Everything on the Table: Tabular, Graphic, and Interactive Approaches for Interpreting and Presenting Monte Carlo Simulation Data

Abstract Monte Carlo simulation studies (MCSS) form a cornerstone for quantitative methods research. They are frequently used to evaluate and compare the properties of statistical methods and inform both future research and current best practices. However, the presentation of results from MCSS often leaves much to be desired, with findings typically conveyed via a series of elaborate tables from which readers are expected to derive meaning. The goal of this dissertation is to explore, summarize, and describe a framework for the presentation of MCSS, and show how modern computing and visualization techniques improve their interpretability. Chapter One describes this problem by introducing the logic of MCSS, how they are conducted, what findings typically look like, and current practices for their presentation. Chapter Two demonstrates methods for improving the display of static tabular data, specifically via formatting, effects ordering, and rotation. Chapter Three delves into semi-graphic and graphical approaches for aiding the presentation of tabular data via shaded tables, and extensions to the tableplot and the hypothesis-error plot frameworks. Chapter Four describes the use of interactive computing applets to aid the exploration of complex tabular data, and why this is an ideal approach. Throughout this work, emphasis is placed on how such techniques improve our understanding of a particular dataset or model. Claims are supported with applied demonstrations. Implementation of the ideas from each chapter have been coded within the R language for statistical computing and are available for adoption by other researchers in a dedicated package (SimDisplay). It is hoped that these ideas might enhance our understanding of how to best present MCSS findings and be drawn upon in both applied and academic environments

Recent Advances in Visualizing Multivariate Linear Models

This paper reviews our work in the development of visualization methods (implemented in R) for understanding and interpreting the effects of predictors in multivariate linear models (MLMs) of the form Y = XB + U, and some of their recent extensions.We begin with a description of and examples from the Hypothesis-Error (HE) plots framework (utilizing the heplots package), wherein multivariate tests can be visualized via ellipsoids in 2D, 3D or all pairwise views for the Hypothesis and Error Sum of Squares and Products (SSP) matrices used in hypothesis tests. Such HE plots provide visual tests of significance: a term is significant by Roy’s test if and only if its H ellipsoid projects somewhere outside the E ellipsoid. These ideas extend naturally to repeated measures designs in the multivariate context. When the rank of the hypothesis matrix for a term exceeds 2, these effects can also be visualized in a reduced-rank canonical space via the candisc package, which also provides new data plots for canonical correlation problems. Finally, we discuss some recent work-in-progress: the extension of these methods to robust MLMs, and the development of generalizations of influence measures and diagnostic plots for MLMs (in the mvinfluence package).Este artículo hace una revisión de los desarrollos recientes en métodos de visualización (implementados en R) para la comprensión e interpretación de los efectos de los predictores en modelos lineales multivariados (MLMs) de la forma Y = XB + U y sus extensiones recientes. Comenzamos con una descripción y ejemplos de los gráficos de Hipótesis- Error (HE), (utilizando el paquete heplots) en los cuales los tests multivariados son visualizados vía elipsoides en 2D, 3D o todas las vistas pareadas de las matrices de sumas de cuadrados y productos (SSP por sus siglas en inglés) de Hipótesis y Error. Las gráficas HE permiten pruebas de significancia visuales: un término es significativo en el test de Roy si y solo si su elipsoide H es proyectado fuera del elipsoide E. Estas ideas se extienden a diseños de medidas repetidas en el contexto multivariado. Cuando el rango de la matriz de hipótesis para un término es mayor a 2, estos efectos pueden ser visualizados en un espacio canónico de rango reducido vía el paquete candisc, que a su vez también permite nuevos gráficos para problemas de correlación canónica. Finalmente, se discuten algunas áreas de investigación en desarrollo: la extensión de estos métodos a MLMs robustos, generalizaciones de las medidas de influencia y gráficas de diagnóstico para MLMs (en el paquete mvinfluence)

Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy.

