-squared for general regression models in the presence of sampling weights

2008 ◽  
Vol 78 (12) ◽  
pp. 1671-1672
Author(s):  
S. Freels ◽  
K. Sinha
2014 ◽  
Vol 112 (4) ◽  
pp. 243-252 ◽  
Author(s):  
Sabrina J. Lovell ◽  
David W. Carter

1998 ◽  
Vol 217 (1) ◽  
Author(s):  
Hans Schneeweiß

ZusammenfassungNach einer kurzen Einführung in die Theorie der erwartungstreuen Schätzgleichungen für allgemeine Regressionsmodelle und der korrigierten Schätzgleichungen für Regressionsmodelle mit fehlerbehafteten Kovariablen wird die Approximationsgüte eines auf Reihenentwicklung basierenden Ansatzes von Stefanski diskutiert.


2010 ◽  
Vol 121-122 ◽  
pp. 346-349
Author(s):  
Yu Qin Sun ◽  
Yuan Ttao Jiang ◽  
Yong Ge Tian

One century ago (1910), the Hungarian mathematician Alfred Haar introduced the simplest wavelets in approximation theory, which are now known as the Haar wavelets. This type of wavelets can effectively be used to fit data in statistical applications. It is well known that for a general regression model, it is not easy to write estimations of its parameters in analytical forms. However, regression models generated from the Haar wavelets are easy to compute. In this article, we introduce how to use the Haar wavelets to formulate regression models and to fit data. In addition, we mention some variations of the Haar wavelets and their possible applications.


Author(s):  
Lindsay S Mayberry ◽  
Robert A Greevy ◽  
Li-Ching Huang ◽  
Shilin Zhao ◽  
Cynthia A Berg

Abstract Background Family members’ responses to adults’ diabetes and efforts to manage it vary widely. Multiple aspects of diabetes-specific family functioning have been identified as important for self-management and psychosocial well-being in theoretical (i.e., theories of social support and collaborative coping) and observational literature. Purpose Develop a typological framework of diabetes-specific family functioning and examine cross-sectional associations between type and diabetes outcomes. Methods We used electronic health record (EHR) data to identify a cohort of 5,545 adults receiving outpatient care for type 2 diabetes and invited them to complete a survey assessing 10 dimensions of diabetes-specific family functioning. We used k-means cluster analysis to identify types. After type assignment, we used EHR data for the full cohort to generate sampling weights to correct for imbalance between participants and non-participants. We used weighted data to examine unadjusted associations between participant characteristics and type, and in regression models to examine associations between type and diabetes outcomes. Regression models were adjusted for sociodemographics, diabetes duration, and insulin status. Results We identified and named four types: Collaborative and Helpful (33.8%), Satisfied with Low Involvement (22.2%), Want More Involvement (29.6%), and Critically Involved (14.5%; reflecting the highest levels of criticism and harmful involvement). Across these types, hemoglobin A1c, diabetes distress, depressive symptoms, diabetes medication adherence, and diabetes self-efficacy worsened. After covariate adjustment, type remained independently associated with each diabetes outcome (all p’s < .05). Conclusions The typology extends theories of family support in diabetes and applications of the typology may lead to breakthroughs in intervention design, tailoring, and evaluation.


2021 ◽  
Vol 23 (1) ◽  
pp. 135-148
Author(s):  
Yunjeong Kang ◽  
Myung Hwan Na ◽  
Wanhyun Cho ◽  
Hyeon Seok Ko

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