Nonparametric variable importance assessment using machine learning techniques

In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the regression technique employed. For this reason, practitioners often only resort to one of a few regression techniques for which a variable importance measure is naturally defined. Unfortunately, these regression techniques are often sub-optimal for predicting response. Additionally, because the variable importance measures native to different regression techniques generally have a different interpretation, comparisons across techniques can be difficult. In this work, we study a novel variable importance measure that can be used with any regression technique, and whose interpretation is agnostic to the technique used. Specifically, we propose a generalization of the ANOVA variable importance measure, and discuss how it facilitates the use of possibly-complex machine learning techniques to flexibly estimate the variable importance of a single feature or group of features. Using the tools of targeted learning, we also describe how to construct an efficient estimator of this measure, as well as a valid confidence interval. Through simulations, we show that our proposal has good practical operating characteristics, and we illustrate its use with data from a study of the median house price in the Boston area, and a study of risk factors for cardiovascular disease in South Africa

Adaptive Non-Inferiority Margins under Observable Non-Constancy

A central assumption in the design and conduct of non-inferiority trials is that the active-control therapy will have the same degree of effectiveness in the planned non-inferiority trial as it had in the prior placebo-controlled trials used to define the non-inferiority margin. This is referred to as the `constancy\u27 assumption. If the constancy assumption fails, the chosen non-inferiority margin is not valid and the study runs the risk of approving an inferior product or failing to approve a beneficial product. The constancy assumption cannot be validated in a trial without a placebo arm, and it is unlikely ever to be met completely. However, it is often the case that there exist strong, measurable predictors of constancy, such as dosing and adherence, and such predictors can be used to identify situations where the constancy assumption will likely fail. Here we propose a method for using measurable predictors of active-control effectiveness to specify non-inferiority margins targeted to the planned study population, and further use these predictors to adapt the non-inferiority margin at the end of the study. Population-specific margins can help avoid violations of the constancy assumption, and adaptive margins can help adjust for violations that will inevitably occur in real clinical trials, while at the same time maintain pre-specified levels of type I error and power

DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple ([Formula: see text]40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples

The Economic Impact of Transvenous Defibrillation Lead Systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72437/1/j.1540-8159.1994.tb02379.x.pd

NASA Ocean Altimeter Pathfinder Project

The NOAA/NASA Pathfinder program was created by the Earth Observing System (EOS) Program Office to determine how satellite-based data sets can be processed and used to study global change. The data sets are designed to be long time-sedes data processed with stable calibration and community consensus algorithms to better assist the research community. The Ocean Altimeter Pathfinder Project involves the reprocessing of all altimeter observations with a consistent set of improved algorithms, based on the results from TOPEX/POSEIDON (T/P), into easy-to-use data sets for the oceanographic community for climate research. This report describes the processing schemes used to produce a consistent data set and two of the products derived f rom these data. Other reports have been produced that: a) describe the validation of these data sets against tide gauge measurements and b) evaluate the statistical properties of the data that are relevant to climate change. The use of satellite altimetry for earth observations was proposed in the early 1960s. The first successful space based radar altimeter experiment was flown on SkyLab in 1974. The first successful satellite radar altimeter was flown aboard the Geos-3 spacecraft between 1975 and 1978. While a useful data set was collected from this mission for geophysical studies, the noise in the radar measured and incomplete global coverage precluded ft from inclusion in the Ocean Altimeter Pathfinder program. This program initiated its analysis with the Seasat mission, which was the first satellite radar altimeter flown for oceanography

Protein C deficiency and acute myocardial infarction in the third decade

Protein C deficiency has been associated with a predisposition to venous thrombosis and thromboembolism. Arterial thrombosis has been seen much less frequently and may require other vascular risk factors. Here we describe a young patient with protein C deficiency presenting with an acute myocardial infarction (AMI).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29245/1/0000302.pd

Comparative genomic analyses reveal broad diversity in botulinum-toxin-producing Clostridia

Background: Clostridium botulinum is a diverse group of bacteria characterized by the production of botulinum neurotoxin. Botulinum neurotoxins are classified into serotypes (BoNT/A-G), which are produced by six species/Groups of Clostridia, but the genetic background of the bacteria remains poorly understood. The purpose of this study was to use comparative genomics to provide insights into the genetic diversity and evolutionary history of bacteria that produce the potent botulinum neurotoxin. Results: Comparative genomic analyses of over 170 Clostridia genomes, including our draft genome assemblies for 59 newly sequenced Clostridia strains from six continents and publicly available genomic data, provided in-depth insights into the diversity and distribution of BoNT-producing bacteria. These newly sequenced strains included Group I and II strains that express BoNT/A,/B,/E, or/F as well as bivalent strains. BoNT-producing Clostridia and closely related Clostridia species were delineated with a variety of methods including 16S rRNA gene, concatenated marker genes, core genome and concatenated multi-locus sequencing typing (MLST) gene phylogenies that related whole genome sequenced strains to publicly available strains and sequence types. These analyses illustrated the phylogenetic diversity in each Group and the diversity of genomic backgrounds that express the same toxin type or subtype. Comparisons of the botulinum neurotoxin genes did not identify novel toxin types or variants. Conclusions: This study represents one of the most comprehensive analyses of whole genome sequence data for Group I and II BoNT-producing strains. Read data and draft genome assemblies generated for 59 isolates will be a resource to the research community. Core genome phylogenies proved to be a powerful tool for differentiating BoNT-producing strains and can provide a framework for the study of these bacteria. Comparative genomic analyses of Clostridia species illustrate the diversity of botulinum-neurotoxin-producing strains and the plasticity of the genomic backgrounds in which bont genes are found.Peer reviewe

Stories of self, us, and now: narrative and power for health equity in grassroots community organizing

IntroductionCommunity organizing initiatives, which build power through cycles of listening, participatory research, collective action, and reflection, have demonstrated the capacity to intervene on, complicate, and resist dominant societal narratives while promoting alternative public narratives focused on shared values and hope for a better future.MethodsTo explore processes of public narrative change and their relationship to community and organizational empowerment, we interviewed 35 key leaders in community organizing initiatives in Detroit, MI and Cincinnati, OH about how narrative change takes place within community organizing practices.ResultsLeaders’ perspectives revealed crucial roles for narrative and storytelling in guiding individual and collective behavior, supporting the development of relationships of trust and accountability, and linking personal and collective experiences to pressing social issues.DiscussionFindings from this study indicate that systemic change is a labor-intensive process and one that requires the development of leaders (stories of self) and the cultivation of collective structures (stories of us) capable of enacting power to effect change with urgency (stories of now). We conclude by discussing implications of these findings for public narrative interventions and related health equity promotion efforts