2,979 research outputs found
Adjusting for bias introduced by instrumental variable estimation in the Cox Proportional Hazards Model
Instrumental variable (IV) methods are widely used for estimating average
treatment effects in the presence of unmeasured confounders. However, the
capability of existing IV procedures, and most notably the two-stage residual
inclusion (2SRI) procedure recommended for use in nonlinear contexts, to
account for unmeasured confounders in the Cox proportional hazard model is
unclear. We show that instrumenting an endogenous treatment induces an
unmeasured covariate, referred to as an individual frailty in survival analysis
parlance, which if not accounted for leads to bias. We propose a new procedure
that augments 2SRI with an individual frailty and prove that it is consistent
under certain conditions. The finite sample-size behavior is studied across a
broad set of conditions via Monte Carlo simulations. Finally, the proposed
methodology is used to estimate the average effect of carotid endarterectomy
versus carotid artery stenting on the mortality of patients suffering from
carotid artery disease. Results suggest that the 2SRI-frailty estimator
generally reduces the bias of both point and interval estimators compared to
traditional 2SRI.Comment: 27 pages, 8 figures, 4 table
Motif Discovery in Tissue-Specific Regulatory Sequences Using Directed Information
Motif discovery for the identification of functional regulatory elements underlying gene expression is a challenging problem. Sequence inspection often leads to discovery of novel motifs (including transcription factor sites) with previously uncharacterized function in gene expression. Coupled with the complexity underlying tissue-specific gene expression, there are several motifs that are putatively responsible for expression in a certain cell type. This has important implications in understanding fundamental biological processes such as development and disease progression. In this work, we present an approach to the identification of motifs (not necessarily transcription factor sites) and examine its application to some questions in current bioinformatics research. These motifs are seen to discriminate tissue-specific gene promoter or regulatory regions from those that are not tissue-specific. There are two main contributions of this work. Firstly, we propose the use of directed information for such classification constrained motif discovery, and then use the selected features with a support vector machine (SVM) classifier to find the tissue specificity of any sequence of interest. Such analysis yields several novel interesting motifs that merit further experimental characterization. Furthermore, this approach leads to a principled framework for the prospective examination of any chosen motif to be discriminatory motif for a group of coexpressed/coregulated genes, thereby integrating sequence and expression perspectives. We hypothesize that the discovery of these motifs would enable the large-scale investigation for the tissue-specific regulatory role of any conserved sequence element identified from genome-wide studies
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School Choice, School Quality, and Postsecondary Attainment
We study the impact of a public school choice lottery in Charlotte-
Mecklenburg schools on college enrollment and degree completion.
We find a significant overall increase in college attainment among
lottery winners who attend their first-choice school. Using rich
administrative data on peers, teachers, course offerings, and other
inputs, we show that the impacts of choice are strongly predicted by
gains on several measures of school quality. Gains in attainment are
concentrated among girls. Girls respond to attending a better school
with higher grades and increases in college-preparatory course
taking, while boys do not
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