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Predictive and prognostic models hold great potential to support clinical decision making in oncology and could ultimately facilitate a paradigm shift to a more personalised form of treatment. While a large number of models relevant to the field of oncology have been developed, few have been translated into clinical use and assessment of clinical utility is not currently considered a routine part of model development. In this narrative review of the clinical evaluation of prediction models in oncology, we propose a high-level process diagram for the life cycle of a clinical model, encompassing model commissioning, clinical implementation and ongoing quality assurance, which aims to bridge the gap between model development and clinical implementation.

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BACKGROUND: Data-driven methods such as hierarchical clustering (HC) and principal component analysis (PCA) have been used to identify asthma subtypes, with inconsistent results. OBJECTIVE: To develop a framework for the discovery of stable and clinically meaningful asthma subtypes. METHODS: We performed HC in a rich data set from 613 asthmatic children, using 45 clinical variables (Model 1), and after PCA dimensionality reduction (Model 2). Clinical experts then identified a set of asthma features/domains which informed clusters in the two analyses. In Model 3, we reclustered the data using these features to ascertain whether this improved the discovery process. RESULTS: Cluster stability was poor in Models 1 and 2. Clinical experts highlighted four asthma features/domains which differentiated the clusters in two models: age of onset, allergic sensitization, severity, and recent exacerbations. In Model 3 (HC using these four features), cluster stability improved substantially. The cluster assignment changed, providing more clinically interpretable results. In a 5-cluster model, we labelled the clusters as: "Difficult asthma" (n = 132); "Early-onset mild atopic" (n = 210); "Early-onset mild non-atopic: (n = 153); "Late-onset" (n = 105); and "Exacerbation-prone asthma" (n = 13). Multinomial regression demonstrated that lung function was significantly diminished among children with "Difficult asthma"; blood eosinophilia was a significant feature of "Difficult," "Early-onset mild atopic," and "Late-onset asthma." Children with moderate-to-severe asthma were present in each cluster. CONCLUSIONS AND CLINICAL RELEVANCE: An integrative approach of blending the data with clinical expert domain knowledge identified four features, which may be informative for ascertaining asthma endotypes. These findings suggest that variables which are key determinants of asthma presence, severity, or control may not be the most informative for determining asthma subtypes. Our results indicate that exacerbation-prone asthma may be a separate asthma endotype and that severe asthma is not a single entity, but an extreme end of the spectrum of several different asthma endotypes.

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BACKGROUND: The prevalence of excess body weight, commonly measured as body mass index (BMI)≥25 kg m(-2), has increased substantially in many populations worldwide over the past three decades, but the rate of increase has slowed down in some western populations. OBJECTIVE: We address the hypothesis that the slowing down of BMI trend increases in England reflects a majority sub-population resistant to further BMI elevation. DESIGN: Pseudo-panel data derived from annual cross-sectional surveys, the Health Surveys for England (1992-2010). Trends in median BMI values were explored using regression models with splines, and gender-specific mixture model (latent class analysis) were fit to take an account of increasing BMI distribution variance with time and identify hidden subgroups within the population. SUBJECTS: BMI was available for 164 155 adults (men: 76 382; women: 87 773). RESULTS: Until 2001, the age-adjusted yearly increases in median BMI were 0.140 and 0.139 kg m(-2) for men and women, respectively, decreasing thereafter to 0.073 and 0.055 kg m(-2) (differences between time periods, both P-values<0.0001). The mixture model identified two components--a normal BMI and a high BMI sub-population--the proportions for the latter were 23.5% in men and 33.7% in women. The remaining normal BMI populations were 'resistant' with minimal increases in mean BMI values over time. By age, mean BMI values in the normal BMI sub-population increased greatest between 20 and 34 years for men; for women, the increases were similar throughout age groups (slope differences, P<0.0001). CONCLUSION: In England, recent slowing down of adult BMI trend increases can be explained by two sub-populations--a high BMI sub-population getting 'fatter' and a majority 'resistant' normal BMI sub-population. These findings support a targeted, rather than a population-wide, policy to tackle the determinants of obesity.

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BACKGROUND: Interactions between prognostic and pharmacodynamic (PD) biomarkers have received little attention. METHODS: Prognostic and PD utilities were assessed with linear mixed-effects models using published data on repeated measurements of circulating caspase-cleaved (ctCK18) and total (tCK18) cytokeratin 18, in 57 patients with metastatic colorectal cancer undergoing chemotherapy. RESULTS: The model for tCK18 (but not cCK18) separated the prognostic/PD interaction from the pure prognostic effect, illustrating the principle of dual prognostic and PD characteristics for a given biomarker. CONCLUSION: These models provide the framework for the analysis and interpretation of longitudinal data to detect prognostic/PD biomarker interactions.

