On the significance of omitted variables in intersection crash modeling

Advances in safety research—trying to improve the collective understanding of motor vehicle crash causes and contributing factors—rest upon the pursuit of numerous lines of research inquiry. The research community has focused considerable attention on analytical methods development (negative binomial models, simultaneous equations, etc.), on better experimental designs (before-after studies, comparison sites, etc.), on improving exposure measures, and on model specification improvements (additive terms, non-linear relations, etc.). One might logically seek to know which lines of inquiry might provide the most significant improvements in understanding crash causation and/or prediction. It is the contention of this paper that the exclusion of important variables (causal or surrogate measures of causal variables) cause omitted variable bias in model estimation and is an important and neglected line of inquiry in safety research. In particular, spatially related variables are often difficult to collect and omitted from crash models—but offer significant opportunities to better understand contributing factors and/or causes of crashes. This study examines the role of important variables (other than Average Annual Daily Traffic (AADT)) that are generally omitted from intersection crash prediction models. In addition to the geometric and traffic regulatory information of intersection, the proposed model includes many spatial factors such as local influences of weather, sun glare, proximity to drinking establishments, and proximity to schools—representing a mix of potential environmental and human factors that are theoretically important, but rarely used. Results suggest that these variables in addition to AADT have significant explanatory power, and their exclusion leads to omitted variable bias. Provided is evidence that variable exclusion overstates the effect of minor road AADT by as much as 40% and major road AADT by 14%.

Macrophage and mast-cell invasion of tumor cell islets confers a marked survival advantage in non-small-cell lung cancer

Purpose The role played by the innate immune system in determining survival from non-small-cell lung cancer (NSCLC) is unclear. The aim of this study was to investigate the prognostic significance of macrophage and mast-cell infiltration in NSCLC. Methods We used immunohistochemistry to identify tryptase+ mast cells and CD68+ macrophages in the tumor stroma and tumor islets in 175 patients with surgically resected NSCLC. Results Macrophages were detected in both the tumor stroma and islets in all patients. Mast cells were detected in the stroma and islets in 99.4% and 68.5% of patients, respectively. Using multivariate Cox proportional hazards analysis, increasing tumor islet macrophage density (P < .001) and tumor islet/stromal macrophage ratio (P < .001) emerged as favorable independent prognostic indicators. In contrast, increasing stromal macrophage density was an independent predictor of reduced survival (P = .001). The presence of tumor islet mast cells (P = .018) and increasing islet/stromal mast-cell ratio (P = .032) were also favorable independent prognostic indicators. Macrophage islet density showed the strongest effect: 5-year survival was 52.9% in patients with an islet macrophage density greater than the median versus 7.7% when less than the median (P < .0001). In the same groups, respectively, median survival was 2,244 versus 334 days (P < .0001). Patients with a high islet macrophage density but incomplete resection survived markedly longer than patients with a low islet macrophage density but complete resection. Conclusion The tumor islet CD68+ macrophage density is a powerful independent predictor of survival from surgically resected NSCLC. The biologic explanation for this and its implications for the use of adjunctive treatment requires further study. © 2005 by American Society of Clinical Oncology.

Modelling survival data to account for model uncertainty: a single model or model averaging?

This study considered the problem of predicting survival, based on three alternative models: a single Weibull, a mixture of Weibulls and a cure model. Instead of the common procedure of choosing a single “best” model, where “best” is defined in terms of goodness of fit to the data, a Bayesian model averaging (BMA) approach was adopted to account for model uncertainty. This was illustrated using a case study in which the aim was the description of lymphoma cancer survival with covariates given by phenotypes and gene expression. The results of this study indicate that if the sample size is sufficiently large, one of the three models emerge as having highest probability given the data, as indicated by the goodness of fit measure; the Bayesian information criterion (BIC). However, when the sample size was reduced, no single model was revealed as “best”, suggesting that a BMA approach would be appropriate. Although a BMA approach can compromise on goodness of fit to the data (when compared to the true model), it can provide robust predictions and facilitate more detailed investigation of the relationships between gene expression and patient survival. Keywords: Bayesian modelling; Bayesian model averaging; Cure model; Markov Chain Monte Carlo; Mixture model; Survival analysis; Weibull distribution

Hybrid frequency–time domain models for dynamic response analysis of marine structures

Time-domain models of marine structures based on frequency domain data are usually built upon the Cummins equation. This type of model is a vector integro-differential equation which involves convolution terms. These convolution terms are not convenient for analysis and design of motion control systems. In addition, these models are not efficient with respect to simulation time, and ease of implementation in standard simulation packages. For these reasons, different methods have been proposed in the literature as approximate alternative representations of the convolutions. Because the convolution is a linear operation, different approaches can be followed to obtain an approximately equivalent linear system in the form of either transfer function or state-space models. This process involves the use of system identification, and several options are available depending on how the identification problem is posed. This raises the question whether one method is better than the others. This paper therefore has three objectives. The first objective is to revisit some of the methods for replacing the convolutions, which have been reported in different areas of analysis of marine systems: hydrodynamics, wave energy conversion, and motion control systems. The second objective is to compare the different methods in terms of complexity and performance. For this purpose, a model for the response in the vertical plane of a modern containership is considered. The third objective is to describe the implementation of the resulting model in the standard simulation environment Matlab/Simulink.

