This paper discusses certain statistical issues arising in an analysis to relate HLA-B antigens to the incidence of graft-versus-host disease in a clinical study. The Cox regression method is used to develop a global test of the hypothesis of no association and to produce estimated relative-risk factors corresponding to the presence of each particular allele. Multiple-testing considerations are central to any attempt to identify pairs of alleles having significantly different relative-risk factors. A number of simultaneous-testing approaches are considered including cumulative P-value plotting and a direct simulation of the null distribution of the maximum pairwise ratio.
While the vision of accelerating materials discovery using data
driven methods is well-founded, practical realization has been
throttled due to challenges in data generation, ingestion, and
materials state-aware machine learning. High-throughput experiments and automated computational workflows are addressing
the challenge of data generation, and capitalizing on these emerging data resources requires ingestion of data into an architecture
that captures the complex provenance of experiments and simulations. In this manuscript, we describe an event-sourced architecture for materials provenance (ESAMP) that encodes the sequence
and interrelationships among events occurring in a simulation or
experiment. We use this architecture to ingest a large and varied dataset (MEAD) that contains raw data and metadata from
millions of materials synthesis and characterization experiments
performed using various modalities such as serial, parallel, multimodal experimentation. Our data architecture tracks the evolution of a material’s state, enabling a demonstration of how stateequivalency rules can be used to generate datasets that significantly enhance data-driven materials discovery. Specifically, using state-equivalency rules and parameters associated with statechanging processes in addition to the typically used composition
data, we demonstrated marked reduction of uncertainty in prediction of overpotential for oxygen evolution reaction (OER) catalysts. Finally, we discuss the importance of ESAMP architecture in
enabling several aspects of accelerated materials discovery such
as dynamic workflow design, generation of knowledge graphs,
and efficient integration of theory and experiment.
Two populations, one Montreal- and one Utah-based, were studied with respect to heart disease risk factors on a cross-sectional basis. The Utah population afforded consistently lower mean blood pressures than the Montreal population, although there was not evidence that the Utah population was less obese, or had a lower pulse rate. Also, in the Utah population, it was found that the proportion of persons with a family history of heart disease did not differ significantly in the hyper- and normo-tensive groups. Fourteen parameters were investigated in the Montreal population, and the analyses indicated that, when other variables are controlled, age, pulse rate, some measure of serum lipid levels, and a family history of heart disease generally assist in the discrimination between the hyper- and normo-tensive groups, but the obesity measurement did not. In that sense, obesity, on its own, may not be considered a risk factor for hypertension.
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