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BACKGROUND: Populations are under-served by local health policies and management of resources. This partly reflects a lack of realistically complex models to enable appraisal of a wide range of potential options. Rising computing power coupled with advances in machine learning and healthcare information now enables such models to be constructed and executed. However, such models are not generally accessible to public health practitioners who often lack the requisite technical knowledge or skills. OBJECTIVES: To design and develop a system for creating, executing and analysing the results of simulated public health and healthcare policy interventions, in ways that are accessible and usable by modellers and policy-makers. METHODS: The system requirements were captured and analysed in parallel with the statistical method development for the simulation engine. From the resulting software requirement specification the system architecture was designed, implemented and tested. A model for Coronary Heart Disease (CHD) was created and validated against empirical data. RESULTS: The system was successfully used to create and validate the CHD model. The initial validation results show concordance between the simulation results and the empirical data. CONCLUSIONS: We have demonstrated the ability to connect health policy-modellers and policy-makers in a unified system, thereby making population health models easier to share, maintain, reuse and deploy.

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In applications such as medical statistics and genetics, we encounter situations where a large number of highly correlated predictors explain a response. For example, the response may be a disease indicator and the predictors may be treatment indicators or single nucleotide polymorphisms (SNPs). Constructing a good predictive model in such cases is well studied. Less well understood is how to recover the 'true sparsity pattern', that is finding which predictors have direct effects on the response, and indicating the statistical significance of the results. Restricting attention to binary predictors and response, we study the recovery of the true sparsity pattern using a two-stage method that separates establishing the presence of effects from inferring their exact relationship with the predictors. Simulations and a real data application demonstrate that the method discriminates well between associations and direct effects. Comparisons with lasso-based methods demonstrate favourable performance of the proposed method.

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Despite its relevance to a wide range of technological and fundamental areas, a quantitative understanding of protein surface clustering dynamics is often lacking. In inorganic crystal growth, surface clustering of adatoms is well described by diffusion-aggregation models. In such models, the statistical properties of the aggregate arrays often reveal the molecular scale aggregation processes. We investigate the potential of these theories to reveal hitherto hidden facets of protein clustering by carrying out concomitant observations of lysozyme adsorption onto mica surfaces, using atomic force microscopy, and Monte Carlo simulations of cluster nucleation and growth. We find that lysozyme clusters diffuse across the substrate at a rate that varies inversely with size. This result suggests which molecular scale mechanisms are responsible for the mobility of the proteins on the substrate. In addition the surface diffusion coefficient of the monomer can also be extracted from the comparison between experiments and simulations. While concentrating on a model system of lysozyme-on-mica, this 'proof of concept' study successfully demonstrates the potential of our approach to understand and influence more biomedically applicable protein-substrate couples.

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A methodology for discovering the mechanisms and dynamics of protein clustering on solid surfaces is presented. In situ atomic force microscopy images are quantitatively compared to Monte Carlo simulations using cluster statistics to differentiate various models. We study lysozyme adsorption on mica as a model system and find that all surface-supported clusters are mobile, not just the monomers, with diffusion constant inversely related to cluster size. The surface monomer diffusion constant is measured to be D(1) approximately 9 x 10(-16) cm(2) s(-1), such a low value being difficult to measure using other techniques.

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Radon levels underground in two abandoned mines in Devon, United Kingdom, are reported and analyzed. Extremely high levels have been noted, 7,100,000 Bq m(-3) being the highest level recorded. This is approximately 89 times higher than the highest published radon level for caves and mines in Devon and Cornwall, England, which is 80,000 Bq m(-3). These levels have significant health implications for both casual and occupational mine explorers.

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The concerns over the risks to human health from radon in underground caves are poorly documented, unlike in workplace or domestic environments where exposures are relatively well known. In U.K. caves, radon has been identified as occurring at elevated levels; but with the exception of major show caves, its impact and risk to the many groups who use the caves have thus far received inadequate attention. This paper presents a survey performed in a relatively "low-risk" geographical area of the United Kingdom and quantifies the risk of exposure in this cave environment. Radon levels up to 12,552 Bq m(-3) were measured: Such concentrations are very high but are likely to underestimate the levels in many other parts of the cave system, for reasons associated with cave architecture and meteorology. This study confirms previous workers' conclusions that long-term users of deep caves, as opposed to rock shelters, are at risk. Annual doses to certain groups of cave users have been calculated to be as high as 120 mSv, a very high value. The study also demonstrates that there is variation both within and between caves as a result of subtleties of the bedrock geology, fault patterns, and weathering. This paper sets out a theoretical model.

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The UK Department of Health has recently advised caution with respect to the uncontrolled use of private mobile telecommunication equipment in designated sensitive hospital areas. This advice, which has clear implications for patient safety and well being, was issued following reports of the interference of automatic drug delivery devices, infusion pumps and other sensitive systems. The difficulty of monitoring relatives, visitors and, occasionally, staff who use such mobile telephones in the sensitive areas has led to an initiative by The Princess Margaret Hospital to automatically and continuously monitor the frequencies used by the mobile telephones and to initiate an alarm should such phones be used within a designated area. This paper describes a protocol for the use of these monitors developed in the Princess Margaret Hospital which is designed to minimize the number of monitoring units while providing optimum departmental cover.

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