2,171 research outputs found
A causal inference framework for comparative effectiveness and safety research using observational data, with application in type 2 diabetes
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
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
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
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/
XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting
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
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
Idiopathic inflammatory myopathies and cancer : familial risk, genetics and consequences
Idiopathic inflammatory myopathies (IIMs) are a group of rare rheumatic inflammatory
diseases (RIDs), characterised by a diverse range of clinical, serological and
histopathological characteristics, with muscle weakness as a shared hallmark. While
advancements in disease management have improved the survival rates of patients with IIM,
the mortality rate among patients with IIM is still higher than the general population, mainly
due to association with comorbidities such as cancer. The pathogenesis of IIM, the
pathological link between IIM and cancer and the impact of cancer on the survival of patients
with IIM remain a subject of uncertainty. The rarity and heterogeneity inherent in IIM pose
significant challenges in filing these knowledge gaps. This thesis encompasses five studies,
which aimed at addressing research questions concerning the genetic contribution to IIM and
its link with other autoimmune diseases and cancer, as well as the disease burden in the
context of cancer in a large representative population of patients with IIM.
Study I was a population-based case-control family study including 7,615 first-degree
relatives of 1,620 patients with IIM diagnosis between 1997 and 2016 and 37,309 first-degree
relatives of 7,797 matched comparators without IIM. Patients with IIM were four times more
likely to have at least one first-degree relative affected by IIM compared to matched
comparators without IIM. The heritability of IIM, a proportion of the phenotypic variance
that can be explained by additive genetic variance, was 22% in the Swedish population.
Study II, with the same study population as in Study I, analysed the familial associations
between IIM and a variety of autoimmune diseases under a causal framework. We found
shared familial factors between IIM and other RIDs, inflammatory bowel diseases,
autoimmune thyroid diseases and celiac disease.
Study III, with a similar study population and analytical approach as in Study II,
comprehensively investigated the familial co-aggregation of IIM and cancer. We did not
observe a familial association between IIM and cancer overall but modification effect by sex
was noted: there was a modest familial association (adjusted odds ratio=1.39) with cancer in
male first-degree relatives of patients with IIM. We also found that offspring of patients with
IIM were more likely to have a cancer diagnosis at age younger than 50 years compared to
those of matched comparators without IIM. In the exploratory analysis by specific cancer
types, findings suggest that IIM shared familial factors with myeloid malignancies and liver
cancer.
Study IV explored genetic correlation between IIM and B-cell lymphomas via a cross-trait
secondary analysis using summary statistics from genome-wide associations studies of IIM
and four common B-cell lymphoma subtypes including diffuse large B-cell lymphoma,
follicular lymphoma, chronic lymphocytic leukaemia and marginal zone lymphoma. We
detected a limited number of genomic loci, predominantly within the human leukocyte
antigen region, demonstrating significant genetic correlations between IIM and common Bcell
lymphoma subtypes.
Study V, a cohort study, followed 1,826 patients to (first and second) cancer and death
(overall and cause-specific death) events since IIM diagnosis for more than 20 years.
Compared to patients with no cancer diagnosis after IIM, patients with a first cancer diagnosis
after IIM faced a greater five-year mortality (22% versus 49%). This excessive risk was due
to an increased risk of death from cancer. In patients with a first cancer diagnosis after IIM,
the one-year risk of having a second primary cancer was 11% and having a second cancer
diagnosis slightly increased the risk of death. We also reported several prognostic factors
associated with increased risks of cancer and death (overall, from cancer and from other
causes).
This thesis offers useful insight into the role of genetics in IIM pathogenesis and its
connections with other autoimmune diseases and cancer, as well as the impact of cancer on
the survival of patients with IIM. The observed familial aggregation of IIM and familial
associations between IIM and other autoimmune diseases suggest genetic involvement in the
development of IIM. Family history of IIM, other RIDs, inflammatory bowel diseases,
autoimmune thyroid diseases and celiac disease may serve as indicators pointing towards an
IIM diagnosis. Missing heritability is suggested by the discrepancy between our family-based
heritability and the SNP-based heritability, implying yet-to-be discovered genetic variants
associated with IIM. The acquired knowledge of shared familial factors between IIM and
other autoimmune diseases may inform future genetic studies aiming to uncover novel IIMassociated
genetic variants. There is a limited shared familial/genetic susceptibility between
IIM and cancer. The human leukocyte antigen region plays an important role in the limited
shared genetic susceptibility between IIM and common B-cell lymphoma subtypes. IIM
concomitant with cancer leads to a substantial increase in mortality, mainly due to cancer.
