Novel methods and the maximum likelihood estimation technique for estimating traffic critical gap

Most unsignalised intersection capacity calculation procedures are based on gap acceptance models. Accuracy of critical gap estimation affects accuracy of capacity and delay estimation. Several methods have been published to estimate drivers’ sample mean critical gap, the Maximum Likelihood Estimation (MLE) technique regarded as the most accurate. This study assesses three novel methods; Average Central Gap (ACG) method, Strength Weighted Central Gap method (SWCG), and Mode Central Gap method (MCG), against MLE for their fidelity in rendering true sample mean critical gaps. A Monte Carlo event based simulation model was used to draw the maximum rejected gap and accepted gap for each of a sample of 300 drivers across 32 simulation runs. Simulation mean critical gap is varied between 3s and 8s, while offered gap rate is varied between 0.05veh/s and 0.55veh/s. This study affirms that MLE provides a close to perfect fit to simulation mean critical gaps across a broad range of conditions. The MCG method also provides an almost perfect fit and has superior computational simplicity and efficiency to the MLE. The SWCG method performs robustly under high flows; however, poorly under low to moderate flows. Further research is recommended using field traffic data, under a variety of minor stream and major stream flow conditions for a variety of minor stream movement types, to compare critical gap estimates using MLE against MCG. Should the MCG method prove as robust as MLE, serious consideration should be given to its adoption to estimate critical gap parameters in guidelines.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

The effect of very small air gaps on small field dosimetry

The purpose of this study was to investigate the effect of very small air gaps (less than 1 mm) on the dosimetry of small photon fields used for stereotactic treatments. Measurements were performed with optically stimulated luminescent dosimeters (OSLDs) for 6 MV photons on a Varian 21iX linear accelerator with a Brainlab μMLC attachment for square field sizes down to 6 mm × 6 mm. Monte Carlo simulations were performed using EGSnrc C++ user code cavity. It was found that the Monte Carlo model used in this study accurately simulated the OSLD measurements on the linear accelerator. For the 6 mm field size, the 0.5 mm air gap upstream to the active area of the OSLD caused a 5.3 % dose reduction relative to a Monte Carlo simulation with no air gap. A hypothetical 0.2 mm air gap caused a dose reduction > 2 %, emphasizing the fact that even the tiniest air gaps can cause a large reduction in measured dose. The negligible effect on an 18 mm field size illustrated that the electronic disequilibrium caused by such small air gaps only affects the dosimetry of the very small fields. When performing small field dosimetry, care must be taken to avoid any air gaps, as can be often present when inserting detectors into solid phantoms. It is recommended that very small field dosimetry is performed in liquid water. When using small photon fields, sub-millimetre air gaps can also affect patient dosimetry if they cannot be spatially resolved on a CT scan. However the effect on the patient is debatable as the dose reduction caused by a 1 mm air gap, starting out at 19% in the first 0.1 mm behind the air gap, decreases to < 5 % after just 2 mm, and electronic equilibrium is fully re-established after just 5 mm.

Langevin dynamics modeling of the water diffusion tensor in partially aligned collagen networks

In this work, a Langevin dynamics model of the diffusion of water in articular cartilage was developed. Numerical simulations of the translational dynamics of water molecules and their interaction with collagen fibers were used to study the quantitative relationship between the organization of the collagen fiber network and the diffusion tensor of water in model cartilage. Langevin dynamics was used to simulate water diffusion in both ordered and partially disordered cartilage models. In addition, an analytical approach was developed to estimate the diffusion tensor for a network comprising a given distribution of fiber orientations. The key findings are that (1) an approximately linear relationship was observed between collagen volume fraction and the fractional anisotropy of the diffusion tensor in fiber networks of a given degree of alignment, (2) for any given fiber volume fraction, fractional anisotropy follows a fiber alignment dependency similar to the square of the second Legendre polynomial of cos(θ), with the minimum anisotropy occurring at approximately the magic angle (θMA), and (3) a decrease in the principal eigenvalue and an increase in the transverse eigenvalues is observed as the fiber orientation angle θ progresses from 0◦ to 90◦. The corresponding diffusion ellipsoids are prolate for θ < θMA, spherical for θ ≈ θMA, and oblate for θ > θMA. Expansion of the model to include discrimination between the combined effects of alignment disorder and collagen fiber volume fraction on the diffusion tensor is discussed.

