Q-LEARNING WITH CENSORED DATA

We develop methodology for a multistage-decision problem with flexible number of stages in which the rewards are survival times that are subject to censoring. We present a novel Q-learning algorithm that is adjusted for censored data and allows a flexible number of stages. We provide finite sample bounds on the generalization error of the policy learned by the algorithm, and show that when the optimal Q-function belongs to the approximation space, the expected survival time for policies obtained by the algorithm converges to that of the optimal policy. We simulate a multistage clinical trial with flexible number of stages and apply the proposed censored-Q-learning algorithm to find individualized treatment regimens. The methodology presented in this paper has implications in the design of personalized medicine trials in cancer and in other life-threatening diseases.     

Inheritance,poverty, and disability

Inheritance is a significant means of transferring wealth from one generation to the next, and therefore increasingly attracts attention from researchers and policy-makers working on intergenerational and multidimensional poverty. However, until now disabled persons have been overlooked in these discussions. This oversight is particularly unfortunate because, as a group, the estimated one billion people with disabilities (some 15% of the world’s population) are among the poorest and most marginalized of the global population. Over the past decade, a small but growing literature has examined the recursive connections between poverty and disability throughout the developing world. In this paper, we argue that disabled individuals are routinely denied inheritance rights in many low-income and middle-income countries, and that this is a significant and largely unrecognized contributor to their indigence. The denial of inheritance is both a social justice issue and a practice that can no longer be overlooked if disabled persons are to be brought into the development mainstream.     

Digital Finance and FinTech: current research and future research directions

Since decades, the financial industry has experienced a continuous evolution in service delivery due to digitalization. This evolution is characterized by expanded connectivity and enhanced speed of information processing both at the customer interface and in back-office processes. Recently, there has been a shift in the focus of digitalization from improving the delivery of traditional tasks to introducing fundamentally new business opportunities and models for financial service companies. Digital Finance encompasses a magnitude of new financial products, financial businesses, finance-related software, and novel forms of customer communication and interaction—delivered by FinTech companies and innovative financial service providers. Against this backdrop, the research on finance and information systems has started to analyze these changes and the impact of digital progress on the financial sector. Therefore, this article reviews the current state of research in Digital Finance that deals with these novel and innovative business functions. Moreover, it gives an outlook on potential future research directions. As a conceptual basis for reviewing this field, the Digital Finance Cube, which embraces three key dimensions of Digital Finance and FinTech, i.e., the respective business functions, the technologies and technological concepts applied as well as the institutions concerned, is introduced. This conceptualization supports researchers and practitioners when orientating in the field of Digital Finance, allows for the arrangement of academic research relatively to each other, and enables for the revelation of the gaps in research.     

A Tiered Framework for Risk‐Relevant Characterization and Ranking of Chemical Exposures: Applications to the National Children's Study (NCS)

A challenge for large‐scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co‐occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure‐relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new “Tiered Exposure Ranking” (TiER) framework, developed to support various aspects of risk‐relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both “discovery‐driven” and “hypothesis‐driven” analyses. “Tier 1” applications focus on “exposomic” pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk‐relevant exposure metrics for populations and individuals. In this article, “tier 1” applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A “tier 2” application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk‐relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.     

The heritability of emergent leadership: Age and gender as moderating factors

In this study, we examined the moderating influences of gender and age with respect to testing the heritability of leadership emergence. A large data base of 12,112 twins from Sweden was used in the current study to decompose the variance of emergent leadership into an unobservable genetic component and environmental components that are either common or unshared among twin pairs. Consistent with prior leadership research on genetics, we found that a genetic factor is able to explain a significant proportion of the variation across individuals in predicting how twins perceive their emergent leadership behavior (about 44% for women and 37% for men). Furthermore, we also found that the magnitude of genetic influence on emergent leadership varied with age, but only for women with the heritability estimate being highest for the mid-age women versus lowest for the older women. Implications for advancing research on the genetic and environmental influences on leadership emergence are discussed.     

Leadership of highly creative people in highly creative fields: A historiometric study of scientific leaders

In recent years there has been a marked increase in the study of the influence of leadership on creativity, and the effects of this relationship on organizational performance. While a number of explanations have been broached with regard to the positive effects of leadership on creativity, many of these studies propose different, and often contradictory, methods for leaders to achieve these positive effects on creativity within their organizations and work groups. Additionally, little work has been done examining the effects of leadership on highly creative people in fields requiring creativity. The purpose of this study is to examine two existing leadership theories with regard to their viability as models to explain creative performance of eminent scientists. Eminent scientists represent a population of leaders of highly creative individuals in a field that values the production of innovative ideas and products as a marker of performance. Ninety-three excerpts from the biographies of scientists were content coded for leader behaviors and performance criteria. The results of this analysis indicate that a model based on strategic planning and product championing may serve to explain the positive effects of leadership on creativity in a highly creative population.     

Infants’ Lexical Processing Efficiency is Related to Vocabulary Size by One Year of Age

By 15–18 months, infants’ skill in interpreting familiar words, or lexical processing efficiency (LPE), improves substantially and is correlated with vocabulary size concurrently and several months later. Prior to this age LPE is quite poor, and to date there is little evidence that it is related to vocabulary size. If this relation only emerges once infants have relatively good LPE, and also know a substantial number of words, it could suggest that the processes that support the rapid growth in vocabulary commonly observed as infants approach age 2 may not yet be functional in the earlier stages of lexical development. However, using a modified LPE task we found that 12‐month‐olds with better LPE understood more words at that age and also produced more words several months later. These results suggest that meaningful individual differences in LPE are already emerging by 12 months and may support lexical development across the second year.     

Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms

Competing risks occur in a time-to-event analysis in which a patient can experience one of several types of events. Traditional methods for handling competing risks data presuppose one censoring process, which is assumed to be independent. In a controlled clinical trial, censoring can occur for several reasons: some independent, others dependent. We propose an estimator of the cumulative incidence function in the presence of both independent and dependent censoring mechanisms. We rely on semi-parametric theory to derive an augmented inverse probability of censoring weighted (AIPCW) estimator. We demonstrate the efficiency gained when using the AIPCW estimator compared to a non-augmented estimator via simulations. We then apply our method to evaluate the safety and efficacy of three anti-HIV regimens in a randomized trial conducted by the AIDS Clinical Trial Group, ACTG A5095.