Mixed Effects Quantile Regressions Research Articles

Quantitative measures of recent and lifetime agricultural pesticide use are associated with increased pesticide concentrations in house dust

ObjectiveElevated pesticide concentrations have been found in dust from homes with residents who use agricultural pesticides, but few studies have compared these concentrations to quantitative measures of their use. We evaluated household pesticide dust concentrations in relation to quantitative, active ingredient-specific metrics of agricultural pesticide use in the Biomarkers of Exposure and Effect in Agriculture Study. MethodsParticipants provided vacuum dust samples (2013–2018) and information regarding recent (last 12 months) and lifetime pesticide use. Thirty-two pesticide analytes were measured in 295 dust samples from 213 participants; 54 had repeated measurements (median = 96 days between visits). We used mixed-effects quantile regression models to estimate relative differences in pesticide concentrations for recent and lifetime agricultural use (number of days, intensity-weighted days), recent home/garden use (yes/no), and household characteristics. Only household characteristics were examined for dacthal because of no use information. We calculated intraclass correlation coefficients (ICCs) to evaluate temporal variability. We report only descriptive statistics for pesticides with detection rates <25 %. ResultsFor currently used pesticides, quantitative measures of recent agricultural use were associated with significantly increased household pesticide dust concentrations for malathion, metolachlor, acetochlor, cyfluthrin, and atrazine (p-trends < 0.001), but not permethrin. Similarly, quantitative measures of lifetime use were associated with increased concentrations of malathion, metolachlor, carbaryl, diazinon, and atrazine (p-trends < 0.001), but not permethrin, chlorpyrifos, or chlorothalonil. For banned pesticides, ever agricultural use was associated with elevated chlordane and heptachlor concentrations and non-significantly elevated dieldrin concentrations, but not lindane, p,p-DDD, p,p-DDE, or p,p-DDT. Recent home/garden use predicted increased malathion, carbaryl, and cyfluthrin concentrations. ICCs (range = 0.57–0.90) suggested moderate to high correlation over 3–6 months. Detection rates were <25 % for alachlor, butylate, EPTC, metribuzin, simazine, carbofuran, coumaphos, as well as for three banned pesticides (cyanazine, aldrin, endosulfan). ConclusionsHousehold pesticide dust concentrations were strongly associated with the frequency of agricultural pesticide use.

A mixed effects changepoint quantile regression model for longitudinal data with application on COVID-19 data

IntroductionLongitudinal individual response profiles could exhibit a mixture of two or more phases of increase or decrease in trend throughout the follow-up period, with one or more unknown transition points (changepoints). The detection and estimation of these changepoints is crucial. Most of the proposed statistical methods for detecting and estimating changepoints in literature rely on distributional assumptions that may not hold. In this case, a good alternative is to use a robust approach; the quantile regression model. There are methods in the literature to deal with quantile regression models with a changepoint. These methods ignore the within-subject dependence of longitudinal data.MethodsWe propose a mixed effects quantile regression model with changepoints to account for dependence structure in the longitudinal data. Fixed effects parameters, in addition to the location of the changepoint, are estimated using the profile estimation method. The stochastic approximation EM algorithm is proposed to estimate the fixed effects parameters exploiting the link between an asymmetric Laplace distribution and the quantile regression. In addition, the location of the changepoint is estimated using the usual optimization methods.Results and discussionA simulation study shows that the proposed estimation and inferential procedures perform reasonably well in finite samples. The practical use of the proposed model is illustrated using COVID-19 data. The data focus on the effect of global economic and health factors on the monthly death rate due to COVID-19 from 1 April 2020 to 30th April 2021. the results show a positive effect on the monthly number of patients with COVID-19 in intensive care units (ICUs) for both 0.5th and 0.8th quantiles of new monthly deaths per million. The stringency index, hospital beds, and diabetes prevalence have no significant effect on both 0.5th and 0.8th quantiles of new monthly deaths per million.

