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A Bayesian Joint Bent-cable Model for Longitudinal Measurements and Survival Time with Heterogeneous Random-effects Distributions
Get PDFBiomarkers are measured repeatedly in clinical studies until a pre-defined endpoint, such as death from certain causes, is reached. Such repeated measurements may present a dynamic process for understanding when to expect the study's endpoint. Joint modelling is often employed to handle such a model. Typically, shared random effects are assumed to be common to both the longitudinal component and the study's endpoint. These shared random effects usually assume homogeneous and follow a normal distribution. However, identifying homogeneous subgroups is important when the underlying population is heterogeneous. This issue has received little attention in the literature, particularly for multi-phase longitudinal responses. In this paper, we propose a joint modelling approach for longitudinal and survival models using a bent-cable mixed model for longitudinal measurements and a Weibull distribution for the survival component. We also incorporate finite mixture of normal distribution assumptions to account for the unobserved heterogeneity in the shared random effects model. A Bayesian MCMC is developed for parameter estimation and inferences. The proposed method is evaluated using simulation studies and the Tehran Lipid and Glucose Study dataset
How to Present Answers to Your Research Questions: The Fourth Stage of The Social Research Toolbox, QGAP [How-to Guide]
Get PDFOnce you have gathered data, examined the evidence and undertaken a systematic analysis, it is time to present your findings or your answers to your research question(s). In this guide, we consider the last stage in the social research toolbox, or QGAP, series. We first discuss some of the motivations for publishing or writing up your work and how to get started with the writing process. The guide then explores some of the conventions around how to present qualitative and quantitative findings, focusing on ‘academic voice’ and how to make data accessible and informative for your reader through tables, graphs, quotations and typologies. Then particular attention is paid to the types of information about the process of research that ought to be communicated to enable audiences to make a judgement about the credibility of your research claims. This guide therefore is not only useful to those writing up research but also those wishing to appraise the research claims of others
Predictive Quantile Regressions with Persistent and Heteroskedastic Predictors: A Powerful 2SLS Testing Approach
Get PDFWe develop new tests for predictability at a given quantile, based on the Lagrange Multiplier [LM] principle, in the context of quantile regression [QR] models which allow for persistent and endogenous predictors driven by heteroskedastic errors. Of the extant predictive QR tests in the literature, only the moving blocks bootstrap implementation, due to Fan and Lee (2019), of the Wald-type test of Lee (2016) can allow for conditionally heteroskedastic errors in the context of a QR model with persistent predictors. In common with all other tests in the literature these tests cannot, however, allow for unconditionally heteroskedastic behaviour in the errors. The LM- based approach we adopt in this paper is obtained from a simple auxiliary linear test regression which facilitates inference based on established instrumental variable methods. We demonstrate that, as a result, the tests we develop, based on either conventional or heteroskedasticity- consistent standard errors in the auxiliary regression, are robust under the null hypothesis of no predictability to conditional heteroskedasticity and to unconditional heteroskedasticity in the errors driving the predictors, with no need for bootstrap implementation. We also propose tests for joint predictability across a set of multiple distinct quantiles. Simulation results for both conditionally and unconditionally heteroskedastic errors highlight the superior finite sample properties of our proposed LM tests over the tests of Lee (2016) and Fan and Lee (2019) and the recent variable addition tests of Cai et al. (2023). An empirical application to the equity premium for the S&P 500 highlights the practical usefulness of our proposed tests, uncovering significant evidence of predictability in the left and right tails of the returns distribution for a number of predictors containing information on market or firm risk
Examining the Origins and Outcomes of Research-Related Emotions in Faculty: Developing the Research Emotions Questionnaire (REQ)
