Direct Infusion (DI) Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS) is becoming a popular measurement platform in metabolomics. This thesis aims to advance the data processing and analysis pipeline of the DI FT-ICR based metabolomics, and broaden its applicability to a clinical research. To meet the first objective, the issue of missing data that occur in a final data matrix containing metabolite relative abundances measured for each sample analysed, is addressed. The nature of these data and their effect on the subsequent data analyses are investigated. Eight common and/or easily accessible missing data estimation algorithms are examined and a three stage approach is proposed to aid the identification of the optimal one. Finally, a novel survival analysis approach is introduced and assessed as an alternative way of missing data treatment prior univariate analysis. To address the second objective, DI FT-ICR MS based metabolomics is assessed in terms of its applicability to research investigating metabolomic changes occurring in liver grafts throughout the human orthotopic liver transplantation (OLT). The feasibility of this approach to a clinical setting is validated and its potential to provide a wealth of novel metabolic information associated with OLT is demonstrated.

Variation in health care, whether it be in terms of the utilisation of resources, observed health outcomes, costs, quality or access to health care is a well recognised and ever present feature of the modern day health care system. Health care variations challenge basic assumptions about the nature of the health care economy and raise questions about efficiency, equity and where best to direct policy instruments in health care markets. Despite the vast literature documenting variation, and the many discussions around ways to reduce variations in health care markets, the field of dental care has received little interest, in comparison to that of general medical care. This thesis will address this gap and will analyse the variation observed in a specific dental care treatment (dental radiographs) within NHS Scotland, with particular emphasis on the contribution of both dentist and patient unobserved heterogeneity. The thesis takes its focus from two strands of the literature; the underlying theoretical aspect draws on the literature concerning the theory of incentives and physician agency, whilst the empirical component makes use of recent advances in micro-econometric methods, documented in the labour economics literature. Although the thesis is predominantly an empirical analysis, the estimation strategy combines ideas from both the theoretical and empirical literature. A matched patient provider dataset from NHS Scotland is used to conduct an analysis of the variation in dental radiographs, in the presence of, and controlling for unobserved dentist and patient heterogeneity. The results indicate that the remuneration structure alone has little or no impact on the treatment decision to provide a radiograph. When a dentist changes from being on a fixed salary contract to being paid on a fee-for-service basis, they are in fact less likely to provide a radiograph. This result changes in the presence of insurance (identified as being when patients are exempt from the patient charge) and indicates that when the self employed dentist can identify the patient as being exempt, they are more likely to provide a radiograph. This result provides some support for the theory that in the presence of insurance, financial incentives do influence the treatment decision. A final result of the study highlights the importance of accounting for unobserved patient and provider heterogeneity, a factor that has had little attention in the healthcare literature. The results suggest that patient variation, as opposed to the variation across dentists, is much more important in explaining total variation. This is a similar result to that found in both the labour and education literatures.

A computer interview has been found to be valuable in eliciting information. This thesis describes the use of a CIS in two different clinical settings (GP surgery and chiropractic clinic), with the aims of aiding recognition of patients with anxiety or depression and enhancing communication between patient and clinician. The Hospital Anxiety and Depression Scale (HADS) was included in the computer interview. 60 patients used the CIS in both settings, 6 GPs and 3 chiropractors were involved in the study. A high level of acceptance of the CIS was found in patients and clinicians in both settings. 99% of patients rated the system as “easy” or “extremely easy to use”. In the GP surgery, 80% of patients felt that they were “possibly” (68.33%) or “definitely” (11.67%) more focussed for the consultation. In the chiropractic clinic, 41.7% of patients said they disclosed new information and 33.3% felt better prepared for the consultation. The CIS aided the recognition of some individuals with anxiety or depression, more so within the chiropractic clinic than in the GP surgery. The information in the interview transcript was considered more useful by the chiropractors than the GPs (85% v 21.67%); this could be partially attributable to the fact that the chiropractors added 15 reassessment questions to the question set, whilst the GPs only added 4, more general, questions. The CIS also helped to highlight communication issues and show trends within the patient populations. Although the CIS was found to be of benefit in both settings, the GPs felt that it was more appropriate for use with specific patient groups. In the chiropractic clinic, the CIS was an effective addition to the periodic patient reassessment process. It would be possible to deliver other, individualised screening interviews using the CIS, delivery of which could be enhanced using handheld devices.