Relationships of Illness Perceptions with Depression and Anxiety in People Who Live with HIV/AIDS in a High-prevalence Ethnic Autonomous Region of Sichuan, China.

Poor mental health challenges outcomes and treatment of people living with HIV/AIDS (PLWHA) and may be related to illness perceptions. To investigate if depression and anxiety were associated with illness perceptions, we drew a random sample of 729 PLWHA from 13 administrative units in Liangshan Prefecture, Sichuan, China. Among the PLWHA surveyed, 222 and 175 had probable anxiety or depression. In mixed-effects logistic regression, negative illness perceptions were associated with increased odds of anxiety/depression. In linear mixed-effects quantile regression, the relationship of more negative illness perceptions with more severe and frequent symptoms of anxiety/depression grew stronger in the upper quantiles of the GAD-7 and PHQ-9 distributions, well beyond scale cut-offs for probable clinical relevance. We hypothesize that negative illness perceptions of HIV/AIDS and severity of depression and anxiety symptoms may mutually reinforce each other. Illness perceptions are a promising intervention target for improving the mental health of PLWHA.

The Effects of Short- and Long-term Exposures to Air Pollution on microRNA in Participants of the Normative Aging Study (NAS)

BACKGROUND AND AIM: Previous research have suggested the link between air pollution and epigenetic changes such as microRNA expression. In this study, we examined whether short- and long-term exposures to PM2.5, NO2, and O3 are associated with changes in the microRNA profile of patients in the Normative Aging Study (NAS). METHODS: The study population consisted of persons enrolled in NAS which has followed male veterans every three to five years since 1963. Our study period of interest spanned from 1999 to 2015. Blood samples collected from participants were used to sequence microRNA found in extracellular vesicles. We included 712 samples from 657 participants which passed quality checks after processing. We used fine-scaled predictions derived from ensemble models to assign exposure values based on each subject’s residential address. Mixed-effects quantile regression analyses were used to examine the exposure-outcome relationship with random intercepts for each person. We adjusted for covariates such as age, race, education level, smoking, drinking, BMI, diabetes, temperature and batch effects. We then linked microRNA which significantly changed in response to air pollution with their biological pathways. We further looked at the association between the detected microRNA sequences and cardiovascular health outcomes. RESULTS:Each 1-µg/m3 increase in 12-week and 6-month moving averages of PM2.5 were associated with a decrease of 3.15 (95% CI: 5.93, 0.37) and 5.80 (95% CI: 9.57, 2.03) in the median number of unique microRNA reads in the patient population, respectively. In single-pollutant models, an increase in intermediate and long-term moving averages of PM2.5 were associated with significant changes in several microRNA species linked to pancreatic cancer and chronic myeloid leukemia (CML). CONCLUSIONS:Air pollution exposures were associated with significant changes in several microRNA species. KEYWORDS: Air Pollution, Epigenomics, Cardiovascular Diseases, Environmental Epidemiology

A bias-adjusted estimator in quantile regression for clustered data

Quantile regression models with random effects are useful for studying associations between covariates and quantiles of the response distribution for clustered data. Parameter estimation is examined for a class of mixed-effects quantile regression models, with focus on settings with many but small clusters. The main contributions are the following: (i) documenting that existing methods may lead to severely biased estimators for fixed effects parameters; (ii) proposing a new two-step estimation methodology where predictions of the random effects are first computed by a pseudo likelihood approach (the LQMM method) and then used as offsets in standard quantile regression; (iii) proposing a novel bootstrap sampling procedure in order to reduce bias of the two-step estimator and compute confidence intervals. The proposed estimation and associated inference is assessed numerically through rigorous simulation studies and applied to an AIDS Clinical Trial Group (ACTG) study.