No full textUniversity faculty experience many emotions that have implications for their research success; however, previous studies on research-related emotions in faculty have consistently employed self-report measures with limited validity, reliability, and scope. The current study aimed to validate the Research Emotions Questionnaire (REQ) among STEM faculty, examine potential differences in emotions by demographic and job-related factors, and test a hypothesized model of emotions as predictors of faculty research success based on Pekrun’s control-value theory (CVT). An online survey was completed by 611 STEM faculty from 10 research-intensive US universities, with the data showing the REQ to be valid and reliable. Women reported more anxiety and disappointment, underrepresented minorities reported more anxiety, and full professors reported more enjoyment and pride, as well as less anxiety and disappointment, compared to junior colleagues. Structural equation modeling results showed perceived control and value appraisals significantly predicted research emotions and, in turn, self-reported research success. Negative binomial regressions revealed enjoyment, boredom, disappointment, and frustration as significant predictors of bibliometric counts of publications and citations. The REQ is an improved tool for understanding faculty research emotions, with implications for developing targeted emotional regulation programs to enhance faculty well-being, success, and job satisfaction, particularly for underrepresented groups
Learn more from your data with asymptotic regression
Get PDFAll measures of behaviour have a temporal context, and the context often takes a similar form: monotonically decreasing or increasing towards an asymptote. Whether these behavioural dynamics are the object of study or a nuisance variable, their inclusion in models of data makes conclusions more complete, robust and well-specified, and can contribute to theory development. Here we demonstrate that asymptotic regression is a relatively simple tool that can be applied to repeated-measures data to estimate three parameters: starting point, rate of change, and asymptote. Each of these parameters has a meaningful interpretation in terms of ecological validity, learning and performance limits, respectively. They can also be used to help decide how many trials to include in an experiment, and as a principled approach to reducing noise in data. We demonstrate the broad utility of asymptotic regression for modeling the effect of the passage of time within a single trial, and for changes over trials of an experiment, using four simple examples from existing data. An important limit of asymptotic regression is that it cannot be applied to data that is stationary or changes non-monotonically. But for data that has performance changes that progress steadily towards an asymptote, as many behavioural measures do, it is a simple and powerful tool for describing those changes
The Impact of Sleep on Breast Cancer-Specific Mortality: A Mendelian Randomisation Study
Get PDFBackground: The relationship between sleep traits and survival in breast cancer is uncertain and complex. There are multiple biological, psychological and treatment-related factors that could link sleep and cancer outcomes. Previous studies could be biased due to methodological limitations such as reverse causation and confounding. Here, we used two-sample mendelian randomisation (MR) to investigate the causal relationship between sleep and breast cancer mortality.
Methods: Publicly available genetic summary data from females of European ancestry from UK Biobank and 23andme and the Breast Cancer Association Consortium were used to generate instrumental variables for sleep traits (chronotype, insomnia symptoms, sleep duration, napping, daytime-sleepiness, and ease of getting up (N= 446,118-1,409,137)) and breast cancer outcomes (15 years post-diagnosis, stratified by tumour subtype and treatment (N=91,686 and Ndeaths=7,531 over a median follow-up of 8.1 years)). Sensitivity analyses were used to assess the robustness of analyses to MR assumptions.
Results: Initial results found some evidence for a per category increase in daytime-sleepiness reducing overall breast cancer mortality (HR=0.34, 95% CI=0.14, 0.80), and for insomnia symptoms reducing odds of mortality in oestrogen receptor positive breast cancers not receiving chemotherapy (HR=0.18, 95% CI=0.05, 0.68) and in patients receiving aromatase inhibitors (HR=0.23, 95% CI=0.07, 0.78). Importantly, these relationships were not robust following sensitivity analyses meaning we could not demonstrate any causal relationships.
