Overview of parametric survival analysis for health-economic applications.

Health economic models rely on data from trials to project the risk of events (e.g., death) over time beyond the span of the available data. Parametric survival analysis methods can be applied to identify an appropriate statistical model for the observed data, which can then be extrapolated to derive a complete time-to-event curve. This paper describes the properties of the most commonly used statistical distributions as a basis for these models and describes an objective process of identifying the most suitable parametric distribution in a given dataset. The approach can be applied with both individual-patient data as well as with survival probabilities derived from published Kaplan-Meier curves. Both are illustrated with analyses of overall survival from the Sorafenib Hepatocellular Carcinoma Assessment Randomised Protocol trial

Evolution of the Global Risk Network Mean-Field Stability Point

With a steadily growing human population and rapid advancements in technology, the global human network is increasing in size and connection density. This growth exacerbates networked global threats and can lead to unexpected consequences such as global epidemics mediated by air travel, threats in cyberspace, global governance, etc. A quantitative understanding of the mechanisms guiding this global network is necessary for proper operation and maintenance of the global infrastructure. Each year the World Economic Forum publishes an authoritative report on global risks, and applying this data to a CARP model, we answer critical questions such as how the network evolves over time. In the evolution, we compare not the current states of the global risk network at different time points, but its steady state at those points, which would be reached if the risk were left unabated. Looking at the steady states show more drastically the differences in the challenges to the global economy and stability the world community had faced at each point of the time. Finally, we investigate the influence between risks in the global network, using a method successful in distinguishing between correlation and causation. All results presented in the paper were obtained using detailed mathematical analysis with simulations to support our findings.Comment: 11 pages, 5 figures, the 6th International Conference on Complex Networks and Their Application

The Public’s Perception of Humanlike Robots: Online Social Commentary Reflects an Appearance-Based Uncanny Valley, a General Fear of a “Technology Takeover”, and the Unabashed Sexualization of Female-Gendered Robots

Towards understanding the public’s perception of humanlike robots, we examined commentary on 24 YouTube videos depicting social robots ranging in human similarity – from Honda’s Asimo to Hiroshi Ishiguro’s Geminoids. In particular, we investigated how people have responded to the emergence of highly humanlike robots (e.g., Bina48) in contrast to those with more prototypically-“robotic” appearances (e.g., Asimo), coding the frequency at which the uncanny valley versus fears of replacement and/or a “technology takeover” arise in online discourse based on the robot’s appearance. Here we found that, consistent with Masahiro Mori’s theory of the uncanny valley, people’s commentary reflected an aversion to highly humanlike robots. Correspondingly, the frequency of uncanny valley-related commentary was significantly higher in response to highly humanlike robots relative to those of more prototypical appearances. Independent of the robots’ human similarity, we further observed a moderate correlation to exist between people’s explicit fears of a “technology takeover” and their emotional responding towards robots. Finally, through the course of our investigation, we encountered a third and rather disturbing trend – namely, the unabashed sexualization of female-gendered robots. In exploring the frequency at which this sexualization manifests in the online commentary, we found it to exceed that of both the uncanny valley and fears of robot sentience/replacement combined. In sum, these findings help to shed light on the relevance of the uncanny valley “in the wild” and further, they help situate it with respect to other design challenges for HRI

Incorporating measurement error in n=1 psychological autoregressive modeling

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30-50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters.</p

Lagrangian bias in the local bias model

It is often assumed that the halo-patch fluctuation field can be written as a Taylor series in the initial Lagrangian dark matter density fluctuation field. We show that if this Lagrangian bias is local, and the initial conditions are Gaussian, then the two-point cross-correlation between halos and mass should be linearly proportional to the mass-mass auto-correlation function. This statement is exact and valid on all scales; there are no higher order contributions, e.g., from terms proportional to products or convolutions of two-point functions, which one might have thought would appear upon truncating the Taylor series of the halo bias function. In addition, the auto-correlation function of locally biased tracers can be written as a Taylor series in the auto-correlation function of the mass; there are no terms involving, e.g., derivatives or convolutions. Moreover, although the leading order coefficient, the linear bias factor of the auto-correlation function is just the square of that for the cross-correlation, it is the same as that obtained from expanding the mean number of halos as a function of the local density only in the large-scale limit. In principle, these relations allow simple tests of whether or not halo bias is indeed local in Lagrangian space. We discuss why things are more complicated in practice. We also discuss our results in light of recent work on the renormalizability of halo bias, demonstrating that it is better to renormalize than not. We use the Lognormal model to illustrate many of our findings.Comment: 14 pages, published on JCA