The success of immunotherapy has led to a myriad of clinical trials accompanied by efforts to gain mechanistic insight and identify predictive signatures for personalization. However, many immune monitoring technologies face investigator bias, missing unanticipated cellular responses in limited clinical material. We present here a mass cytometry (CyTOF) workflow for standardized, systems-level biomarker discovery in immunotherapy trials. To broadly enumerate immune cell identity and activity, we established and extensively assessed a reference panel of 33 antibodies to cover major cell subsets, simultaneously quantifying activation and immune checkpoint molecules in a single assay. This assay enumerates ≥98% of peripheral immune cells with ≥4 positively identifying antigens. Robustness and reproducibility are demonstrated on multiple samples types, across two research centers and by orthogonal measurements. Using automated analysis, we identify stratifying immune signatures in bone marrow transplantation-associated graft-versus-host disease. Together, this validated workflow ensures comprehensive immunophenotypic analysis and data comparability and will accelerate biomarker discovery

Multi-modal News Understanding with Professionally Labelled Videos (ReutersViLNews)

While progress has been made in the domain of video-language understanding, current state-of-the-art algorithms are still limited in their ability to understand videos at high levels of abstraction, such as news-oriented videos. Alternatively, humans easily amalgamate information from video and language to infer information beyond what is visually observable in the pixels. An example of this is watching a news story, where the context of the event can play as big of a role in understanding the story as the event itself. Towards a solution for designing this ability in algorithms, we present a large-scale analysis on an in-house dataset collected by the Reuters News Agency, called Reuters Video-Language News (ReutersViLNews) dataset which focuses on high-level video-language understanding with an emphasis on long-form news. The ReutersViLNews Dataset consists of long-form news videos collected and labeled by news industry professionals over several years and contains prominent news reporting from around the world. Each video involves a single story and contains action shots of the actual event, interviews with people associated with the event, footage from nearby areas, and more. ReutersViLNews dataset contains videos from seven subject categories: disaster, finance, entertainment, health, politics, sports, and miscellaneous with annotations from high-level to low-level, title caption, visual video description, high-level story description, keywords, and location. We first present an analysis of the dataset statistics of ReutersViLNews compared to previous datasets. Then we benchmark state-of-the-art approaches for four different video-language tasks. The results suggest that news-oriented videos are a substantial challenge for current video-language understanding algorithms and we conclude by providing future directions in designing approaches to solve the ReutersViLNews dataset

Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy

The success of immunotherapy has led to a myriad of clinical trials accompanied by efforts to gain mechanistic insight and identify predictive signatures for personalization. However, many immune monitoring technologies face investigator bias, missing unanticipated cellular responses in limited clinical material. We present here a mass cytometry (CyTOF) workflow for standardized, systems-level biomarker discovery in immunotherapy trials. To broadly enumerate immune cell identity and activity, we established and extensively assessed a reference panel of 33 antibodies to cover major cell subsets, simultaneously quantifying activation and immune checkpoint molecules in a single assay. This assay enumerates >= 98% of peripheral immune cells with >= 4 positively identifying antigens. Robustness and reproducibility are demonstrated on multiple samples types, across two research centers and by orthogonal measurements. Using automated analysis, we identify stratifying immune signatures in bone marrow transplantation-associated graft-versus-host disease. Together, this validated workflow ensures comprehensive immunophenotypic analysis and data comparability and will accelerate biomarker discovery

HIV-1 Vpr drives a tissue residency-like phenotype during selective infection of resting memory T cells