Multi-area reliability assessment of interconnected generating systems using hybrid approach

This paper present an efficient method using system state sampling technique in Monte Carlo simulation for reliability evaluation of multi-area power systems, at Hierarchical Level One (HLI). System state sampling is one of the common methods used in Monte Carlo simulation. The cpu time and memory requirement can be a problem, using this method. Combination of analytical and Monte Carlo method known as Hybrid method, as presented in this paper, can enhance the efficiency of the solution. Incorporation of load model in this study can be utilised either by sampling or enumeration. Both cases are examined in this paper, by application of the methods on Roy Billinton Test System(RBTS).

Uncertainty analysis of pollutant build-up modelling based on a Bayesian Weighted Least Squares approach

Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality data sets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares Regression and Bayesian Weighted Least Squares Regression for the estimation of uncertainty associated with pollutant build-up prediction using limited data sets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in the prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling.

The influence of neighbourhood disadvantage on smoking cessation and its contribution to inequalities in smoking status

Introduction and aims: Individual smokers from disadvantaged backgrounds are less likely to quit, which contributes to widening inequalities in smoking. Residents of disadvantaged neighbourhoods are more likely to smoke, and neighbourhood inequalities in smoking may also be widening because of neighbourhood differences in rates of cessation. This study examined the association between neighbourhood disadvantage and smoking cessation and its relationship with neighbourhood inequalities in smoking. Design and methods: A multilevel longitudinal study of mid-aged (40-67 years) residents (n=6915) of Brisbane, Australia, who lived in the same neighbourhoods (n=200) in 2007 and 2009. Neighbourhood inequalities in cessation and smoking were analysed using multilevel logistic regression and Markov chain Monte Carlo simulation. Results: After adjustment for individual-level socioeconomic factors, the probability of quitting smoking between 2007 and 2009 was lower for residents of disadvantaged neighbourhoods (9.0%-12.8%) than their counterparts in more advantaged neighbourhoods (20.7%-22.5%). These inequalities in cessation manifested in widening inequalities in smoking: in 2007 the between-neighbourhood variance in rates of smoking was 0.242 (p≤0.001) and in 2009 it was 0.260 (p≤0.001). In 2007, residents of the most disadvantaged neighbourhoods were 88% (OR 1.88, 95% CrI 1.41-2.49) more likely to smoke than residents in the least disadvantaged neighbourhoods: the corresponding difference in 2009 was 98% (OR 1.98 95% CrI 1.48-2.66). Conclusion: Fundamentally, social and economic inequalities at the neighbourhood and individual-levels cause smoking and cessation inequalities. Reducing these inequalities will require comprehensive, well-funded, and targeted tobacco control efforts and equity based policies that address the social and economic determinants of smoking.

Dosimetric effects of a high-density spinal implant

In this study, a treatment plan for a spinal lesion, with all beams transmitted though a titanium vertebral reconstruction implant, was used to investigate the potential effect of a high-density implant on a three-dimensional dose distribution for a radiotherapy treatment. The BEAMnrc/DOSXYZnrc and MCDTK Monte Carlo codes were used to simulate the treatment using both a simplified, recltilinear model and a detailed model incorporating the full complexity of the patient anatomy and treatment plan. The resulting Monte Carlo dose distributions showed that the commercial treatment planning system failed to accurately predict both the depletion of dose downstream of the implant and the increase in scattered dose adjacent to the implant. Overall, the dosimetric effect of the implant was underestimated by the commercial treatment planning system and overestimated by the simplified Monte Carlo model. The value of performing detailed Monte Carlo calculations, using the full patient and treatment geometry, was demonstrated.

