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19 results

  • Functional data analysis and prediction tools for continuous glucose-monitoring studies

  • Emrah Gecili, Rui Huang, Jane C. Khoury, Eileen King, Mekibib Altaye, Katherine Bowers, Rhonda D. Szczesniak
  • Journal:
    Journal of Clinical and Translational Science / Volume 5 / Issue 1 / 2021
    Published online by Cambridge University Press:
    22 September 2020, e51
    • Article
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  • Introduction:

    To identify phenotypes of type 1 diabetes based on glucose curves from continuous glucose-monitoring (CGM) using functional data (FD) analysis to account for longitudinal glucose patterns. We present a reliable prediction model that can accurately predict glycemic levels based on past data collected from the CGM sensor and real-time risk of hypo-/hyperglycemic for individuals with type 1 diabetes.

    Methods:

    A longitudinal cohort study of 443 type 1 diabetes patients with CGM data from a completed trial. The FD analysis approach, sparse functional principal components (FPCs) analysis was used to identify phenotypes of type 1 diabetes glycemic variation. We employed a nonstationary stochastic linear mixed-effects model (LME) that accommodates between-patient and within-patient heterogeneity to predict glycemic levels and real-time risk of hypo-/hyperglycemic by creating specific target functions for these excursions.

    Results:

    The majority of the variation (73%) in glucose trajectories was explained by the first two FPCs. Higher order variation in the CGM profiles occurred during weeknights, although variation was higher on weekends. The model has low prediction errors and yields accurate predictions for both glucose levels and real-time risk of glycemic excursions.

    Conclusions:

    By identifying these distinct longitudinal patterns as phenotypes, interventions can be targeted to optimize type 1 diabetes management for subgroups at the highest risk for compromised long-term outcomes such as cardiac disease or stroke. Further, the estimated change/variability in an individual’s glucose trajectory can be used to establish clinically meaningful and patient-specific thresholds that, when coupled with probabilistic predictive inference, provide a useful medical-monitoring tool.

  • EDA for HLM: Visualization when Probabilistic Inference Fails

  • Jake Bowers, Katherine W. Drake
  • Journal:
    Political Analysis / Volume 13 / Issue 4 / Autumn 2005
    Published online by Cambridge University Press:
    04 January 2017, pp. 301-326

  • Nearly all hierarchical linear models presented to political science audiences are estimated using maximum likelihood under a repeated sampling interpretation of the results of hypothesis tests. Maximum likelihood estimators have excellent asymptotic properties but less than ideal small sample properties. Multilevel models common in political science have relatively large samples of units like individuals nested within relatively small samples of units like countries. Often these level-2 samples will be so small as to make inference about level-2 effects uninterpretable in the likelihood framework from which they were estimated. When analysts do not have enough data to make a compelling argument for repeated sampling based probabilistic inference, we show how visualization can be a useful way of allowing scientific progress to continue despite lack of fit between research design and asymptotic properties of maximum likelihood estimators.

    Somewhere along the line in the teaching of statistics in the social sciences, the importance of good judgment got lost amid the minutiae of null hypothesis testing. It is all right, indeed essential, to argue flexibly and in detail for a particular case when you use statistics. Data analysis should not be pointlessly formal. It should make an interesting claim; it should tell a story that an informed audience will care about, and it should do so by intelligent interpretation of appropriate evidence from empirical measurements or observations.

    —Abelson, 1995, p. 2

    With neither prior mathematical theory nor intensive prior investigation of the data, throwing half a dozen or more exogenous variables into a regression, probit, or novel maximum-likelihood estimator is pointless. No one knows how they are interrelated, and the high-dimensional parameter space will generate a shimmering pseudo-fit like a bright coat of paint on a boat's rotting hull.

    —Achen, 1999, p. 26

  • Unpacking Viazemskii's Khalat: The Technologies of Dilettantism in Early Nineteenth-Century Russian Literary Culture

  • Katherine Bowers
  • Journal:
    Slavic Review / Volume 74 / Issue 3 / Fall 2015
    Published online by Cambridge University Press:
    20 January 2017, pp. 529-552
    Print publication:
    Fall 2015

  • This article explores the image of the khalat, or dressing gown, in and around Petr Viazemskii's 1817 poem “Proshchanie s khalatom” (Farewell to My Dressing Gown). As the poem circulated during the period between its creation and printing, its central image—the khalat—became enshrined as a symbol for early nineteenth-century literary culture around and within the Arzamas circle, emphasizing a creative inner life and an informal approach to writing. The poem mediates between friendship, honor, authenticity, and authorship and the formalities, duties, and expectations of society life. The khalat image appears in later poems, correspondence, and occasional writings by Anton Del'vig, Aleksandr Pushkin, and Vasilii Zhukovskii, among others. Tracing the image through its intertextual influences, extratextual impact, and memetic evolution, I examine the way it contributed to the development of an intellectual network through information transfer during the early nineteenth century and beyond.

Russian Writers and the Fin de Siècle
  • The Twilight of Realism
  • Edited by Katherine Bowers, Ani Kokobobo
  • Published online:
    05 July 2015
    Print publication:
    17 June 2015
  • Russian literature has a reputation for gloomy texts, especially during the late nineteenth century. This volume argues that a 'fin-de-siècle' mood informed Russian literature long before the chronological end of the nineteenth century, in ways that had significant impact on the development of Russian realism. Some chapters consider ideas more readily associated with fin-de-siècle Europe such as degeneration theory, biodeterminism, Freudian psychoanalysis or apocalypticism, alongside earlier Russian realist texts by writers such as Turgenev, Dostoevsky or Tolstoy. Other chapters explore the changes that realism underwent as modernism emerged, examining later nineteenth-century or early twentieth-century texts in the context of the earlier realist tradition or their own cultural moment. Overall, a team of emerging and established scholars of Russian literature and culture present a wide range of creative and insightful readings that shed new light on later realism in all its manifestations.