Evaluating Specification Tests for Markov-Switching Time-Series Models

We evaluate the performance of several specification tests for Markov regime-switching time-series models. We consider the Lagrange multiplier (LM) and dynamic specification tests of Hamilton (1996) and Ljung–Box tests based on both the generalized residual and a standard-normal residual constructed using the Rosenblatt transformation. The size and power of the tests are studied using Monte Carlo experiments. We find that the LM tests have the best size and power properties. The Ljung–Box tests exhibit slight size distortions, though tests based on the Rosenblatt transformation perform better than the generalized residual-based tests. The tests exhibit impressive power to detect both autocorrelation and autoregressive conditional heteroscedasticity (ARCH). The tests are illustrated with a Markov-switching generalized ARCH (GARCH) model fitted to the US dollar–British pound exchange rate, with the finding that both autocorrelation and GARCH effects are needed to adequately fit the data.

Methods and preliminary results for a data linkage project to determine long-term survival after intensive care unit admission

Aims: To describe a local data linkage project to match hospital data with the Australian Institute of Health and Welfare (AIHW) National Death Index (NDI) to assess longterm outcomes of intensive care unit patients. Methods: Data were obtained from hospital intensive care and cardiac surgery databases on all patients aged 18 years and over admitted to either of two intensive care units at a tertiary-referral hospital between 1 January 1994 and 31 December 2005. Date of death was obtained from the AIHW NDI by probabilistic software matching, in addition to manual checking through hospital databases and other sources. Survival was calculated from time of ICU admission, with a censoring date of 14 February 2007. Data for patients with multiple hospital admissions requiring intensive care were analysed only from the first admission. Summary and descriptive statistics were used for preliminary data analysis. Kaplan-Meier survival analysis was used to analyse factors determining long-term survival. Results: During the study period, 21 415 unique patients had 22 552 hospital admissions that included an ICU admission; 19 058 surgical procedures were performed with a total of 20 092 ICU admissions. There were 4936 deaths. Median follow-up was 6.2 years, totalling 134 203 patient years. The casemix was predominantly cardiac surgery (80%), followed by cardiac medical (6%), and other medical (4%). The unadjusted survival at 1, 5 and 10 years was 97%, 84% and 70%, respectively. The 1-year survival ranged from 97% for cardiac surgery to 36% for cardiac arrest. An APACHE II score was available for 16 877 patients. In those discharged alive from hospital, the 1, 5 and 10-year survival varied with discharge location. Conclusions: ICU-based linkage projects are feasible to determine long-term outcomes of ICU patients

The importance of environmental issues and costs in Life Cycle Cost Analysis (LCCA) for highway projects

Sustainability has been increasingly recognised as an integral part of highway infrastructure development. In practice however, the fact that financial return is still a project’s top priority for many, environmental aspects tend to be overlooked or considered as a burden, as they add to project costs. Sustainability and its implications have a far-reaching effect on each project over time. Therefore, with highway infrastructure’s long-term life span and huge capital demand, the consideration of environmental cost/ benefit issues is more crucial in life-cycle cost analysis (LCCA). To date, there is little in existing literature studies on viable estimation methods for environmental costs. This situation presents the potential for focused studies on environmental costs and issues in the context of life-cycle cost analysis. This paper discusses a research project which aims to integrate the environmental cost elements and issues into a conceptual framework for life cycle costing analysis for highway projects. Cost elements and issues concerning the environment were first identified through literature. Through questionnaires, these environmental cost elements will be validated by practitioners before their consolidation into the extension of existing and worked models of life-cycle costing analysis (LCCA). A holistic decision support framework is being developed to assist highway infrastructure stakeholders to evaluate their investment decision. This will generate financial returns while maximising environmental benefits and sustainability outcome.

Further notes on the application of zero-inflated models in highway safety

The intent of this note is to succinctly articulate additional points that were not provided in the original paper (Lord et al., 2005) and to help clarify a collective reluctance to adopt zero-inflated (ZI) models for modeling highway safety data. A dialogue on this important issue, just one of many important safety modeling issues, is healthy discourse on the path towards improved safety modeling. This note first provides a summary of prior findings and conclusions of the original paper. It then presents two critical and relevant issues: the maximizing statistical fit fallacy and logic problems with the ZI model in highway safety modeling. Finally, we provide brief conclusions.

