scholarly journals Semiparametric regression models for repeated measures of mortal cohorts with non-monotone missing outcomes and time-dependent covariates

2010 ◽  
Vol 29 (22) ◽  
pp. 2282-2296 ◽  
Author(s):  
Michelle Shardell ◽  
Gregory E. Hicks ◽  
Ram R. Miller ◽  
Jay Magaziner
2020 ◽  
Vol 33 (5) ◽  
pp. e100263
Author(s):  
Elsa Vazquez Arreola ◽  
Jeffrey R Wilson ◽  
Ding-Geng Chen

In studies on psychiatry and neurodegenerative diseases, it is common to have data that are correlated due to the hierarchical structure in data collection or to repeated measures on the subject longitudinally. However, the feedback effect created due to time-dependent covariates in these studies is often overlooked and seldom modelled. This article reviews the methodological development of feedback effects with marginal models for longitudinal data and discusses their implementation.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
I-Chen Chen ◽  
Philip M. Westgate

AbstractWhen observations are correlated, modeling the within-subject correlation structure using quantile regression for longitudinal data can be difficult unless a working independence structure is utilized. Although this approach ensures consistent estimators of the regression coefficients, it may result in less efficient regression parameter estimation when data are highly correlated. Therefore, several marginal quantile regression methods have been proposed to improve parameter estimation. In a longitudinal study some of the covariates may change their values over time, and the topic of time-dependent covariate has not been explored in the marginal quantile literature. As a result, we propose an approach for marginal quantile regression in the presence of time-dependent covariates, which includes a strategy to select a working type of time-dependency. In this manuscript, we demonstrate that our proposed method has the potential to improve power relative to the independence estimating equations approach due to the reduction of mean squared error.


2019 ◽  
Vol 11 (12) ◽  
pp. 3265 ◽  
Author(s):  
Anca Mehedintu ◽  
Georgeta Soava ◽  
Mihaela Sterpu

In this paper we study the evolution of remittances and risk of poverty threshold for nine emerging countries in the European Union and analyzed the evolution and trend of the share of remittances in the risk of poverty threshold. The analysis was performed on data taken from the Eurostat database for the period 2005–2017. The statistical analysis of the data showed that the evolution of both remittances and risk of poverty threshold was heavily influenced by the global economic crisis. Although after the crisis, the risk of poverty threshold has seen a growing trend in all emerging countries, the remittances have experienced sinuous variations, dramatic declines for some of the countries (drastically for Romania and Latvia) and significant increases for others (Hungary). The results of the analysis using time-dependent regression models lead to the conclusion that, although the share of remittances in risk of poverty threshold diminished abruptly after the 2009 economic crisis, in the short term it is expected to maintain a growth trend for most of the analyzed countries (Bulgaria, Czechia, Hungary, Lithuania, Poland, Romania, and Slovakia), followed downward tendency after 2018 for Bulgaria and Romania, and after 2020 for Hungary and Lithuania. For Latvia and Estonia, both quadratic and cubic models estimate a decreasing evolution.


2012 ◽  
Vol 31 (10) ◽  
pp. 931-948 ◽  
Author(s):  
Matthew W. Guerra ◽  
Justine Shults ◽  
Jay Amsterdam ◽  
Thomas Ten-Have

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