In order to find previously unknown subgroups in biomedical data and generate testable hypotheses, visually guided exploratory analysis can be of tremendous importance. In this paper we propose a new dissimilarity measure that can be used within the Multidimensional Scaling framework to obtain a joint low-dimensional representation of both the samples and variables of a multivariate data set, thereby providing an alternative to conventional biplots. In comparison with biplots, the representations obtained by our approach are particularly useful for exploratory analysis of data sets where there are small groups of variables sharing unusually high or low values for a small group of samples.
Individual participant data from digital sources informed and improved precision in the evaluation of predictive biomarkers in Bayesian network meta-analysis
Beyond Missing Data: Questionnaire Response Uncertainty as an Early Digital Sign of Dementia-Related Vulnerability
Shanghai Institute for Mathematics and Interdisciplinary Department of Epidemiology and Biostatistics, School of Sciences（上海数学研究所与公共卫生学院流行病学与生物统计系）；Department of Statistics and Data Science, National University of Singapore（新加坡国立大学统计与数据科学系）
Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation
Data-driven controlled subgroup selection in clinical trials
Statistical Laboratory, University of Cambridge（剑桥大学统计实验室）；Novartis Pharma AG（诺华制药）；Medical University of Vienna, Center for Medical Data Science, Institute of Medical Statistics（维也纳医科大学医学数据科学中心医学统计研究所）；School of Mathematics and Maxwell Institute for Mathematical Sciences, University of Edinburgh（爱丁堡大学数学学院与麦克斯韦数学科学研究所）；School of Mathematical Sciences, University of Southampton（南安普顿大学数学科学学院）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；School of Mathematical Sciences, Lancaster University（兰卡斯特大学数学科学学院）
