Risk Factors for Birth Defects: A Conditional LogisticRegression Analysis of a Case-Control Study in Guang-dong Province of China

Information was obtained from the same questionnaire (23 risk factors listed) of cases and controls. We used a multivariate logistic model, which described variables significantly increased risk of birth defects. The risk factors included maternal educational levels, medicine taken during pregnancy and antenatal care. It was suggested to strengthen antenatal care was the main preventive measure against birth defects.

A dynamic logistic regression for network link prediction

In social network analysis, link prediction is a problem of fundamental importance. How to conduct a comprehensive and principled link prediction, by taking various network structure information into consideration,is of great interest. To this end, we propose here a dynamic logistic regression method. Specifically, we assume that one has observed a time series of network structure. Then the proposed model dynamically predicts future links by studying the network structure in the past. To estimate the model, we find that the standard maximum likelihood estimation(MLE) is computationally forbidden. To solve the problem, we introduce a novel conditional maximum likelihood estimation(CMLE) method, which is computationally feasible for large-scale networks. We demonstrate the performance of the proposed method by extensive numerical studies.

Incorporating within- and between-patch resource selection in identification of critical habitat for brood-rearing greater sage-grouse

Introduction:Incorporating information on animal behavior in resource-based predictive modeling(e.g.,occurrence mapping)can elucidate the relationship between process and spatial pattern and depict habitat in terms of its structure as well as its function.In this paper,we assigned location data on brood-rearing greater sage-grouse(Centrocercus urophasianus)to either within-patch(encamped)or between-patch(traveling)behavioral modes by estimating a movement-based relative displacement index.Objectives were to estimate and validate spatially explicit models of within-versus between-patch resource selection for application in habitat management and compare these models to a non-behaviorally adjusted model.Results:A single model,the vegetation and water resources model,was most plausible for both the encamped and traveling modes,including the non-behaviorally adjusted model.When encamped,sage-grouse selected for taller shrubs,avoided bare ground,and were closer to mesic areas.Traveling sage-grouse selected for greater litter cover and herbaceous vegetation.Preference for proximity to mesic areas was common to both encamped and traveling modes and to the non-behaviorally adjusted model.The non-behaviorally adjusted map was similar to the encamped model and validated well.However,we observed different selection patterns during traveling that could have been masked had behavioral state not been accounted for.Conclusions:Characterizing habitat that structured between-patch movement broadens our understanding of the habitat needs of brood-rearing sage-grouse,and the combined raster surface offers a reliable habitat management tool that is readily amenable to application by GIS users in efforts to focus sustainable landscape management.