This thesis examines health inequalities by area-level socioeconomic deprivation, and health in later life in New Zealand. It identifies whether expansion or compression of morbidity is occurring at the end of life. It asks if overall morbidity at a population level is likely to increase or decrease in future as life expectancy increases, and if the same trend is seen for more and less deprived areas. The focus of this research is the identification and dissemination of mortality and morbidity patterns present in two large datasets, using powerful but relatively simple techniques. Large administrative datasets on morbidity and public hospital discharges in New Zealand between 1974 and 2006 are used in the analyses. The thesis consists of three papers. Each paper uses the same datasets, but addresses separate research questions using different methods. The first paper is an exploratory analysis of age-specific and age-standardised mortality and hospital bed day rates, which are used as a proxy for morbidity. The second paper explores lifetime morbidity by using period-prevalence life table functions including Hospital Utilisation Expectancies: a variation of health expectancies. The third paper uses individual record linkage between the mortality and hospital datasets to examine hospital use in the last few months of life. Hospital bed day and mortality rates declined over the time period, and convergence was seen between more and less deprived areas. Individuals at the oldest ages (80 years and over) saw little variation in hospital or mortality rates by area deprivation. Strong evidence for compression of morbidity was observed, particularly at older ages. This was in the absence of evidence for rectangularisation of the survival curve, considered by some to be a prerequisite for compression of morbidity. Rectangularisation of the survival curve would be denoted by life expectancy increases slowing, indicating the nearing of a limit to life expectancy. Instead, compression of morbidity was achieved through a decline in the severity of morbidity in the months prior to death. No evidence of a change in the point at onset of morbidity prior to death was observed. There was however some evidence that the decline in hospital utilisation prior to death (particularly for deaths at older ages) may be partly artefactual. Further research using a different measure of morbidity is required to either support or disprove this theory.

Noncompliance to treatment allocation is likely to complicate estimation of causal effects in clinical trials. The ubiquitous nonrandom phenomenon of noncompliance renders per-protocol and as- treated analyses or even simple regression adjustments for noncompliance inadequate for causal inference. For survival data, several specialist methods have been developed when noncompliance is related to risk. The Causal Accelerated Life Model (CALM) allows time-dependent departures from randomized treatment in either arm and relates each observed event time to a potential event time that would have been observed if the control treatment had been given throughout the trial. Alternatively, the structural Proportional Hazards (C-Prophet) model accounts for all-or-nothing noncompliance in the treatment arm only while the CHARM estimator allows time-dependent departures from randomized treatment by considering survival outcome as a sequence of binary outcomes to provide an 'approximate' overall hazard ratio estimate which is adjusted for compliance. The problem of efficacy estimation is compounded for two-active treatment trials (additional noncompliance) where the ITT estimate provides a biased estimator for the true hazard ratio even under homogeneous treatment effects assumption. Using plausible arm-specific predictors of compliance, principal stratification methods can be applied to obtain principal effects for each stratum. The present work applies the above methods to data from the Esprit trials study which was conducted to ascertain whether or not unopposed oestrogen (hormone replacement therapy - HRT) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. We use statistically designed simulation studies to evaluate the performance of these methods in terms of bias and 95% confidence interval coverage. We also apply a principal stratification method to adjust for noncompliance in two treatment arms trial originally developed for binary data for survival analysis in terms of causal risk ratio. In a Bayesian framework, we apply the method to Esprit data to account for noncompliance in both treatment arms and estimate principal effects. We apply statistically designed simulation studies to evaluate the performance of the method in terms of bias in the causal effect estimates for each stratum. ITT analysis of the Esprit data showed the effects of taking HRT tablets was not statistically significantly different from placebo for both all cause mortality and myocardial reinfarction outcomes. Average compliance rate for HRT treatment was 43% and compliance rate decreased as the study progressed. CHARM and C-Prophet methods produced similar results but CALM performed best for Esprit: suggesting HRT would reduce risk of death by 50%. Simulation studies comparing the methods suggested that while both C-Prophet and CHARM methods performed equally well in terms of bias, the CALM method performed best in terms of both bias and 95% confidence interval coverage albeit with the largest RMSE. The principal stratification method failed for the Esprit study possibly due to the strong distribution assumption implicit in the method and lack of adequate compliance information in the data which produced large 95% credible intervals for the principal effect estimates. For moderate value of sensitivity parameter, principal stratification results suggested compliance with HRT tablets relative to placebo would reduce risk of mortality by 43% among the most compliant. Simulation studies on performance of this method showed narrower corresponding mean 95% credible intervals corresponding to the the causal risk ratio estimates for this subgroup compared to other strata. However, the results were sensitive to the unknown sensitivity parameter.

