Department of Biostatistics
Neurostatistics Working Group
2014 - 2015
ABSTRACT: When a true survival endpoint cannot be assessed for some subjects, an alternative endpoint that measures the true endpoint with error may be collected, which often occurs when obtaining the true endpoint is too invasive or costly. We develop nonparametric and semiparametric estimated likelihood functions that incorporate both uncertain endpoints available for all participants and true endpoints available for only a subset of participants. We propose maximum estimated likelihood estimators of the discrete survival function of time to the true endpoint and of a hazard ratio representing the effect of a binary or continuous covariate assuming a proportional hazards model. We show that the proposed estimators are consistent and asymptotically normal and develop the analytical forms of the variance estimators. Through extensive simulations, we also show that the proposed estimators have little bias compared to the naïve estimator, which uses only uncertain endpoints, and are more efficient with moderate missingness compared to the complete-case estimator, which uses only available true endpoints. We illustrate the proposed method by estimating the risk of developing Alzheimer's disease using data from the Alzheimer's Disease Neuroimaging Initiative.
*Joint work with Jarcy Zee.
ABSTRACT: Small vessel disease (SVD) is an important risk factor for cognitive impairment and dementia. The mechanisms linking SVD to cognitive impairment are not well understood. We hypothesized that multiple small, spatially distributed vascular lesions affect cognition through disruption of brain connectivity. We therefore examined local and global network alterations in patients with SVD and examined the relationship between network efficiency, markers of SVD burden on MRI and PET, and potential clinical consequences.
ABSTRACT: Our ability to recognize visual objects, such as faces, is realized by neurons in the inferotemporal cortex (IT). These cells show preferences for individual images and image categories (and are thus selective), and are able to maintain these preferences even if one introduces irrelevant contextual changes (they are tolerant to changes in retinal size, position or viewpoint). To perform these computations, posterior IT neurons (pIT) require feedforward anatomical projections from over a dozen cortical regions, predominantly from area V4 and anterior IT, but also from areas V3 and V2. We do not know why multiple projections to pIT are required. In this study, we are defining the contributions of areas V2, V3 and V4 towards selectivity and tolerance in pIT neurons. By reversibly inactivating these visual regions, we can observe selective changes in response selectivity of IT neurons. We can interpret these changes using multivariate statistical techniques, such as multidimensional scaling, affinity propagation and linear classifiers. Our preliminary findings suggest that these input clusters to IT are concerned with different but overlapping computations.
ABSTRACT: Censored covariates arise frequently in biomarker assessement in epidemiological studies and in family history studies of disease. While there is a large literature on regression models when the outcome variable is subject to censoring, there is a more limited literature on the treatment of censored covariates, especially for type II censoring. We develop threshold regression approaches for linear regression models with covariate subject to random censoring. Compared with existing methods, the proposed methods are simple but effective as they avoid complicated modeling in dealing with censored covariate values. We study the asymptotic properties of the resultant estimators. In addition to estimating the regression coefficient of the censored covariate, the threshold regression methods can also be used to test whether the effect of the censored covariate is significant. We discuss the choice of optimal threshold which yields the most powerful test. The finite sample performance of the proposed methods are assessed through simulation studies. We also apply the method to a motivation example.
ABSTRACT: This Harvard Catalyst Biostatistics symposium will explore statistical issues that arise in the study of neurologic diseases. The symposium will begin with motivating clinical background and identification of pressing analytical needs in amyotrophic lateral sclerosis, Alzheimer's disease, multiple sclerosis, and Parkinson's disease. The statistical talks will focus on methods for incorporating and handling causal inference, multiple endpoints, high dimensional biomarker selection, censored covariates, and measurement issues in short-term clinical trials. The symposium is intended for statisticians and neurological disease researchers who have analytical interests.
ABSTRACT: The field of functional neuroimaging is growing very rapidly resulting in a vast amount of data for analysis. Recently, several collections of resting state functional magnetic resonance images from different laboratories have been combined in freely available datasets for analysis including the 1000 Functional Connectomes Project Dataset, ADHD 200 among others. Statistical dimension reduction techniques such as singular value decomposition (SVD), independent component analysis (ICA), etc. are routinely used by practitioners in the field of neuroimaging to analyze complex fMRI data. In this talk, the main dimension reduction approaches for fMRI data are discussed stressing the major issues in the applications and the advantages of the methods depending on the biological question at hand. Extensions of the methods to high dimensional data are presented.
ABSTRACT: Although self-reported cognitive concerns (SCC) have previously been dismissed as a sign of the "worried well", there is emerging evidence to suggest that SCC may herald initial cognitive decrements at the stage of preclinical Alzheimer's disease (AD). Recent work from our own group and others suggests that specific SCC may in fact indicate early awareness prior to objective impairment on standardized tests and may be associated with evidence of early pathology on AD biomarkers and longitudinal decline.
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