HIV-1 replicates in CD4+ T cells, leading to AIDS. Determining how HIV-1 shapes its niche to create a permissive environment is central to informing efforts to limit pathogenesis, disturb reservoirs, and achieve a cure. A key roadblock in understanding HIV-T cell interactions is the requirement to activate T cells in vitro to make them permissive to infection. This dramatically alters T cell biology and virus-host interactions. Here we show that HIV-1 cell-to-cell spread permits efficient, productive infection of resting memory T cells without prior activation. Strikingly, we find that HIV-1 infection primes resting T cells to gain characteristics of tissue-resident memory T cells (TRM), including upregulating key surface markers and the transcription factor Blimp-1 and inducing a transcriptional program overlapping the core TRM transcriptional signature. This reprogramming is driven by Vpr and requires Vpr packaging into virions and manipulation of STAT5. Thus, HIV-1 reprograms resting T cells, with implications for viral replication and persistence

HIV-1 Vpr drives a tissue residency-like phenotype during selective infection of resting memory T cells.

open access articleHIV-1 replicates in CD4+ T cells, leading to AIDS. Determining how HIV-1 shapes its niche to create a permissive environment is central to informing efforts to limit pathogenesis, disturb reservoirs, and achieve a cure. A key roadblock in understanding HIV-T cell interactions is the requirement to activate T cells in vitro to make them permissive to infection. This dramatically alters T cell biology and virus-host interactions. Here we show that HIV-1 cell-to-cell spread permits efficient, productive infection of resting memory T cells without prior activation. Strikingly, we find that HIV-1 infection primes resting T cells to gain characteristics of tissue-resident memory T cells (TRM), including upregulating key surface markers and the transcription factor Blimp-1 and inducing a transcriptional program overlapping the core TRM transcriptional signature. This reprogramming is driven by Vpr and requires Vpr packaging into virions and manipulation of STAT5. Thus, HIV-1 reprograms resting T cells, with implications for viral replication and persistence

Insulin Dose and Cardiovascular Mortality in the ACCORD Trial

In the ACCORD trial, intensive treatment of patients with type 2 diabetes and high cardiovascular (CV) risk was associated with higher all-cause and CV mortality. Post hoc analyses have failed to implicate rapid reduction of glucose, hypoglycemia, or specific drugs as the causes of this finding. We hypothesized that exposure to injected insulin was quantitatively associated with increased CV mortality

Cost-effectiveness of Population Screening for BRCA Mutations in Ashkenazi Jewish Women Compared With Family History-Based Testing

BACKGROUND: Population-based testing for BRCA1/2 mutations detects the high proportion of carriers not identified by cancer family history (FH)-based testing. We compared the cost-effectiveness of population-based BRCA testing with the standard FH-based approach in Ashkenazi Jewish (AJ) women. METHODS: A decision-analytic model was developed to compare lifetime costs and effects amongst AJ women in the UK of BRCA founder-mutation testing amongst: 1) all women in the population age 30 years or older and 2) just those with a strong FH (≥10% mutation risk). The model assumes that BRCA carriers are offered risk-reducing salpingo-oophorectomy and annual MRI/mammography screening or risk-reducing mastectomy. Model probabilities utilize the Genetic Cancer Prediction through Population Screening trial/published literature to estimate total costs, effects in terms of quality-adjusted life-years (QALYs), cancer incidence, incremental cost-effectiveness ratio (ICER), and population impact. Costs are reported at 2010 prices. Costs/outcomes were discounted at 3.5%. We used deterministic/probabilistic sensitivity analysis (PSA) to evaluate model uncertainty. RESULTS: Compared with FH-based testing, population-screening saved 0.090 more life-years and 0.101 more QALYs resulting in 33 days' gain in life expectancy. Population screening was found to be cost saving with a baseline-discounted ICER of -£2079/QALY. Population-based screening lowered ovarian and breast cancer incidence by 0.34% and 0.62%. Assuming 71% testing uptake, this leads to 276 fewer ovarian and 508 fewer breast cancer cases. Overall, reduction in treatment costs led to a discounted cost savings of £3.7 million. Deterministic sensitivity analysis and 94% of simulations on PSA (threshold £20000) indicated that population screening is cost-effective, compared with current NHS policy. CONCLUSION: Population-based screening for BRCA mutations is highly cost-effective compared with an FH-based approach in AJ women age 30 years and older