Polymorphisms of the SIPA1 gene and sporadic breast cancer susceptibility

Background The novel breast cancer metastasis modulator gene signal-induced proliferation-associated 1 (Sipa1) underlies the breast cancer metastasis efficiency modifier locus Mtes 1 and has been shown to influence mammary tumour metastatic efficiency in the mouse, with an ectopically expressing Sipa1 cell line developing 1.5 to 2 fold more surface pulmonary metastases. Sipa1 encodes a mitogen-inducible GTPase activating (GAP) protein for members of the Ras-related proteins; participates in cell adhesion and modulates mitogen-induced cell cycle progression. Germline SIPA1 SNPs showed association with positive lymph node metastasis and hormonal receptor status in a Caucasian cohort. We hypothesized that SIPA1 may also be correlated to breast carcinoma incidence as well as prognosis. Therefore, this study investigated the potential relationship of SIPA1 and human breast cancer incidence by a germline SNP genotype frequency association study in a case-control Caucasian cohort in Queensland, Australia. Methods The SNPs genotyped in this study were identified in a previous study and the genotyping assays were carried out using TaqMan SNP Genotyping Assays. The data were analysed with chi-square method and the Monte Carlo style CLUMP analysis program. Results Results indicated significance with SIPA1 SNP rs3741378; the CC genotype was more frequently observed in the breast cancer group compared to the disease-free control group, indicating the variant C allele was associated with increased breast cancer incidence. Conclusion This observation indicates SNP rs3741378 as a novel potential sporadic breast cancer predisposition SNP. While it showed association with hormonal receptor status in breast cancer group in a previous pilot study, this exonic missense SNP (Ser (S) to Phe (F)) changes a hydrophilic residue (S) to a hydrophobic residue (F) and may significantly alter the protein functions of SIPA1 in breast tumourgenesis. SIPA1 SNPs rs931127 (5' near gene), and rs746429 (synonymous (Ala (A) to Ala (A)), did not show significant associations with breast cancer incidence, yet were associated with lymph node metastasis in the previous study. This suggests that SIPA1 may be involved in different stages of breast carcinogenesis and since this study replicates a previous study of the associated SNP, it implicates variants of the SIPA1 gene as playing a potential role in breast cancer.

Investigation of a neuronal nitric oxide synthase gene (NOS1) polymorphism in a multiple sclerosis population

Multiple Sclerosis (MS) is a chronic neurological disease characterized by demyelination associated with infiltrating white blood cells in the central nervous system (CNS). Nitric oxide synthases (NOS) are a family of enzymes that control the production of nitric oxide. It is possible that neuronal NOS could be involved in MS pathophysiology and hence the nNOS gene is a potential candidate for involvement in disease susceptibility. The aim of this study was to determine whether allelic variation at the nNOS gene locus is associated with MS in an Australian cohort. DNA samples obtained from a Caucasian Australian population affected with MS and an unaffected control population, matched for gender, age and ethnicity, were genotyped for a microsatellite polymorphism in the promoter region of the nNOS gene. Allele frequencies were compared using chi-squared based statistical analyses with significance tested by Monte Carlo simulation. Allelic analysis of MS cases and controls produced a chi-squared value of 5.63 with simulated P = 0.96 (OR(max) = 1.41, 95% CI: 0.926-2.15). Similarly, a Mann-Whitney U analysis gave a non-significant P-value of 0.377 for allele distribution. No differences in allele frequencies were observed for gender or clinical course subtype (P > 0.05). Statistical analysis indicated that there is no association of this nNOS variant and MS and hence the gene does not appear to play a genetically significant role in disease susceptibility.

Sibpair studies implicate chromosome 18 in essential hypertension

Interest in chromosome 18 in essential hypertension comes from comparative mapping of rat blood pressure quantitative trait loci (QTL), familial orthostatic hypotensive syndrome studies, and essential hypertension pedigree linkage analyses indicating that a locus or loci on human chromosome 18 may play a role in hypertension development. To further investigate involvement of chromosome 18 in human essential hypertension, the present study utilized a linkage scan approach to genotype twelve microsatellite markers spanning human chromosome 18 in 177 Australian Caucasian hypertensive (HT) sibling pairs. Linkage analysis showed significant excess allele sharing of the D18S61 marker when analyzed with SPLINK (P=0.00012), ANALYZE (Sibpair) (P=0.0081), and also with MAPMAKER SIBS (P=0.0001). Similarly, the D18S59 marker also showed evidence for excess allele sharing when analyzed with SPLINK (P=0.016), ANALYZE (Sibpair) (P=0.0095), and with MAPMAKER SIBS (P = 0.014). The adenylate cyclase activating polypeptide 1 gene (ADCYAP1) is involved in vasodilation and has been co-localized to the D18S59 marker. Results testing a microsatellite marker in the 3′ untranslated region of ADCYAP1 in age and gender matched HT and normotensive (NT) individuals showed possible association with hypertension (P = 0.038; Monte Carlo P = 0.02), but not with obesity. The present study shows a chromosome 18 role in essential hypertension and indicates that the genomic region near the ADCYAP1 gene or perhaps the gene itself may be implicated. Further investigation is required to conclusively determine the extent to which ADCYAP1 polymorphisms are involved in essential hypertension. © 2003 Wiley-Liss, Inc.

