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Register a new userLymphangiogenesis and carcinoma in the uterine cervix: Joint and hierarchical models for random cluster sizes and continuous outcomes
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Added by
T. R. Fanshawe
Publication date
2016
fields
Mathematical Statistics
and research's language is
English
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Although the lymphatic system is clearly linked to the metastasis of most human carcinomas, the mechanisms by which lymphangiogenesis occurs in response to the presence of carcinoma remain unclear. Hierarchical models are presented to investigate the properties of lymphatic vessel production in 2997 fields taken from 20 individuals with invasive carcinoma, 21 individuals with cervical intraepithelial neoplasia and 21 controls. Such data demonstrate a high degree of correlation within tumour samples from the same individual. Joint hierarchical models utilising shared random effects are discussed and fitted in a Bayesian framework to allow for the correlation between two key outcome measures: a random cluster size (the number of lymphatic vessels in a tissue sample) and a continuous outcome (vessel size). Results show that invasive carcinoma samples are associated with increased production of smaller and more irregularly-shaped lymphatic vessels and suggest a mechanistic link between carcinoma of the cervix and lymphangiogenesis.
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Continuous-time assessments of game outcomes in sports have become increasingly common in the last decade. In American football, only discrete-time estimates of play value were possible, since the most advanced public football datasets were recorded at the play-by-play level. While measures such as expected points and win probability are useful for evaluating football plays and game situations, there has been no research into how these values change throughout the course of a play. In this work, we make two main contributions: First, we introduce a general framework for continuous-time within-play valuation in the National Football League using player-tracking data. Our modular framework incorporates several modular sub-models, to easily incorporate recent work involving player tracking data in football. Second, we use a long short-term memory recurrent neural network to construct a ball-carrier model to estimate how many yards the ball-carrier is expected to gain from their current position, conditional on the locations and trajectories of the ball-carrier, their teammates and opponents. Additionally, we demonstrate an extension with conditional density estimation so that the expectation of any measure of play value can be calculated in continuous-time, which was never before possible at such a granular level.
Studying the determinants of adverse pregnancy outcomes like stillbirth and preterm birth is of considerable interest in epidemiology. Understanding the role of both individual and community risk factors for these outcomes is crucial for planning appropriate clinical and public health interventions. With this goal, we develop geospatial mixed effects logistic regression models for adverse pregnancy outcomes. Our models account for both spatial autocorrelation and heterogeneity between neighborhoods. To mitigate the low incidence of stillbirth and preterm births in our data, we explore using class rebalancing techniques to improve predictive power. To assess the informative value of the covariates in our models, we use posterior distributions of their coefficients to gauge how well they can be distinguished from zero. As a case study, we model stillbirth and preterm birth in the city of Philadelphia, incorporating both patient-level data from electronic health records (EHR) data and publicly available neighborhood data at the census tract level. We find that patient-level features like self-identified race and ethnicity were highly informative for both outcomes. Neighborhood-level factors were also informative, with poverty important for stillbirth and crime important for preterm birth. Finally, we identify the neighborhoods in Philadelphia at highest risk of stillbirth and preterm birth.
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