A review of explicit and implicit assumptions when providing personalized feedback based on self-report EMA data

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this field, EMA/ERA-data-driven summaries are presented to participants with the goal of promoting their insight in their experiences. Underlying this procedure are some fundamental assumptions about (i) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (ii) the translation of these experiences and evaluations to different types of data, (iii) the comparison of these different types of data, and (iv) the impact of a summary of moment-to-moment experiences on retrospective evaluations of those experiences. We argue that these assumptions deserve further exploration, in order to create a strong evidence-based foundation for the personalized feedback procedure

Inactivation of Aurora kinases and Cyclin-dependent kinases 4/6 allows cancers to adopt an endoreplication and form polyploid/polyaneuploid giant cancer cells (PGCCs/PACCs) that resist antimitotic drugs

View full abstracthttps://openworks.mdanderson.org/leading-edge/1044/thumbnail.jp

Microstructural changes in the trigeminal nerve of patients with episodic migraine assessed using magnetic resonance imaging

Background: There is histological evidence of microstructural changes in the zygomaticotemporal branch of the trigeminal nerve in migraineurs. This raises the possibility that altered trigeminal nerve properties contribute to migraine pathophysiology. Whilst it is not possible to explore the anatomy of small trigeminal nerve branches it is possible to explore the anatomy of the trigeminal root entry zone using magnetic resonance imaging in humans. The aim of this investigation is to assess the microstructure of the trigeminal nerve in vivo to determine if nerve alterations occur in individuals with episodic migraine. Methods: In 39 migraineurs and 39 matched controls, T1-weighted anatomical images were used to calculate the volume (mm3) and maximal cross-sectional area of the trigeminal nerve root entry zone; diffusion tensor images were used to calculate fractional anisotropy, mean diffusion, axial diffusion and radial diffusion. Results: There were significant differences between the left and right nerve of controls and migraineurs with respect to volume and not cross-sectional area. Migraineurs displayed reduced axial diffusion in the right nerve compared to the left nerve, and reduced fractional anisotropy in the left nerve compared to left controls. Furthermore, although there were no differences in mean diffusion or radial diffusion, regional analysis of the nerve revealed significantly greater radial diffusion in the middle and rostral portion of the left trigeminal nerve in migraineurs compared with controls. Conclusions: Migraine pathophysiology is associated with microstructural abnormalities within the trigeminal nerve that are consistent with histological evidence of altered myelin and/or organization. These peripheral nerve changes may provide further insight into migraine pathophysiology and enable a greater understanding for targeted treatments of pain alleviation

Cognitive Biases about Climate Variability in Smallholder Farming Systems in Zambia

Given the varying manifestations of climate change over time and the influence of climate perceptions on adaptation, it is important to understand whether farmer perceptions match patterns of environmental change from observational data. We use a combination of social and environmental data to understand farmer perceptions related to rainy season onset. Household surveys were conducted with 1171 farmers across Zambia at the end of the 2015/16 growing season eliciting their perceptions of historic changes in rainy season onset and their heuristics about when rain onset occurs. We compare farmers' perceptions with satellite-gauge-derived rainfall data from the Climate Hazards Group Infrared Precipitation with Station dataset and hyper-resolution soil moisture estimates from the HydroBlocks land surface model. We find evidence of a cognitive bias, where farmers perceive the rains to be arriving later, although the physical data do not wholly support this. We also find that farmers' heuristics about rainy season onset influence maize planting dates, a key determinant of maize yield and food security in sub-Saharan Africa. Our findings suggest that policy makers should focus more on current climate variability than future climate change.National Science Foundation [SES-1360463, BCS-1115009, BCS-1026776]6 month embargo; published online: 29 March 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]