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

A Bayesian regression approach to assess uncertainty in pollutant wash-off modelling

Due to knowledge gaps in relation to urban stormwater quality processes, an in-depth understanding of model uncertainty can enhance decision making. Uncertainty in stormwater quality models can originate from a range of sources such as the complexity of urban rainfall-runoff-stormwater pollutant processes and the paucity of observed data. Unfortunately, studies relating to epistemic uncertainty, which arises from the simplification of reality are limited and often deemed mostly unquantifiable. This paper presents a statistical modelling framework for ascertaining epistemic uncertainty associated with pollutant wash-off under a regression modelling paradigm using Ordinary Least Squares Regression (OLSR) and Weighted Least Squares Regression (WLSR) methods with a Bayesian/Gibbs sampling statistical approach. The study results confirmed that WLSR assuming probability distributed data provides more realistic uncertainty estimates of the observed and predicted wash-off values compared to OLSR modelling. It was also noted that the Bayesian/Gibbs sampling approach is superior compared to the most commonly adopted classical statistical and deterministic approaches commonly used in water quality modelling. The study outcomes confirmed that the predication error associated with wash-off replication is relatively higher due to limited data availability. The uncertainty analysis also highlighted the variability of the wash-off modelling coefficient k as a function of complex physical processes, which is primarily influenced by surface characteristics and rainfall intensity.

A practical and theoretical definition of very small field size for radiotherapy output factor measurements

Purpose This work introduces the concept of very small field size. Output factor (OPF) measurements at these field sizes require extremely careful experimental methodology including the measurement of dosimetric field size at the same time as each OPF measurement. Two quantifiable scientific definitions of the threshold of very small field size are presented. Methods A practical definition was established by quantifying the effect that a 1 mm error in field size or detector position had on OPFs, and setting acceptable uncertainties on OPF at 1%. Alternatively, for a theoretical definition of very small field size, the OPFs were separated into additional factors to investigate the specific effects of lateral electronic disequilibrium, photon scatter in the phantom and source occlusion. The dominant effect was established and formed the basis of a theoretical definition of very small fields. Each factor was obtained using Monte Carlo simulations of a Varian iX linear accelerator for various square field sizes of side length from 4 mm to 100 mm, using a nominal photon energy of 6 MV. Results According to the practical definition established in this project, field sizes < 15 mm were considered to be very small for 6 MV beams for maximal field size uncertainties of 1 mm. If the acceptable uncertainty in the OPF was increased from 1.0 % to 2.0 %, or field size uncertainties are 0.5 mm, field sizes < 12 mm were considered to be very small. Lateral electronic disequilibrium in the phantom was the dominant cause of change in OPF at very small field sizes. Thus the theoretical definition of very small field size coincided to the field size at which lateral electronic disequilibrium clearly caused a greater change in OPF than any other effects. This was found to occur at field sizes < 12 mm. Source occlusion also caused a large change in OPF for field sizes < 8 mm. Based on the results of this study, field sizes < 12 mm were considered to be theoretically very small for 6 MV beams. Conclusions Extremely careful experimental methodology including the measurement of dosimetric field size at the same time as output factor measurement for each field size setting and also very precise detector alignment is required at field sizes at least < 12 mm and more conservatively < 15 mm for 6 MV beams. These recommendations should be applied in addition to all the usual considerations for small field dosimetry, including careful detector selection.

Retrospective evaluation of dosimetric quality for prostate carcinomas treated with 3D conformal, intensity-modulated and volumetric-modulated arc radiotherapy

Introduction This study examines and compares the dosimetric quality of radiotherapy treatment plans for prostate carcinoma across a cohort of 163 patients treated across 5 centres: 83 treated with three-dimensional conformal radiotherapy (3DCRT), 33 treated with intensity-modulated radiotherapy (IMRT) and 47 treated with volumetric-modulated arc therapy (VMAT). Methods Treatment plan quality was evaluated in terms of target dose homogeneity and organ-at-risk sparing, through the use of a set of dose metrics. These included the mean, maximum and minimum doses; the homogeneity and conformity indices for the target volumes; and a selection of dose coverage values that were relevant to each organ-at-risk. Statistical significance was evaluated using two-tailed Welch’s T-tests. The Monte Carlo DICOM ToolKit software was adapted to permit the evaluation of dose metrics from DICOM data exported from a commercial radiotherapy treatment planning system. Results The 3DCRT treatment plans offered greater planning target volume dose homogeneity than the other two treatment modalities. The IMRT and VMAT plans offered greater dose reduction in the organs-at-risk: with increased compliance with recommended organ-at-risk dose constraints, compared to conventional 3DCRT treatments. When compared to each other, IMRT and VMAT did not provide significantly different treatment plan quality for like-sized tumour volumes. Conclusions This study indicates that IMRT and VMAT have provided similar dosimetric quality, which is superior to the dosimetric quality achieved with 3DCRT.