Future research should focus on reducing cancer-related disease burden in patients with IIM
The Commercialisation of English and Scottish Higher Education and its Impact on Academic Misconduct
This thesis aims to investigate the impact of the commercialisation of higher education in England and Scotland on academic misconduct. Commercialisation has positioned students as customers, which has been linked to a rise in student consumerism among them. It has also led to widening participation to include more students from non-traditional backgrounds who are more likely to struggle academically. In accordance with general strain theory, these students may experience strain due to an inability to attain a good grade through legitimate means, potentially leading them to turn to illegitimate means such as academic misconduct instead. Previous research has found a link between student consumerism and academic entitlement and between academic entitlement and academic misconduct. Based on this, the present study assessed how well academic entitlement mediated the effects of student consumerism and strain on students’ attitudes towards academic misconduct.To achieve this, data were collected from undergraduate and taught postgraduate students from across England and Scotland using an online questionnaire. Of the 432 responses retained for analysis, 421 were used in an SEM model to assess the relationships between the variables of concern. The results showed that student consumerism was positively related to academic entitlement, that academic entitlement was positively related to lenient attitudes towards academic misconduct, and that the relationship between student consumerism and attitudes towards academic misconduct was fully mediated by academic entitlement. Strain in the form of poor test-taking ability, attention problems, and course disinterest was positively related to academic entitlement, and academic entitlement was the strongest mediator of the relationship between strain and attitudes towards academic misconduct. Moreover, post-hoc tests revealed no significant differences in the student consumerism and academic entitlement of English and Scottish students or of students with differing levels of fee responsibility. The thesis therefore makes a significant contribution to knowledge by showing how two consequences of commercialisation, namely student consumerism and the strain experienced by a greater number of students, lead to more lenient attitudes towards academic misconduct through academic entitlement.<br/
Data- og ekspertdreven variabelseleksjon for prediktive modeller i helsevesenet : mot økt tolkbarhet i underbestemte maskinlæringsproblemer
Modern data acquisition techniques in healthcare generate large collections of data from multiple sources, such as novel diagnosis and treatment methodologies. Some concrete examples are electronic healthcare record systems, genomics, and medical images. This leads to situations with often unstructured, high-dimensional heterogeneous patient cohort data where classical statistical methods may not be sufficient for optimal utilization of the data and informed decision-making. Instead, investigating such data structures with modern machine learning techniques promises to improve the understanding of patient health issues and may provide a better platform for informed decision-making by clinicians. Key requirements for this purpose include (a) sufficiently accurate predictions and (b) model interpretability. Achieving both aspects in parallel is difficult, particularly for datasets with few patients, which are common in the healthcare domain. In such cases, machine learning models encounter mathematically underdetermined systems and may overfit easily on the training data. An important approach to overcome this issue is feature selection, i.e., determining a subset of informative features from the original set of features with respect to the target variable. While potentially raising the predictive performance, feature selection fosters model interpretability by identifying a low number of relevant model parameters to better understand the underlying biological processes that lead to health issues.
Interpretability requires that feature selection is stable, i.e., small changes in the dataset do not lead to changes in the selected feature set. A concept to address instability is ensemble feature selection, i.e. the process of repeating the feature selection multiple times on subsets of samples of the original dataset and aggregating results in a meta-model. This thesis presents two approaches for ensemble feature selection, which are tailored towards high-dimensional data in healthcare: the Repeated Elastic Net Technique for feature selection (RENT) and the User-Guided Bayesian Framework for feature selection (UBayFS). While RENT is purely data-driven and builds upon elastic net regularized models, UBayFS is a general framework for ensembles with the capabilities to include expert knowledge in the feature selection process via prior weights and side constraints. A case study modeling the overall survival of cancer patients compares these novel feature selectors and demonstrates their potential in clinical practice.
Beyond the selection of single features, UBayFS also allows for selecting whole feature groups (feature blocks) that were acquired from multiple data sources, as those mentioned above. Importance quantification of such feature blocks plays a key role in tracing information about the target variable back to the acquisition modalities. Such information on feature block importance may lead to positive effects on the use of human, technical, and financial resources if systematically integrated into the planning of patient treatment by excluding the acquisition of non-informative features. Since a generalization of feature importance measures to block importance is not trivial, this thesis also investigates and compares approaches for feature block importance rankings.
