Design and Analysis for Precision Medicine Subgroup Identification

In 2015 President Barack Obama announced the launch of the Precision Medicine Initiative, spurring an out pour of interest into research regarding patient-specific health. Precision medicine is the reproducible research from which health care professionals can provide targeted treatments to their patients. Two objectives in precision medicine include (i) identifying treatment-response subgroups and (ii) identifying disease subgroups. In this manuscript, we will consider a place for traditional study designs in the new age of precision medicine by presenting the machine learning tools and statistical theory necessary to do so. We begin with a newly proposed method for estimating the individualized treatment regime from crossover studies. This method expands generalized outcome weighted learning into the 2x2 crossover study framework by considering the difference in treatment response as the observed reward and correcting for carryover effects, estimated through regression methods. After, we propose a new technique for identifying disease subgroups by applying hierarchical clustering techniques to what can be interpreted as a set of denoised outcomes. These values are weighted averages of the observed and fitted outcomes, estimated by regressing on a set of features. Finally, we return to identifying treatment-response subgroups, but, in the realm of case-control studies. We again expand on generalized outcome weighted learning in addition to accounting for the difference in the covariate distribution between the selected study sample and the total population. Between this method and electronic health data, advancements for rare and expensive to study diseases may be closer than we think.Doctor of Philosoph

An overview of clustering methods with guidelines for application in mental health research

Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and librarie

Identifying Clinical Phenotypes of Type 1 Diabetes for the Co-Optimization of Weight and Glycemic Control

Obesity is an increasing concern in the clinical care of youth with type 1 diabetes (T1D). Standard approaches to co-optimize weight and glycemic control are challenged by profound population-level heterogeneity. Therefore, the goal of the dissertation was to apply novel analytic methods to understand heterogeneity in the co-occurrence of weight, glycemia, and underlying patterns of minute-to-minute dysglycemia among youth with T1D. Data from the SEARCH for Diabetes in Youth study were used to characterize subgroups of youth with T1D showing similar weight status and level of glycemic control as distinct ‘weight-glycemia phenotypes’ of T1D. Cross-sectional weight-glycemia phenotypes were identified at the 5+ year follow-up visit (n=1,817) using hierarchical clustering on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c), generated by reinforcement learning tree predictions. Longitudinal weight-glycemia phenotypes spanning eight years were identified with longitudinal k-means clustering using baseline and follow-up BMIz and HbA1c measures (n=570). Logistic regression modeling tested for differences in the emergence of early/subclinical diabetes complications across subgroups. Seven-day blinded continuous glucose monitoring (CGM) data from baseline of the Flexible Lifestyles Empowering Change randomized trial (n=234, 13-16 years, HbA1c 8-13%) was clustered with a neural network approach to identify subgroups of adolescents with T1D and elevated HbA1c sharing patterns in their CGM data as ‘dysglycemia phenotypes.’ We identified six cross-sectional weight-glycemia phenotypes, including four normal-weight, one overweight, and one subgroup with obesity. Subgroups showed striking differences in other sociodemographic and clinical characteristics suggesting underlying health inequity. We identified four longitudinal weight-glycemia phenotypes associated with different patterns of early/subclinical complications, providing evidence that exposure to co-occurring obesity and worsening glycemic control may accelerate the development and increase the burden of co-morbid complications. We identified three dysglycemia phenotypes with significantly different patterns in hypoglycemia, hyperglycemia, glycemic variability, and 18-month changes in HbA1c. Patient-level drivers of the dysglycemia phenotypes appear to be different from risk factors for poor glycemic control as measured by HbA1c. These studies provide pragmatic, clinically-relevant examples of how novel statistics may be applied to data from T1D to derive patient subgroups for tailored interventions to improve weight alongside glycemic control.Doctor of Philosoph

Ninth annual V.M. Goldschmidt Conference : August 22-27, 1999, Harvard University, Cambridge, Massachusetts

The meeting is a forum for presenting and discussing new chemical and isotopic measurements, experimental and theoretical results, and discoveries in geochemistry and cosmochemistry.sponsored by Geochemical Society ... [and others]hosted by Department of Earth and Planetary Sciences, Harvard University ; compiled by Lunar and Planetary Institute.PARTIAL CONTENTS: Bacteria-promoted Dissolution of a Common Soil Silicate / S.L. Brantley, L.I. Liermann, and B.E. Kalinowski--Evolution of Temperature Control on Alkenone Biosynthesis / S.C. Brassell--Chemical Composition of Silurian Seawater: Preliminary Results from Environmental Scanning Electron Microscopy-Energy Dispersive Spectroscopy Analyses of Fluid Inclusions in Marine Halites / S.T. Brennan, T. Lowenstein, M.N. Timofeeff, and L.A. Hardi