651 research outputs found
Topics in social network analysis and network science
This chapter introduces statistical methods used in the analysis of social
networks and in the rapidly evolving parallel-field of network science.
Although several instances of social network analysis in health services
research have appeared recently, the majority involve only the most basic
methods and thus scratch the surface of what might be accomplished.
Cutting-edge methods using relevant examples and illustrations in health
services research are provided
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The analysis of social network data: an exciting frontier for statisticians
The catalyst for this paper is the recent interest in the relationship between social networks and an individual's health, which has arisen following a series of papers by Nicholas Christakis and James Fowler on person- to-person spread of health behaviors. In this issue, they provide a detailed explanation of their methods that offers insights, justifications, and responses to criticisms [1]. In this paper, we introduce some of the key statistical methods used in social network analysis and indicate where those used by Christakis and Fowler (CF) fit into the general framework. The intent is to provide the background necessary for readers to be able to make their own evaluation of the work by CF and understand the challenges of research involving social networks. We entertain possible solutions to some of the difficulties encountered in accounting for confounding effects in analyses of peer effects and provide comments on the contributions of CF
Circumstellar habitable zones for deep terrestrial biospheres
SM and JOJ are grateful to the UK Science and Technology Facilities Council (STFC) for Aurora Studentships. We thank Dr. Stephen Clifford (LPI), Dr. Ravi Kopparapu (Penn State), and Claire Davis (St. Andrews) for generous technical advice. We thank Norm Sleep and two anonymous reviewers for constructive reviews of the manuscriptPeer reviewedPostprin
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
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