p53 prevents progression of nevi to melanoma predominantly through cell cycle regulation

p53 is the central member of a critical tumor suppressor pathway in virtually all tumor types, where it is silenced mainly by missense mutations. In melanoma, p53 predominantly remains wild type, thus its role has been neglected. To study the effect of p53 on melanocyte function and melanomagenesis, we crossed the 'high-p53'Mdm4+/- mouse to the well-established TP-ras0/+ murine melanoma progression model. After treatment with the carcinogen dimethylbenzanthracene (DMBA), TP-ras0/+ mice on the Mdm4+/- background developed fewer tumors with a delay in the age of onset of melanomas compared to TP-ras0/+ mice. Furthermore, we observed a dramatic decrease in tumor growth, lack of metastasis with increased survival of TP-ras0/+: Mdm4+/- mice. Thus, p53 effectively prevented the conversion of small benign tumors to malignant and metastatic melanoma. p53 activation in cultured primary melanocyte and melanoma cell lines using Nutlin-3, a specific Mdm2 antagonist, supported these findings. Moreover, global gene expression and network analysis of Nutlin-3-treated primary human melanocytes indicated that cell cycle regulation through the p21WAF1/CIP1 signaling network may be the key anti-melanomagenic activity of p53.

FGFR2 point mutations in 466 Endometrioid Endometrial Tumors : relationship with MSI, KRAS, PIK3CA, CTNNB1 mutations and Clinicopathological features

Mutations in multiple oncogenes including KRAS, CTNNB1, PIK3CA and FGFR2 have been identified in endometrial cancer. The aim of this study was to provide insight into the clinicopathological features associated with patterns of mutation in these genes, a necessary step in planning targeted therapies for endometrial cancer. 466 endometrioid endometrial tumors were tested for mutations in FGFR2, KRAS, CTNNB1, and PIK3CA. The relationships between mutation status, tumor microsatellite instability (MSI) and clinicopathological features including overall survival (OS) and disease-free survival (DFS) were evaluated using Kaplan-Meier survival analysis and Cox proportional hazard models. Mutations were identified in FGFR2 (48/466); KRAS (87/464); CTNNB1 (88/454) and PIK3CA (104/464). KRAS and FGFR2 mutations were significantly more common, and CTNNB1 mutations less common, in MSI positive tumors. KRAS and FGFR2 occurred in a near mutually exclusive pattern (p = 0.05) and, surprisingly, mutations in KRAS and CTNNB1 also occurred in a near mutually exclusive pattern (p = 0.0002). Multivariate analysis revealed that mutation in KRAS and FGFR2 showed a trend (p = 0.06) towards longer and shorter DFS, respectively. In the 386 patients with early stage disease (stage I and II), FGFR2 mutation was significantly associated with shorter DFS (HR = 3.24; 95% confidence interval, CI, 1.35-7.77; p = 0.008) and OS (HR = 2.00; 95% CI 1.09-3.65; p = 0.025) and KRAS was associated with longer DFS (HR = 0.23; 95% CI 0.05-0.97; p = 0.045). In conclusion, although KRAS and FGFR2 mutations share similar activation of the MAPK pathway, our data suggest very different roles in tumor biology. This has implications for the implementation of anti-FGFR or anti-MEK biologic therapies.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Health service pathways for patients with chronic leg ulcers : identifying effective pathways for facilitation of evidence based wound care

Background: Chronic leg ulcers cause long term ill-health for older adults and the condition places a significant burden on health service resources. Although evidence on effective management of the condition is available, a significant evidence-practice gap is known to exist, with many suggested reasons e.g. multiple care providers, costs of care and treatments. This study aimed to identify effective health service pathways of care which facilitated evidence-based management of chronic leg ulcers. Methods: A sample of 70 patients presenting with a lower limb leg or foot ulcer at specialist wound clinics in Queensland, Australia were recruited for an observational study and survey. Retrospective data were collected on demographics, health, medical history, treatments, costs and health service pathways in the previous 12 months. Prospective data were collected on health service pathways, pain, functional ability, quality of life, treatments, wound healing and recurrence outcomes for 24 weeks from admission. Results: Retrospective data indicated that evidence based guidelines were poorly implemented prior to admission to the study, e.g. only 31% of participants with a lower limb ulcer had an ABPI or duplex assessment in the previous 12 months. On average, participants accessed care 2–3 times/week for 17 weeks from multiple health service providers in the twelve months before admission to the study clinics. Following admission to specialist wound clinics, participants accessed care on average once per week for 12 weeks from a smaller range of providers. The median ulcer duration on admission to the study was 22 weeks (range 2–728 weeks). Following admission to wound clinics, implementation of key indicators of evidence based care increased (p<0.001) and Kaplan-Meier survival analysis found the median time to healing was 12 weeks (95% CI 9.3–14.7). Implementation of evidence based care was significantly related to improved healing outcomes (p<0.001). Conclusions: This study highlights the complexities involved in accessing expertise and evidence based wound care for adults with chronic leg or foot ulcers. Results demonstrate that access to wound management expertise can promote streamlined health services and evidence based wound care, leading to efficient use of health resources and improved health.