3,126 research outputs found
Aerospace medicine and biology: A continuing bibliography with indexes (supplement 335)
This bibliography lists 143 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during March, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance
Nonparametric survival analysis of epidemic data
This paper develops nonparametric methods for the survival analysis of
epidemic data based on contact intervals. The contact interval from person i to
person j is the time between the onset of infectiousness in i and infectious
contact from i to j, where we define infectious contact as a contact sufficient
to infect a susceptible individual. We show that the Nelson-Aalen estimator
produces an unbiased estimate of the contact interval cumulative hazard
function when who-infects-whom is observed. When who-infects-whom is not
observed, we average the Nelson-Aalen estimates from all transmission networks
consistent with the observed data using an EM algorithm. This converges to a
nonparametric MLE of the contact interval cumulative hazard function that we
call the marginal Nelson-Aalen estimate. We study the behavior of these methods
in simulations and use them to analyze household surveillance data from the
2009 influenza A(H1N1) pandemic. In an appendix, we show that these methods
extend chain-binomial models to continuous time.Comment: 30 pages, 6 figure
Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.
Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making
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