fourth quarter (most active). Corresponding hazards ratios for light physical activity were 1.00, 0.60 (0.54 to 0.68), 0.44 (0.38 to 0.51), and 0.38 (0.28 to 0.51), and for moderate-to-vigorous physical activity were 1.00, 0.64 (0.55 to 0.74), 0.55 (0.40 to 0.74), and 0.52 (0.43 to 0.61). For sedentary time, hazards ratios were 1.00 (referent; least sedentary), 1.28 (1.09 to 1.51), 1.71 (1.36 to 2.15), and 2.63 (1.94 to 3.56). Conclusion Higher levels of total physical activity, at any intensity, and less time spent sedentary, are associated with substantially reduced risk for premature mortality, with evidence of a non-linear dose-response pattern in middle aged and older adults. Systematic review registration PROSPERO CRD42018091808

Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images

Glaucoma is one of the leading causes of blindness. There is no cure for glaucoma but detection at its earliest stage and subsequent treatment can aid patients to prevent blindness. Currently, optic disc and retinal imaging facilitates glaucoma detection but this method still requires manual post-imaging modifications that are time-consuming and do not totally remove subjectivity in image assessment. Therefore, it is necessary to automate this process. In this work, we have first proposed a novel computer aided approach for automatic glaucoma detection based on Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from all the existing methods, our approach can extract both geometric (e.g. morphometric properties) and non-geometric based properties (e.g. pixel appearance/intensity values, texture) from images and significantly increase the classification performance. Our proposed approach consists of three new major contributions including automatic localisation of optic disc, automatic segmentation of disc, and classification between normal and glaucoma based on geometric and non-geometric properties of different regions of an image. We have compared our method with existing approaches and tested it on both fundus and Scanning laser ophthalmoscopy (SLO) images. The experimental results show that our proposed approach outperforms the state-of-the-art approaches using either geometric or non-geometric properties. The overall glaucoma classification accuracy for fundus is 94.4% and accuracy of detection of suspicion of glaucoma in SLO images is 93.9%

Cyst fluid CEA concentration discriminates between benign and premalignant/malignant pancreatic cystic lesions. A prospective cohort study. Short title: Diagnostic yield of pancreatic cyst fluid CEA analysis

Background: The objective was to evaluate cyst fluid carcinoembryonic antigen (CEA) as a putative marker of malignant potential in cystic pancreatic lesions (CPLs). Methods: Prospective observational cohort study from October 2008 to September 2013. A total of 190 patients with CPL were in- cluded after signed informed consent. Endoscopic ultrasound and fine needle aspiration (EUS-FNA) of cyst fluid for analysis of CEA was performed as part of the multimodal preoperative workup. The diagnostic performance of cyst fluid CEA value for the selection of the right patients for surgery was evaluated by histological diagnosis as endpoint in operated patients. Diagnostic accuracy of cyst fluid CEA was assessed by receiver operating characteristics (ROC) analysis. Results: Surgical resection was performed in 65 patients (34.2%) after evaluation by the multidisciplinary team (MDT). Lesions with malignant potential or invasive carcinomas were found in 46 (70.8%) of resected cases. Area under the ROC curve (AUC) for cyst fluid CEA was 0.71 (95% CI 0.61 - 0.81). The optimal cut-off value was 36.3 ng/mL (sensitivity 74%, specificity 62%, PPV 51%, NPV 81%, accuracy 66%). A total of 125 patients (65.8%) were never operated, 15 because of unresectable carcinoma. None of the 110 patients undergoing conservative management developed malignancy at a median follow-up of 46.5 months (range 4 - 86 months). Conclusion: There is a significant diagnostic yield of cyst fluid CEA determination, when the indication for surgical exploration is focused

Grid multi-category response logistic models

BACKGROUND: Multi-category response models are very important complements to binary logistic models in medical decision-making. Decomposing model construction by aggregating computation developed at different sites is necessary when data cannot be moved outside institutions due to privacy or other concerns. Such decomposition makes it possible to conduct grid computing to protect the privacy of individual observations. METHODS: This paper proposes two grid multi-category response models for ordinal and multinomial logistic regressions. Grid computation to test model assumptions is also developed for these two types of models. In addition, we present grid methods for goodness-of-fit assessment and for classification performance evaluation. RESULTS: Simulation results show that the grid models produce the same results as those obtained from corresponding centralized models, demonstrating that it is possible to build models using multi-center data without losing accuracy or transmitting observation-level data. Two real data sets are used to evaluate the performance of our proposed grid models. CONCLUSIONS: The grid fitting method offers a practical solution for resolving privacy and other issues caused by pooling all data in a central site. The proposed method is applicable for various likelihood estimation problems, including other generalized linear models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-015-0133-y) contains supplementary material, which is available to authorized users