Calculating partial expected value of perfect information via Monte Carlo sampling algorithms

Partial expected value of perfect information (EVPI) calculations can quantify the value of learning about particular subsets of uncertain parameters in decision models. Published case studies have used different computational approaches. This article examines the computation of partial EVPI estimates via Monte Carlo sampling algorithms. The mathematical definition shows 2 nested expectations, which must be evaluated separately because of the need to compute a maximum between them. A generalized Monte Carlo sampling algorithm uses nested simulation with an outer loop to sample parameters of interest and, conditional upon these, an inner loop to sample remaining uncertain parameters. Alternative computation methods and shortcut algorithms are discussed and mathematical conditions for their use considered. Maxima of Monte Carlo estimates of expectations are biased upward, and the authors show that the use of small samples results in biased EVPI estimates. Three case studies illustrate 1) the bias due to maximization and also the inaccuracy of shortcut algorithms 2) when correlated variables are present and 3) when there is nonlinearity in net benefit functions. If relatively small correlation or nonlinearity is present, then the shortcut algorithm can be substantially inaccurate. Empirical investigation of the numbers of Monte Carlo samples suggests that fewer samples on the outer level and more on the inner level could be efficient and that relatively small numbers of samples can sometimes be used. Several remaining areas for methodological development are set out. A wider application of partial EVPI is recommended both for greater understanding of decision uncertainty and for analyzing research priorities

Prevalence of Obesity and the Relationship between the Body Mass Index and Body Fat: Cross-Sectional, Population-Based Data

Background: Anthropometric measures such as the body mass index (BMI) and waist circumference are widely used as convenient indices of adiposity, yet there are limitations in their estimates of body fat. We aimed to determine the prevalence of obesity using criteria based on the BMI and waist circumference, and to examine the relationship between the BMI and body fat.Methodology/Principal Findings: This population-based, cross-sectional study was conducted as part of the Geelong Osteoporosis Study. A random sample of 1,467 men and 1,076 women aged 20&ndash;96 years was assessed 2001&ndash;2008. Overweight and obesity were identified according to BMI (overweight 25.0&ndash;29.9 kg/m2; obesity 30.0 kg/m2) and waist circumference (overweight men 94.0–101.9 cm; women 80.0–87.9 cm; obesity men 102.0 cm, women $88.0 cm); body fat mass was assessed using dual energy X-ray absorptiometry; height and weight were measured and lifestyle factors documented by self-report. According to the BMI, 45.1% (95%CI 42.4&ndash;47.9) of men and 30.2% (95%CI 27.4&ndash;33.0) of women were overweight and a further 20.2% (95%CI 18.0&ndash;22.4) of men and 28.6% (95%CI 25.8&ndash;31.3) of women were obese. Using waist circumference, 27.5% (95%CI 25.1&ndash;30.0) of men and 23.3% (95%CI 20.8&ndash;25.9) of women were overweight, and 29.3% (95%CI 26.9&ndash;31.7) of men and 44.1% (95%CI 41.2&ndash;47.1) of women, obese. Both criteria indicate that approximately 60% of the population exceeded recommended thresholds for healthy body habitus. There was no consistent pattern apparent between BMI and energy intake. Compared with women, BMI overestimated adiposity in men, whose excess weight was largely attributable to muscular body builds and greater bone mass. BMI also underestimated adiposity in the elderly. Regression models including gender, age and BMI explained 0.825 of the variance in percent body fat.Conclusions/Significance: As the BMI does not account for differences in body composition, we suggest that gender- and age-specific thresholds should be considered when the BMI is used to indicate adiposity.<br /

Investigating organizational quality improvement systems, patient empowerment, organizational culture, professional involvement and the quality of care in European hospitals: the 'Deepening our Understanding of Quality Improvement in Europe (DUQuE)' project

