1,721,025 research outputs found
Connecting statistical brains
No full textTwenty-eight early-career researchers in statistics, with the support of seven international professors, were given 48 hours to propose methods for state-of-the-art data analysis in neuroscience. Antonio Canale, Daniele Durante, Lucia Paci and Bruno Scarpa report from the scene
Reinforcement learning in modern biostatistics: benefits, challenges and new proposals
Full text linkApplications of reinforcement learning (RL) for supporting, managing and improving decision-making are becoming increasingly popular in a variety of medicine and healthcare domains where the problem has a sequential nature. By continuously interacting with the underlying environment, RL techniques are able to learn by trial-and-error on how to take better actions in order to maximize an outcome of interest over time. However, if on one hand RL offers a new powerful framework, on the other hand it poses some unique challenges for data analysis and interpretability, which call for new statistical techniques in both predictive and descriptive learning.
Notably, several methodological challenges, for which the contribution of the biostatistical community may play a crucial role, limit the use of RL in real life. In an aim to bridge the statistics and RL communities, we start by assimilating the different existing RL terminologies, notations and approaches into a coherent body of work, and by translating them from a machine learning (ML) to a statistical perspective. Then, through a comprehensive methodological review, we report and discuss the state-of-the-art RL-based research in healthcare. Two main applied domains emerged: 1) adaptive interventions (AIs), encompassing both dynamic treatment regimes and just-in-time adaptive interventions in mobile health (mHealth); and 2) adaptive designs of clinical trials, specifically dose-finding designs and adaptive randomization. We illustrate existing RL-based methods in these areas, discussing their benefits and existing open problems that may impact their application in real life. A major barrier to adopting RL in real-world experiments is the lack of clarity on how statistical analyses and inference are impacted. In clinical trials for example, if on one side, to achieve the practical (and more ethical) goal of improving patients’ benefits, RL may have better abilities in terms of maximising clinical outcomes by adaptively randomizing participants to the best evidence-based treatment; on the other side, to achieve the scientific goal of e.g., discovering whether one treatment is more effective compared to a control treatment, less is known about their inferential properties. Through a simulation study, we investigate the challenges of conducting hypothesis testing from data collected through a class of RL, i.e., multi-armed bandits (MABs), outlining the harms MAB algorithms can cause to traditional statistical tests’ type-I error and power. This empirical evaluation provides guidance to two alternative ways of pursuing improved statistical hypothesis testing: 1) to explore ways of modifying the test statistic using knowledge of the adaptive data collection nature; 2) to modify the algorithm or framework for a more sensitive problem to both statistical inference as well as reward maximization. Focusing on the Thompson Sampling (a randomized MAB strategy), we show how a modified version of it results in an optimal intermediate between these two objectives. These findings can provide insights into how challenges can be surmounted by bridging machine learning, statistics, and applied sciences, to conduct adaptive experiments in the real-world, aiming to simultaneously help individuals and advance
scientific research. We finally combine our methodological knowledge with a motivating mHealth study for improving physical activity, to illustrate the tremendous collaboration opportunities between statistics and RL researchers in the space of developing adaptive interventions into the increasingly growing area of mHealth
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Novel Food A New Reality in Food Ingredient
No full textFrom the legislative standpoint, foods do not necessarily have a nutritional role, in that they are defined as: any product or substance that is transformed (even partially) or not transformed and that is meant to be ingested by humans (or for which ingestion can be reasonably assumed), excluding such products as pharmaceuticals and tobacco products, among others. In accordance with EC Regulation 258/97 concerning novel foods, the safety of foods must be established by a history of consumption. For novel foods, safety must be proven through risk assessment. However, the authorization procedures for novel foods is complex
Robust Methods for Detecting Spontaneous Activations in fMRI Data
No full textFunctional magnetic resonance imaging (fMRI) is a technique for mea- suring brain activity. The outcomes of fMRI measurements are complex data that can be interpreted as multivariate time series, recorded at different brain locations, usu- ally across subjects. The literature has been mainly concerned with task-based fMRI analysis, which focuses on the response to controlled exogenous stimuli. Neverthe- less, resting state fMRI (RfMRI) analysis, dealing with spontaneous brain activity, is considered the key to understand the neuronal organisation of the brain. The aim of this paper is to identify spontaneous neural activations and to estimate the brain response function in RfMRI data, called Hemodynamic Response Function (HRF). To this purpose, we apply an existing method based on a normality assumption for the data generating process and we consider a novel, more general method, based on robust filtering. Finally, we compare the neural activations and HRF estimates for two specific patients
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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