Quantile regression for mixed models with an application to examine blood pressure trends in China

Smith, Luke B.; Fuentes, Montserrat; Gordon‐Larsen, Penny; Reich, Brian J.

doi:10.1214/15-aoas841

Cited by 15 publications

(13 citation statements)

References 43 publications

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“…Quantile regression model allows the predictor variable to have a more complex relationship with the response variable [26]. Our developed tree height’s conditional probability density functions for a given diameter (Eqs B.1, B.4, B.7, B.10 and B.13) enables us to write the quantile equation of the tree height to any desired conditional quantile of the height’s distribution.…”

Section: Resultsmentioning

confidence: 99%

New Insights into Tree Height Distribution Based on Mixed Effects Univariate Diffusion Processes

Rupšys

2016

PLoS ONE

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The aim of this paper is twofold: to introduce the mathematics of stochastic differential equations (SDEs) for forest dynamics modeling and to describe how such a model can be applied to aid our understanding of tree height distribution corresponding to a given diameter using the large dataset provided by the Lithuanian National Forest Inventory (LNFI). Tree height-diameter dynamics was examined with Ornstein-Uhlenbeck family mixed effects SDEs. Dynamics of a tree height, volume and their coefficients of variation, quantile regression curves of the tree height, and height-diameter ratio were demonstrated using newly developed tree height distributions for a given diameter. The parameters were estimated by considering a discrete sample of the diameter and height and by using an approximated maximum likelihood procedure. All models were evaluated using a validation dataset. The dataset provided by the LNFI (2006–2010) of Scots pine trees is used in this study to estimate parameters and validate our modeling technique. The verification indicated that the newly developed models are able to accurately capture the behavior of tree height distribution corresponding to a given diameter. All of the results were implemented in a MAPLE symbolic algebra system.

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Section: Resultsmentioning

confidence: 99%

New Insights into Tree Height Distribution Based on Mixed Effects Univariate Diffusion Processes

Rupšys

2016

PLoS ONE

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“…Other methods or algorithms used to QR includes Barrodale-Roberts algorithm [82], Expectation-Maximization (EM) algorithm [83], Monte Carlo Expectation-Maximization (MCEM) algorithm [13,84,85], and Bayesian approach by Markov chain Monte Carlo (MCMC) procedure [86][87][88][89][90][91][92][93]. Longitudinal QR has been rapidly expanded in many areas, including investment and finance [94,95], economics [96], environmental science [97,98], geography [99], public health [100,101] and biomedical research [102][103][104][105]. In investment and finance areas, Bassett and Chen [94] utilized longitudinal QR to provide additional information from the time series data of portfolio returns based on the way style that affects returns at places other than the expected value of return.…”

Section: Qr Models For Longitudinal Datamentioning

confidence: 99%

“…It provided a full scan of information among time effects, education level, and years of experience in different wage quantile. In public health, Smith et al [100] revealed that the association between high blood pressure and living in an urban area has evolved from positive to negative, with the strongest changes occurring in the upper tail. In meteorology, Timofeev and Sterin [97] utilized longitudinal QR to analyze various changes in climate characteristics.…”

Section: Qr Models For Longitudinal Datamentioning

confidence: 99%

Quantile Regression Models and Their Applications: A Review

Huang¹,

Zhang²,

Chen³

et al. 2017

J Biom Biostat

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Quantile regression (QR) has received increasing attention in recent years and applied to wide areas such as investment, finance, economics, medicine and engineering. Compared with conventional mean regression, QR can characterize the entire conditional distribution of the outcome variable, may be more robust to outliers and misspecification of error distribution, and provides more comprehensive statistical modeling than traditional mean regression. QR models could not only be used to detect heterogeneous effects of covariates at different quantiles of the outcome, but also offer more robust and complete estimates compared to the mean regression, when the normality assumption violated or outliers and long tails exist. These advantages make QR attractive and are extended to apply for different types of data, including independent data, time-to-event data and longitudinal data. Consequently, we present a brief review of QR and its related models and methods for different types of data in various application areas.Citation: Huang Q, Zhang H, Chen J, He M (2017) Quantile Regression Models and Their Applications: A Review. J Biom Biostat 8: 354.

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“…This is particularly the case owing to their contribution to the incidence of cardiovascular disease (CVD). A number of studies have found significant and linear relationships between SBP and DBP levels, and CVD morbidity and mortality 4 , 5 . Analysis of longitudinal data spanning 20 years confirms that SBP and DBP have continuous, graded, strong, independent, etiologically significant relationships to blood pressure-related risks, primarily incidence and mortality from coronary heart disease, stroke, and all CVDs 6 .…”

Section: Introductionmentioning

confidence: 99%

Quantile regression analysis of modifiable and non-modifiable drivers’ of blood pressure among urban and rural women in Ghana

Amugsi

Dimbuene

Asiki

et al. 2018

Sci Rep

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High blood pressure is an increasingly problematic public health concern in many developing countries due to the associated cardiovascular and renal complications. This study set out to investigate the drivers of blood pressure among urban and rural women using the 2014 Ghana Demographic and Health Survey data. Diastolic blood pressure (DBP) and systolic blood pressure (SBP) were the outcomes of interest. Our findings showed that body mass index (BMI) had a significant positive effect on DBP and SBP in both urban and rural settings, with the largest effect occurring among women in the 75th quantile. Arm circumference also had a positive effect on DBP and SBP across all quantiles in both settings. Age had an increasing positive effect along the entire conditional DBP and SBP distribution in both settings. Women who were pregnant had lower DBP and SBP relative to those who were not pregnant in both settings. These results highlight the important drivers of DBP and SBP, and the differential effects of these drivers on blood pressure (BP) among women in urban and rural settings. To increase their effectiveness, interventions to address high BP should take into account these differential effects.

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Quantile regression for mixed models with an application to examine blood pressure trends in China

Cited by 15 publications

References 43 publications

New Insights into Tree Height Distribution Based on Mixed Effects Univariate Diffusion Processes

New Insights into Tree Height Distribution Based on Mixed Effects Univariate Diffusion Processes

Quantile Regression Models and Their Applications: A Review

Quantile regression analysis of modifiable and non-modifiable drivers’ of blood pressure among urban and rural women in Ghana

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