Department of Biostatistics
Neurostatistics Working Group

2015 - 2016

Coordinators: Dr. Rebecca Betensky, and Dr. Catherine Lee

Schedule: Wednesdays, 12:30-1:30 p.m.
SPH2, Room 426 (unless otherwise notified)

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Seminar Description
This working group provides a forum for presentation and discussion of completed, ongoing, or planned statistical analyses of neurological data. Such data include, for example, in vivo human brain images (anatomic, functional and spectroscopic magnetic resonance imaging), gene expression studies of human and non-human animal brain tissue (brightfield and immunofluorescence microscopy, DNA microarrays, laser micro-dissection), in vivo micro-dialysis, clinical trials data for a variety of neurologic diseases, and genetic data from family studies. Non-statistical presentations of neurological, psychiatric and technological background material will also be included. Through this seminar, statisticians will gain exposure to the statistical issues that arise in the broad field of neurology and brain imaging psychiatry and to the diverse ongoing research in this area throughout Harvard and the world. A main goal of the seminar is to stimulate statistical interest in neuroscience and neurology and to develop strategies for collaboration within these fields.


September 16

Jessica Gronsbell
Doctoral Student, Department of Biostatistics, Harvard University

"NIH F31 Fellowship Application Process"
ABSTRACT: The Ruth L. Kirschstein NRSA for Individual Predoctoral Fellows (F31) Award supports promising doctoral candidates who will perform dissertation research and training for a PhD degree in a scientific health-related field relevant to the missions of the NIH during the tenure of the award. In this talk, I will provide an overview of the application process. First, second, and third year students are encouraged to attend.


Catherine Lee, Ph.D.
Doctoral Student, Department of Biostatistics, Harvard University

"Methods for Analyzing Left-truncated Time-to-event Data with Time-varying Biomarkers Measured Only at Study Entry, with Applications to Alzheimer's Disease"
ABSTRACT: Over the past decade, several biomarkers for Alzheimer's disease (AD) have been identified and well validated. Efforts are currently being made to understand how individual biomarker levels change with time and the order of biomarker changes over time relative to each other. Such time-dependent biomarker models would allow for more accurate disease staging and could be used to predict disease progression. However, analyses of such AD biomarker data present two challenges: time-to-event analyses necessitate the selection of a time origin and the Cox hazard regression requires observation of the covariate process at all failure times. The choice of time origin directly affects the treatment of time-varying predictors in the analyses.

We examine the choice of time origin and implication on analysis and provide methods for relating a sigmoidal time-varying predictor of AD to time-to-event in the setting of delayed entry when the time-varying predictor process is only measured at study entry. We provide an analytic derivation of the bias that occurs when an incorrect time origin is used in a Cox model with time-varying predictor or when a time-varying predictor measured at study entry is incorrectly treated as fixed. These analytical results are supported through a simulation study. Finally, our methods are applied to an Alzheimer's dataset. Note: I will be practicing a 15 minute talk that I will be giving at a Alzheimer's conference in mid-September.

September 30

Sy Han Steven Chiou, Ph.D.
Research Fellow, Department of Biostatistics, Harvard University

"Joint Scale-change Models for Recurrent Events and Failure Time"
ABSTRACT: Recurrent event data arise frequently in various fields such as biomedical sciences, public health, engineering, and social sciences. In many instances, the observation of the recurrent event process can be stopped by the occurrence of a correlated failure event and/or dependent censoring. In this talk, I will present a joint scale-change model for the recurrent event process and the failure time that allows the censoring time to be informative about the recurrent event process. In particular, a shared frailty variable is used to model the association between the two types of outcomes. In contrast to the popular Cox-type joint modeling approaches, the regression parameters in the proposed joint scale-change model have marginal interpretations. Moreover, the proposed approach is robust in the sense that no parametric assumption is imposed on the distribution of the unobserved frailty and the strong Poisson-type assumption is not needed for the recurrent event process. The consistency and asymptotic normality of the proposed semiparametric estimators are established under suitable regularity conditions. To estimate the corresponding variances of the estimators, a computationally efficient resampling-based procedure is proposed. Simulation studies and an analysis of hospitalization data from the Danish Psychiatric Central Register illustrate the performance of the proposed method.

October 7

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October 21

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October 28

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November 4

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December 2

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December 9

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December 16

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January 27

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February 3

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February 10

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February 17

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February 24

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March 2

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March 9

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March 23

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March 30

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April 6

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April 13

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April 20

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April 27

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May 4

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May 11

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