The overarching purpose of the studies presented in this report is the exploration of the uses of information theory and Bayesian inference applied to neural codes. Two approaches were taken: Starting from first principles, a coding mechanism is proposed, the results are compared to a biological neural code. Secondly, tools from information theory are used to measure the information contained in a biological neural code. Chapter 3: The REC model proposed by Harpur and Prager codes inputs into a sparse, factorial representation, maintaining reconstruction accuracy. Here I propose a modification of the REC model to determine the optimal network dimensionality. The resulting code for unfiltered natural images is accurate, highly sparse and a large fraction of the code elements show localized features. Furthermore, I propose an activation algorithm for the network that is faster and more accurate than a gradient descent based activation method. Moreover, it is demonstrated that asymmetric noise promotes sparseness. Chapter 4: A fast, exact alternative to Bayesian classification is introduced. Computational time is quadratic in both the number of observed data points and the number of degrees of freedom of the underlying model. As an example application, responses of single neurons from high-level visual cortex (area STSa) to rapid sequences of complex visual stimuli are analyzed. Chapter 5: I present an exact Bayesian treatment of a simple, yet sufficiently general probability distribution model. The model complexity, exact values of the expectations of entropies and their variances can be computed with polynomial effort given the data. The expectation of the mutual information becomes thus available, too, and a strict upper bound on its variance. The resulting algorithm is first tested on artificial data. To that end, an information theoretic similarity measure is derived. Second, the algorithm is demonstrated to be useful in neuroscience by studying the information content of the neural responses analyzed in the previous chapter. It is shown that the information throughput of STS neurons is maximized for stimulus durations of approx. 60ms.

Information Systems (IS) innovations in healthcare sector are seen as panacea to control burgeoning demand on healthcare resources and lack of streamlining in care delivery. Two particular manifestations of such innovations are telehealth and electronic records in its two forms: the electronic medical records and the electronic health records. Deployment efforts concerning both of these IS-innovations have encountered a rough terrain and have been slow. Problems are also faced while evaluating the effectiveness of innovations on health and care delivery outcomes through strategies such as randomised controlled trials- particularly in case of telehealth. By taking these issues into account, this research investigates the issues that affect IS innovation deployment and its evaluation. The strategy adopted in this research was informed by recursive philosophy and theoretical perspectives within IS that strived to expound this recursive relationship. It involved conducting two longitudinal case studies that are qualitative in nature. The first study involved telehealth deployment and its evaluation in the UK, while the second case study involved the deployment of electronic medical/health records in the US. Data was collected through focus group discussions, interviews and online discussion threads; and was analysed thematically. The results of this research indicate that there are nine issues that arise and affect the deployment and evaluation of IS innovation in healthcare; and these are design, efficiency and effectiveness, optimality and equity, legitimacy, acceptance, demand and efficacy, expertise, new interaction patterns, and trust. These issues are attributes of relationships between the IS innovation, context of healthcare and the user. The significance of these attributes varies during the deployment and evaluation process, and due to iterative nature of IS innovation. This research further indicates that all the attributes have either direct or indirect impact on work practices of the user.