BACKGROUND: Hospitals in European countries apply a wide range of quality improvement strategies. Knowledge of the effectiveness of these strategies, implemented as part of an overall hospital quality improvement system, is limited. METHODS/DESIGN: We propose to study the relationships among organisational quality improvement systems, patient empowerment, organisational culture, professionals' involvement with the quality of hospital care, including clinical effectiveness, patient safety and patient involvement. We will employ a cross-sectional, multi-level study design in which patient-level measurements are nested in hospital departments, which are in turn nested in hospitals in different EU countries. Mixed methods will be used for data collection, measurement and analysis. Hospital/care pathway level constructs that will be assessed include external pressure, hospital governance, quality improvement system, patient empowerment in quality improvement, organisational culture and professional involvement. These constructs will be assessed using questionnaires. Patient-level constructs include clinical effectiveness, patient safety and patient involvement, and will be assessed using audit of patient records, routine data and patient surveys. For the assessment of hospital and pathway level constructs we will collect data from randomly selected hospitals in eight countries. For a sample of hospitals in each country we will carry out additional data collection at patient-level related to four conditions (stroke, acute myocardial infarction, hip fracture and delivery). In addition, structural components of quality improvement systems will be assessed using visits by experienced external assessors. Data analysis will include descriptive statistics and graphical representations and methods for data reduction, classification techniques and psychometric analysis, before moving to bi-variate and multivariate analysis. The latter will be conducted at hospital and multilevel. In addition, we will apply sophisticated methodological elements such as the use of causal diagrams, outcome modelling, double robust estimation and detailed sensitivity analysis or multiple bias analyses to assess the impact of the various sources of bias. DISCUSSION: Products of the project will include a catalogue of instruments and tools that can be used to build departmental or hospital quality and safety programme and an appraisal scheme to assess the maturity of the quality improvement system for use by hospitals and by purchasers to contract hospitals

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Germline selection shapes human mitochondrial DNA diversity.

Approximately 2.4% of the human mitochondrial DNA (mtDNA) genome exhibits common homoplasmic genetic variation. We analyzed 12,975 whole-genome sequences to show that 45.1% of individuals from 1526 mother-offspring pairs harbor a mixed population of mtDNA (heteroplasmy), but the propensity for maternal transmission differs across the mitochondrial genome. Over one generation, we observed selection both for and against variants in specific genomic regions; known variants were more likely to be transmitted than previously unknown variants. However, new heteroplasmies were more likely to match the nuclear genetic ancestry as opposed to the ancestry of the mitochondrial genome on which the mutations occurred, validating our findings in 40,325 individuals. Thus, human mtDNA at the population level is shaped by selective forces within the female germ line under nuclear genetic control, which ensures consistency between the two independent genetic lineages.NIHR, Wellcome Trust, MRC, Genomics Englan

Head and Neck cancer: the possible role of stem cells.

The biology of stem cells and their properties have already been recognized as integral to tumor pathogenesis in several types of cancer. This observation had already a massive impact in the cancer research field and is likely to modify our understanding of the basic mechanisms of tumor iniziation and cancer treating strategies. A role for stem cells has been demonstrated for the hematopoietic system diseases, and breast and brain cancers (Jordan CT, 2004). Going forward it is likely that the possible involvement of stem cells in other malignancies will also be clarified, including head and neck. Head and neck cancer is still the sixth most common cancer type worldwide. Disappointingly, despite significant advances in surgical and other treatments that enhance quality of life, survival rates only have moderately improved during the last 20 years (Forastiere A et al, 2001, Boudewijin JMB et al, 2005). Moreover, head and neck cancer has a severe impact on patients quality of life and the significant morbidity subsequent to treatment often require long term multidisciplinary care. An other major problem of head and neck cancer is that most patients present with an advanced cancer status as the disease often progress without clear clinical symptoms. Therefore, prevention and early diagnosis, as well as an improved comprehension of the cellular-molecular events that regulates the carcinogenesis processes, would help in reducing local morbidity and increasing survival rates