4,618 research outputs found

    Inter-individual variation of the human epigenome & applications

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    Understanding and improving the applicability of randomised controlled trials: subgroup reporting and the statistical calibration of trials to real-world populations

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    Context and objective Randomised controlled trials (hereafter, trials) are widely regarded as the gold standard for evaluating treatment efficacy in medical interventions. They employ strict study designs, rigorous eligibility criteria, standardised protocols, and close participant monitoring under controlled conditions, contributing to high internal validity. However, these stringent criteria and procedures may limit the generalisability of trial findings to real-world situations, which often involve diverse patient populations such as multimorbidity and frailty patients. Consequently, there is growing interest in the applicability of trials to real-world clinical practice. In this thesis I will 1) evaluate how well major trials report on variation in treatment effects and 2) examine the use of trial calibration methods to test trial applicability. Methods 1) A comprehensive and consistent subgroup reporting description was presented, which contributes to the exploration of subgroup effects and treatment heterogeneity for informed decision-making in tailored subgroup populations within routine practice. The study evaluated 2,235 trials from clinicaltrial.gov that involve multiple chronic medical conditions, assessing the presence of subgroup reporting in corresponding publications and extracting subgroup terms. These terms were then standardised and summarised using Medical Subject Headings and WHO Anatomical Therapeutic Chemical codes. Logistic and Poisson regression models were employed to identify independent predictors of subgroup reporting patterns. 2) Two calibration models, namely the regression-based model and inverse odds of sampling weights (IOSW) were implemented. These models were utilised to apply the findings from two influential heart failure (HF) trials - COMET and DIG - to a real-world HF registry in Scotland consisting of 8,012 HF patients mainly with reduced ejection fraction, using individual participant data (IPD) from both datasets. Additionally, calibration was conducted within the subgroup population (lowest and highest risk group) of the real-world Scottish HF registry for exploratory analyses. The study provided comparisons of baseline characteristics and calibrated and uncalibrated results between the trial and registry. Furthermore, it assessed the impact of calibration on the results with the focus on overall effects and precision. Results The subgroup reporting study showed that among 2,235 eligible trials, 48% (1,082 trials) reported overall results and 23% (524 trials) reported subgroups. Age (51%), gender (45%), racial group (28%) and geographical locations (17%) were the most frequently reported subgroups among 524 trials. Characteristics related to the index condition (severity/duration/types, etc.) were somewhat commonly reported. However, reporting on metrics of comorbidity or frailty and mental health were rare. Follow-up time, enrolment size, trial starting year and specific index conditions (e.g., hypercholesterolemia, hypertension etc.) were significant predictors for any subgroup reporting after adjusting for enrolment size and index conditions while funding source and number of arms were not associated with subgroup reporting. The trial calibration study showed that registry patients were, on average, older, had poorer renal function and received higher-doses of loop diuretics than trial participants. The key findings from two HF trials remained consistent after calibration in the registry, with a tolerable decrease in precision (larger confidence intervals) for the effect estimates. Treatment-effect estimates were also similar when trials were calibrated to high-risk and low-risk registry patients, albeit with a greater reduction in precision. Conclusion Variations in subgroup reporting among different trials limited the feasibility to evaluate subgroup effects and examine heterogeneity of treatment effects. If IPD or IPD alternative summarised data is available from trials and the registry, trial applicability can be assessed by performing calibration

    A causal inference framework for comparative effectiveness and safety research using observational data, with application in type 2 diabetes

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    Randomized controlled trials are the gold standard to answer causal questions in health research as the process of randomization ensures balanced treatment groups and therefore makes it possible to compare average group outcomes directly. But they have many limitations with respect to costs, ethical considerations and practicability and therefore may not be suitable to answer all research questions. Evidence on cause and effect relationships from observational studies have the potential to overcome the limitations of trials and close important research gaps as they provide the possibility to study subpopulations of patients which are often excluded due to safety concerns, or can give insights into the risk profile of long-term endpoints. The quality of this real-world evidence depends on the quality of data, their suitability to answer a particular research question and the use of appropriate methods to estimate the treatment effect of interest. Of concern in observational research is bias in the treatment effect estimation due to confounding, as the treatment assignment is not controlled by the researcher and cannot be randomized. It is therefore possible that treatment groups are not balanced and confounding factors exist in the data which influence the treatment choice and the outcome of interest simultaneously. The benefits of observational studies make them attractive for studying the risk and benefit profiles of oral type 2 diabetes treatments, especially of newer agent classes such as Sodium-glucose Cotransporter-2 Inhibitors. Prescribing of this treatment class has increased in recent years and a large proportion of type 2 diabetes patients have become eligible to receive agents from this class after recent treatment guideline changes. More information about treatment effects of Sodium-glucose Cotransporter-2 Inhibitors are needed especially for the large patient population of older adults (e.g. 70 years or older), as possible adverse effects such as osmotic symptoms associated with this class could have severe consequences for these patients. The overall aim of this thesis is to develop a causal inference framework for the exploitation of observational data, needed to derive high quality evidence on the benefit and safety profile of oral type 2 diabetes treatments, with a focus on the widely prescribed treatment class of Sodium-glucose Cotransporter-2 Inhibitors and the patient population of older adults. Chapter 1 and 2 are introductions to causal inference theory including the description of all estimation methods employed in this thesis and an introduction to type 2 diabetes research encompassing important treatment decision considerations, and current research evidence on Sodium-glucose Cotransporter-2 Inhibitors. Chapter 3 presents a triangulation framework of assorted estimation methods to establish the consistency of estimation results from approaches utilizing different parts of the data and relying on different data structure assumptions. Furthermore, an Instrumental Variable approach is introduced which uses data from the period before treatment initiation to mitigate potential bias in case the exchangeability assumption is violated and a history of the outcome of interest previous to treatment initiation has an influence on the treatment decision. Chapter 4 describes a simulation study on the performance of established construction methods for a proxy Instrumental Variable of health care provider prescription preference. The methods are tested under different data conditions such as change in provider preference over time, missing data in baseline covariates and different sample sizes within each health care provider. Additionally, a construction method is introduced that aims to address changes in preference over time and non-ignorabile missingness in baseline characteristics. In Chapter 5 the developed conclusions about a robust Instrumental Variable estimation approach from previous chapters are applied for a causal analysis on the relative benefit and risk profile of Sodium-glucose Cotransporter-2 Inhibitors versus Dipeptidyl peptidase-4 Inhibitors in the patient population of older adults. Chapter 6 provides an overview of the main findings and implications of this thesis and discusses limitations and future research potential of each study.Operating Budget, Research Englan

