Model Selection for Hierarchical Poisson Modeling in Disease Mapping

In disease mapping where predictor effects are to be modeled, it is often the case that sets of predictors are fixed and the aim is to choose between fixed model sets. In this dissertation, I focus on this dimension reduction objective by applying the Poisson data model commonly used for disease mapping of small area health data. I begin with a software comparison of the recently developed R package INLA (Integrated Nested Laplace Approximation) to the MCMC approach by way of the BRugs package in R, which calls OpenBUGS. This software comparison leads to choosing the appropriate platform for carrying out the second portion of this work: a methodology comparison of my proposed non-spatial and spatial approaches of Bayesian model selection to Bayesian Model Averaging. Following that, for the third and final aim, I extend my Bayesian model selection methodologies to the spatio-temporal setting and evaluate the benefit and usefulness of four different modeling approaches. These explorations demonstrate the importance of altering the defaults in INLA and the flexibility of the BUGS software. Additionally, they offer a novel way of determining appropriate linear predictors in the context of non-spatial, spatial, and spatio-temporal small area health data in disease mapping

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

National Prison Rape Elimination Commission (NPREC) Report

Commissioner Brenda V. Smith Commissioner Brenda V. Smith is a Professor at American University’s Washington College of Law, where she teaches community and economic development law, legal ethics and women, and crime and law. Her research interests center on women in conflict with the law and on sexual abuse of individuals in custody. Professor Smith is also Project Director and Principal Investigator for the U.S. Department of Justice’s National Institute of Corrections Cooperative Agreement on Addressing Staff Sexual Misconduct with Offenders. She is an expert on issues affecting women in prison, a topic about which she has widely published and spoken. Before her appointment to the faculty of the Washington College of Law, Professor Smith was Senior Counsel for Economic Security at the National Women’s Law Center. She has also served as the Director of the Center’s Women in Prison Project and its Child and Family Support Project. Professor Smith earned her Bachelor of Arts from Spelman College and her Juris Doctor from Georgetown University Law Center

National Prison Rape Elimination Commission (NPREC) Report

Commissioner Brenda V. Smith Commissioner Brenda V. Smith is a Professor at American University’s Washington College of Law, where she teaches community and economic development law, legal ethics and women, and crime and law. Her research interests center on women in conflict with the law and on sexual abuse of individuals in custody. Professor Smith is also Project Director and Principal Investigator for the U.S. Department of Justice’s National Institute of Corrections Cooperative Agreement on Addressing Staff Sexual Misconduct with Offenders. She is an expert on issues affecting women in prison, a topic about which she has widely published and spoken. Before her appointment to the faculty of the Washington College of Law, Professor Smith was Senior Counsel for Economic Security at the National Women’s Law Center. She has also served as the Director of the Center’s Women in Prison Project and its Child and Family Support Project. Professor Smith earned her Bachelor of Arts from Spelman College and her Juris Doctor from Georgetown University Law Center

Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer.

INTRODUCTION: Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice. METHODS: More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer 'stem' cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account. RESULTS: The 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working. CONCLUSIONS: With resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years

Elective cancer surgery in COVID-19-free surgical pathways during the SARS-CoV-2 pandemic : an international, multicenter, comparative cohort study

PURPOSE As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19-free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19-free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19-free surgical pathways. Patients who underwent surgery within COVID-19-free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19-free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score-matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19-free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION Within available resources, dedicated COVID-19-free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press