This thesis demonstrates that high-dimensional datasets from multiple data sources in the medical domain can be successfully tackled by the presented approaches for feature selection. Experimental evaluations demonstrate favorable properties of both predictive performance, stability, as well as interpretability of results, which carries a high potential for better data-driven decision support in clinical practice.Moderne datainnsamlingsteknikker i helsevesenet genererer store datamengder fra flere kilder, som for eksempel nye diagnose- og behandlingsmetoder. Noen konkrete eksempler er elektroniske helsejournalsystemer, genomikk og medisinske bilder. Slike pasientkohortdata er ofte ustrukturerte, høydimensjonale og heterogene og hvor klassiske statistiske metoder ikke er tilstrekkelige for optimal utnyttelse av dataene og god informasjonsbasert beslutningstaking. Derfor kan det være lovende å analysere slike datastrukturer ved bruk av moderne maskinlæringsteknikker for å øke forståelsen av pasientenes helseproblemer og for å gi klinikerne en bedre plattform for informasjonsbasert beslutningstaking. Sentrale krav til dette formålet inkluderer (a) tilstrekkelig nøyaktige prediksjoner og (b) modelltolkbarhet. Å oppnå begge aspektene samtidig er vanskelig, spesielt for datasett med få pasienter, noe som er vanlig for data i helsevesenet. I slike tilfeller må maskinlæringsmodeller håndtere matematisk underbestemte systemer og dette kan lett føre til at modellene overtilpasses treningsdataene. Variabelseleksjon er en viktig tilnærming for å håndtere dette ved å identifisere en undergruppe av informative variabler med hensyn til responsvariablen. Samtidig som variabelseleksjonsmetoder kan lede til økt prediktiv ytelse, fremmes modelltolkbarhet ved å identifisere et lavt antall relevante modellparametere. Dette kan gi bedre forståelse av de underliggende biologiske prosessene som fører til helseproblemer.
Tolkbarhet krever at variabelseleksjonen er stabil, dvs. at små endringer i datasettet ikke fører til endringer i hvilke variabler som velges. Et konsept for å adressere ustabilitet er ensemblevariableseleksjon, dvs. prosessen med å gjenta variabelseleksjon flere ganger på en delmengde av prøvene i det originale datasett og aggregere resultater i en metamodell. Denne avhandlingen presenterer to tilnærminger for ensemblevariabelseleksjon, som er skreddersydd for høydimensjonale data i helsevesenet: "Repeated Elastic Net Technique for feature selection" (RENT) og "User-Guided Bayesian Framework for feature selection" (UBayFS). Mens RENT er datadrevet og bygger på elastic net-regulariserte modeller, er UBayFS et generelt rammeverk for ensembler som muliggjør inkludering av ekspertkunnskap i variabelseleksjonsprosessen gjennom forhåndsbestemte vekter og sidebegrensninger. En case-studie som modellerer overlevelsen av kreftpasienter sammenligner disse nye variabelseleksjonsmetodene og demonstrerer deres potensiale i klinisk praksis.
Utover valg av enkelte variabler gjør UBayFS det også mulig å velge blokker eller grupper av variabler som representerer de ulike datakildene som ble nevnt over. Kvantifisering av viktigheten av variabelgrupper spiller en nøkkelrolle for forståelsen av hvorvidt datakildene er viktige for responsvariablen. Tilgang til slik informasjon kan føre til at bruken av menneskelige, tekniske og økonomiske ressurser kan forbedres dersom informasjonen integreres systematisk i planleggingen av pasientbehandlingen. Slik kan man redusere innsamling av ikke-informative variabler. Siden generaliseringen av viktighet av variabelgrupper ikke er triviell, undersøkes og sammenlignes også tilnærminger for rangering av viktigheten til disse variabelgruppene.
Denne avhandlingen viser at høydimensjonale datasett fra flere datakilder fra det medisinske domenet effektivt kan håndteres ved bruk av variabelseleksjonmetodene som er presentert i avhandlingen. Eksperimentene viser at disse kan ha positiv en effekt på både prediktiv ytelse, stabilitet og tolkbarhet av resultatene. Bruken av disse variabelseleksjonsmetodene bærer et stort potensiale for bedre datadrevet beslutningsstøtte i klinisk praksis
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