    Determinants of embryonic and foetal growth

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    The main aims of this thesis were:1. To investigate whether there are associations between determinants related to the living environment (in particular neighbourhood deprivation and air pollution) and embryonic growth, foetal growth and pregnancy outcomes;2. To assess the associations between maternal cardiometabolic determinants in pregnancy (lipid status and the presence of hypertensive disorders of pregnancy)and embryonic growth, foetal growth and childhood outcomes;3. To investigate the impact of neighbourhood deprivation on the effectiveness ofthe mHealth “Smarter Pregnancy” program, aimed at improving nutrition and lifestyle behaviours;<br/

    An examination of the verbal behaviour of intergroup discrimination

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    This thesis examined relationships between psychological flexibility, psychological inflexibility, prejudicial attitudes, and dehumanization across three cross-sectional studies with an additional proposed experimental study. Psychological flexibility refers to mindful attention to the present moment, willing acceptance of private experiences, and engaging in behaviours congruent with one’s freely chosen values. Inflexibility, on the other hand, indicates a tendency to suppress unwanted thoughts and emotions, entanglement with one’s thoughts, and rigid behavioural patterns. Study 1 found limited correlations between inflexibility and sexism, racism, homonegativity, and dehumanization. Study 2 demonstrated more consistent positive associations between inflexibility and prejudice. And Study 3 controlled for right-wing authoritarianism and social dominance orientation, finding inflexibility predicted hostile sexism and racism beyond these factors. While showing some relationships, particularly with sexism and racism, psychological inflexibility did not consistently correlate with varied prejudices across studies. The proposed randomized controlled trial aims to evaluate an Acceptance and Commitment Therapy intervention to reduce sexism through enhanced psychological flexibility. Overall, findings provide mixed support for the utility of flexibility-based skills in addressing complex societal prejudices. Research should continue examining flexibility integrated with socio-cultural approaches to promote equity

    Determinants of embryonic and foetal growth

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    The main aims of this thesis were:1. To investigate whether there are associations between determinants related to the living environment (in particular neighbourhood deprivation and air pollution) and embryonic growth, foetal growth and pregnancy outcomes;2. To assess the associations between maternal cardiometabolic determinants in pregnancy (lipid status and the presence of hypertensive disorders of pregnancy)and embryonic growth, foetal growth and childhood outcomes;3. To investigate the impact of neighbourhood deprivation on the effectiveness ofthe mHealth “Smarter Pregnancy” program, aimed at improving nutrition and lifestyle behaviours;<br/

    Life on a scale:Deep brain stimulation in anorexia nervosa

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    Anorexia nervosa (AN) is a severe psychiatric disorder marked by low body weight, body image abnormalities, and anxiety and shows elevated rates of morbidity, comorbidity and mortality. Given the limited availability of evidence-based treatments, there is an urgent need to investigate new therapeutic options that are informed by the disorder’s underlying neurobiological mechanisms. This thesis represents the first study in the Netherlands and one of a limited number globally to evaluate the efficacy, safety, and tolerability of deep brain stimulation (DBS) in the treatment of AN. DBS has the advantage of being both reversible and adjustable. Beyond assessing the primary impact of DBS on body weight, psychological parameters, and quality of life, this research is novel in its comprehensive approach. We integrated evaluations of efficacy with critical examinations of the functional impact of DBS in AN, including fMRI, electroencephalography EEG, as well as endocrinological and metabolic assessments. Furthermore, this work situates AN within a broader theoretical framework, specifically focusing on its manifestation as a form of self-destructive behavior. Finally, we reflect on the practical, ethical and philosophical aspects of conducting an experimental, invasive procedure in a vulnerable patient group. This thesis deepens our understanding of the neurobiological underpinnings of AN and paves the way for future research and potential clinical applications of DBS in the management of severe and enduring AN

    XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting

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    Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Long-term follow-up of children with infantile hemangioma:Multidisciplinary reflections

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