This study examines the impact of the transition from diploma to degree on the initial education and training of three para-professions in England; chiropody, occupational therapy (OT) and radiography. It focuses on the nature of and reasons for changes to their initial professional education and training and the potential impact of these changes on their professionalisation. The study adopted a multiple method approach; a historical review, which included documentary sources and interviews with key informants, aimed at identifying how and why these three para-professions wanted to achieve all-graduate entry, and the use of case studies to explore the differences between the diploma and degree courses. It is concluded that the achievement of all-graduate entry for these para-professions was an unintended consequence of the policies of the then government. As a result of the achievement of all-graduate entry changes were made to the respective diploma courses of these para-professions. The extent of these changes were related to the level of control and influence that the professional bodies exercised over the diploma courses. All the degree courses shared the following features: the development of autonomous, reflective practitioners who are life-long learners, an emphasis on theory rather than practice, and emphasis on propositional knowledge and the study of research methods. Although the degree courses for these para-professions achieved approval from HEIs it is argued that degree education is a contested concept. It is apparent that the para-professionals believed that the achievement of all-graduate entry would improve their professional status. However, it is evident from the study findings that it served to maintain rather than enhance their social status and market position. Changes to the initial education and training of these para-professionals were the results of the para-professionals having to respond to prevailing social, political and economic circumstances. If they had not taken this action it is suggested that their social status and market position may have been adversely affected.

The Neonatal Intensive Care Unit (NICU) is a hospital ward specializing in looking after premature and ill newborn babies. Working in such a busy and complex environment is not easy and sophisticated equipment is used to help the daily work of the medical staff . Computers are used to analyse the large amount of monitored data and extract hidden information, e.g. to detect interesting events. Unfortunately, one group of important events lacks features that are recognizable by computers. This group includes the actions taken by the medical sta , for example two actions related to the respiratory system: inserting an endotracheal tube into a baby’s trachea (ET Intubating) or sucking out the tube (ET Suctioning). These events are very important building blocks for other computer applications aimed at helping the sta . In this research, a strategy for detecting these medical actions based on contextual knowledge is proposed. This contextual knowledge specifies what other events normally occur with each target event and how they are temporally related to each other. The idea behind this strategy is that all medical actions are taken for di erent purposes hence may have di erent procedures (contextual knowledge) for performing them. This contextual knowledge is modelled using a point based framework with special attention given to various types of uncertainty. Event detection consists in searching for consistent matching between a model based on the contextual knowledge and the observed event instances - a Temporal Constraint Satisfaction Problem (TCSP). The strategy is evaluated by detecting ET Intubating and ET Suctioning events, using a specially collected NICU monitoring dataset. The results of this evaluation are encouraging and show that the strategy is capable of detecting complex events in an NICU.

Earlier ecological studies on the relation between H. pylori infection and stomach cancer have considered that the relation between these two variables, as estimated by the exposure coefficient, is constant. However, there is evidence to suggest that this relation changes geographically due to differences in strains of H. pylori. Since the prevalence of H. pylori varies with socio-economic status, the association between the latter and stomach cancer mortality may also vary geographically. This thesis studies stomach cancer by taking into account the geographical variability of the exposure coefficients. The study proposes the use of regression mixtures, clustering models and spatially varying regressions for the study of varying exposure coefficients. The effect of transformations of variables in these models appears to have been little considered. We provide new necessary conditions for invariance under transformations of variables for mixed effect models in general, and for the proposed models in particular. In addition, we show that varying exposure coefficients may induce a varying baseline risk. The regression mixtures and the clustering model are applied to a data set on stomach cancer incidence and H. pylori prevalence in 57 countries worldwide. We extend the clustering model to reflect any distance measure between the geographical units, including the Euclidean distance, in the formation of clusters. We also show that the clustering model performs better than the regression mixture model when the aim is to identify connected clusters and the observations present large variance. The results obtained with the clustering model supported the existence of three clusters where the interaction between the human and H. pylori populations have similar characteristics. Spatially varying regressions are applied to a data set of areal death counts of stomach cancer and spending power in 275 counties in continental Portugal. We provide an original strategy for implementing multivectorial intrinsic autoregressions as the distribution for the random effects. The results obtained with the application of this methodology were consistent with a varying exposure coefficient of spending power.