Conclusions: This study did not provide evidence that sleep traits have a causal role in breast cancer mortality. Further work characterising disruption to normal sleep behaviours and its effects on tumour biology, treatment compliance and quality of life are needed
Masculinity Threats Sequentially Arouse Public Discomfort, Anger, and Positive Attitudes Toward Sexual Violence
Get PDFThree experiments ( N = 943) tested whether men (but not women) responded to gender threats with increased concern about how one looks in the eyes of others (i.e., public discomfort) and subsequent anger that, in turn, predicted attitudes about sexual violence. Consistent with predictions, for men, learning that one is like a woman was associated with threat-related emotions (public discomfort and anger) that, in turn, predicted the increased likelihood to express intent to engage in quid-pro-quo sexual harassment (Study 1), recall sexually objectifying others (Study 2), endorse sexual narcissism (Study 2), and accept rape myths (Study 3). These findings support the notion that failures to uphold normative and socially valued embodiments of masculinity are associated with behavioral intentions and attitudes associated with sexual violence. The implications of these findings for the endurance of sexual violence are discussed
Deep learning over spatial data: From a 3D reconstructive perspective
Get PDFThe ability to create and interact with high-fidelity digital representations of the physical world is crucial for applications ranging from digital twins to autonomous navigation. Yet, the robust interpretation and reconstruction of 3D environments from sparse or noisy observations remains a significant challenge for artificial intelligence. This thesis addresses the fundamental problems in 3D reconstruction by exploring the evolving landscape of deep learning over spatial data, from structured parametric models to flexible implicit representations. Our research navigates the trade-offs between these paradigms to develop novel methods that enhance reconstruction fidelity, efficiency, and user control. The investigation begins by confronting the limitations of explicit parametric methods in handling complex topologies from unstructured point clouds. This analysis motivates a pivot towards implicit neural representations. Our work introduces key innovations in this area, including Seed-Net, an interactive framework that differentiably incorporates sparse user guidance to refine local geometric details in neural fields, and NeuLap, a geometry-aware training scheme that leverages a learned Laplacian prior to refine the convergence process and improve the reconstruction of sharp features from limited data. Building on these insights, the research path leads to the development of a general-purpose backbone for large-scale 3D learning: a Hierarchical Attention OctTree. This architecture introduces a novel attention propagation mechanism that efficiently captures multi-scale spatial context, demonstrating competitive performance and memory efficiency. Collectively, these contributions offer a suite of methods and a conceptual roadmap for 3D spatial learning. Through advancements in interactive reconstruction, prior guided optimization, and scalable deep learning architectures, this research contributes to the field of representing and reconstructing high-fidelity 3D spatial data, providing technical insights and solutions that are valuable for various future applications in fields such as digital twins, robotics, and creative design
Analyzing the Impact of Transmission Strategies on Localization Performance in Wireless Sensor Networks
Get PDFLocalization, essential in WSN applications, enables sensor nodes to determine their physical positions by referencing anchor nodes. We evaluate broadcast and unicast packet transmissions at the data-link layer for their impact on localization performance. Implemented on the Contiki-NG operating system, the study examines how anchor node density and antenna range affect localization success and the number of required anchor nodes between broadcast-based and unicast-based localization propagation in protocol stack. Results using Cooja simulator, demonstrate the trade-offs between unicast and broadcast transmission approaches, particularly in terms of network overhead, energy consumption and localization performance. For instance, with an antenna range of 20 meters, achieving a localization ratio of over 90% requires only 20% anchor density with broadcast transmission, whereas unicast transmission requires a 60% anchor density to achieve the same ratio. This demonstrates that broadcast localization can lead to approximately a 33% reduction in hardware costs, offering significant efficiency gains. These findings provide insights into optimal propagation techniques and highlight the advantages of broadcasting in resource-constrained WSN deployments
Do empathic people respond differently to emotional voices?
Get PDFPast research on the use of motivational voice (or motivational prosody) has found that the way we modulate acoustic cues when we speak can have profound effects on others. However, it is unclear whether the effects also hold for other forms of social communication, such as emotional tone of voice, and what role empathy plays. Across three experiments (two preregistered), we found very large effects indicating that listening to an angry vs. happy voice reduced positive affect in participants, lowered their self-esteem, and eroded their intention to disclose information. These effects were mediated by perceived effort to interact with the speaker, feelings of discomfort, and norm violation, which were higher for an angry voice than for a happy one. Importantly, the effects were, as predicted, stronger for participants scoring high in cognitive empathy and especially affective resonance: More empathic people reported even lower positive affect, self-esteem, and intention to disclose information after listening to the angry vs. happy sounding speaker. This suggests that empathic people are more strongly affected by the tone of voice, even if emotions are only conveyed through vocal tone, without face-to-face interaction. Our findings help to advance related research areas and have important implications for clinical and organizational settings. (PsycInfo Database Record (c) 2025 APA, all rights reserved)