Bayesian Lasso-mixed quantile regression

In this paper, we discuss the regularization in linear-mixed quantile regression. A hierarchical Bayesian model is used to shrink the fixed and random effects towards the common population values by introducing an l1 penalty in the mixed quantile regression check function. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of an age-related macular degeneration (ARMD) data, we assess the performance of the proposed method. The simulation studies and the ARMD data analysis indicate that the proposed method performs well in comparison with the other approaches. © 2012 Taylor & Francis

Dynamic and Assembly Characteristics of Deep-Cavity Basket Acting as a Host for Inclusion Complexation of Mitoxantrone in Biotic and Abiotic Systems

We describe the preparation, dynamic, assembly characteristics of vase-shaped basket 13− along with its ability to form an inclusion complex with anticancer drug mitoxantrone in abiotic and biotic systems. This novel cavitand has a deep nonpolar pocket consisting of three naphthalimide sides fused to a bicyclic platform at the bottom while carrying polar glycines at the top. The results of 1H Nuclear Magnetic Resonance (NMR), 1H NMR Chemical Exchange Saturation Transfer (CEST), Calorimetry, Hybrid Replica Exchange Molecular Dynamics (REMD), and Microcrystal Electron Diffraction (MicroED) measurements are in line with 1 forming dimer [12]6−, to be in equilibrium with monomers 1(R)3− (relaxed) and 1(S)3− (squeezed). Through simultaneous line-shape analysis of 1H NMR data, kinetic and thermodynamic parameters characterizing these equilibria were quantified. Basket 1(R)3− includes anticancer drug mitoxantrone (MTO2+) in its pocket to give stable binary complex [MTO⊂1]− (Kd=2.1 μM) that can be precipitated in vitro with UV light or pH as stimuli. Both in vitro and in vivo studies showed that the basket is nontoxic, while at a higher proportion with respect to MTO it reduced its cytotoxicity in vitro. With well-characterized internal dynamics and dimerization, the ability to include mitoxantrone, and biocompatibility, the stage is set to develop sequestering agents from deep-cavity baskets

MindKind: A mixed-methods protocol for the feasibility of global digital mental health studies in young people

While an estimated 14-20% of young adults experience mental health conditions worldwide, the best strategies for prevention and management are not fully understood. The ubiquity of smartphone use among young people makes them excellent candidates for collecting data about lived experiences and their relationships to mental health. However, not much is known about the factors affecting young peoples’ willingness to share information about their mental health. OBJECTIVE: We aim to understand the data governance and engagement strategies influencing young peoples’ (aged 16-24) participation in app-based studies of mental health. We hypothesize that willingness to participate in research is influenced by involvement  in how their data is collected, shared, and used. METHODS: Here, we describe the MindKind Study, which employs mixed methods to understand the feasibility of global, smartphone-based studies of youth mental health. A pilot 12-week app-based substudy will query participants’ willingness to engage with remote mental health studies. Participants will be randomized into one of four different data governance models designed to understand their preferences, as well as the acceptability of models that allow them more or less control over how their data are accessed and used. Enrolees will receive one of two different engagement strategies. A companion qualitative study will employ a deliberative democracy approach to examine the preferences, concerns and expectations of young people, with respect to remote mental health research. We also detail our engagement with young people as co-researchers in this study. This pilot study is being conducted in India, South Africa and the United Kingdom. CONCLUSION: This study is expected to generate new insights into the feasibility of, and best practices for, remote smartphone-based studies of mental health in youth and represents an important step toward understanding which approaches could help people better manage their mental health

Structured estimation for the nonparametric Cox model

In this paper, we study theoretical properties of the non-parametric Cox proportional hazards model in a high dimensional non-asymptotic setting. We establish the finite sample oracle l bounds for a general class of group penalties that allow possible hierarchical and overlapping structures. We approximate the log partial likelihood with a quadratic functional and use truncation arguments to reduce the error. Unlike the existing literature, we exemplify differences between bounded and possibly unbounded non-parametric covariate effects. In particular, we show that bounded effects can lead to prediction bounds similar to the simple linear models, whereas unbounded effects can lead to larger prediction bounds. In both situations we do not assume that the true parameter is necessarily sparse. Lastly, we present new theoretical results for hierarchical and smoothed estimation in the non-parametric Cox model. We provide two examples of the proposed general framework: a Cox model with interactions and an ANOVA type Cox model.

Confidence intervals for high-dimensional Cox models

We provide theoretical justification for post-selection inference in high-dimensional Cox models, based on the celebrated debiased Lasso procedure (e.g. Zhang and Zhang, 2014; van de Geer et al., 2014). Our generic model setup allows time-dependent covariates and an unbounded time interval, which is unique among post-selection inference studies on high-dimensional survival analysis. In addition, we adopt a novel proof technique to replace the use of Rebolledo’s central limit theorem as in the seminal work of Andersen and Gill (1982). Our theoretical results, which provide conditions under which our confidence intervals are asymptotically valid, are supported by extensive numerical experiments