Bayesian joint modeling for partially linear mixed-effects quantile regression of longitudinal and time-to-event data with limit of detection, covariate measurement errors and skewness

ABSTRACT Joint modeling analysis of longitudinal and time-to-event data has been an active area of statistical methodological study and biomedical research, but the majority of them are based on mean-regression. Quantile regression (QR) can characterize the entire conditional distribution of the outcome variable, and may be more robust to outliers/heavy tails and misspecification of error distribution. Additionally, a parametric specification may be insufficient and inflexible to capture the complicated longitudinal pattern of biomarkers. Thus, this study proposes novel QR-based partially linear mixed-effects joint models with three components (QR-based longitudinal response, longitudinal covariate, and time-to-event processes), and applies to Multicenter AIDS Cohort Study (MACS). Many common data features, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution, are considered to obtain reliable parameter estimates. Many interesting findings are discovered by the complicated joint models under Bayesian inference framework. Simulation studies are also implemented to assess the performance of the proposed joint models under different scenarios.

Real-time indoor PM2.5 monitoring in an urban cohort: Implications for exposure disparities and source control

Fine particulate matter (PM2.5) concentrations are highly variable indoors, with evidence for exposure disparities. Real-time monitoring coupled with novel statistical approaches can better characterize drivers of elevated PM2.5 indoors. We collected real-time PM2.5 data in 71 homes in an urban community of Greater Boston, Massachusetts using Alphasense OPC-N2 monitors. We estimated indoor PM2.5 concentrations of non-ambient origin using mass balance principles, and investigated their associations with indoor source activities at the 0.50 to 0.95 exposure quantiles using mixed effects quantile regressions, overall and by homeownership. On average, the majority of indoor PM2.5 concentrations were of non-ambient origin (≥77%), with a higher proportion at increasing quantiles of the exposure distribution. Major source predictors of non-ambient PM2.5 concentrations at the upper quantile (0.95) were cooking (1.4–23 μg/m3) and smoking (15 μg/m3, only among renters), with concentrations also increasing with range hood use (3.6 μg/m3) and during the heating season (5.6 μg/m3). Across quantiles, renters in multifamily housing experienced a higher proportion of PM2.5 concentrations from non-ambient sources than homeowners in single- and multifamily housing. Renters also more frequently reported cooking, smoking, spray air freshener use, and second-hand smoke exposure, and lived in units with higher air exchange rate and building density. Accounting for these factors explained observed PM2.5 exposure disparities by homeownership, particularly in the upper exposure quantiles. Our results suggest that renters in multifamily housing may experience higher PM2.5 exposures due to a combination of behavioral and building factors that are amenable to intervention.

Prediction of small area quantiles for the conservation effects assessment project using a mixed effects quantile regression model

Quantiles of the distributions of several measures of erosion are important parameters in the Conservation Effects Assessment Project, a survey intended to quantify soil and nutrient loss on crop fields. Because sample sizes for domains of interest are too small to support reliable direct estimators, model based methods are needed. Quantile regression is appealing for CEAP because finding a single family of parametric models that adequately describes the distributions of all variables is difficult and small area quantiles are parameters of interest. We construct empirical Bayes predictors and bootstrap mean squared error estimators based on the linearly interpolated generalized Pareto distribution (LIGPD). We apply the procedures to predict county-level quantiles for four types of erosion in Wisconsin and validate the procedures through simulation.

Small area prediction of quantiles for zero-inflated data and an informative sample design

ABSTRACTThe Conservation Effects Assessment Project (CEAP) is a survey intended to quantify soil and nutrient loss on cropland. Estimates of the quantiles of CEAP response variables are published. Previous work develops a procedure for predicting small area quantiles based on a mixed effects quantile regression model. The conditional density function of the response given covariates and area random effects is approximated with the linearly interpolated generalised Pareto distribution (LIGPD). Empirical Bayes is used for prediction and a parametric bootstrap procedure is developed for mean squared error estimation. In this work, we develop two extensions of the LIGPD-based small area quantile prediction procedure. One extension allows for zero-inflated data. The second extension accounts for an informative sample design. We apply the procedures to predict quantiles of the distribution of percolation (a CEAP response variable) in Kansas counties.

A Robust Statistical Approach to Analyse Population Pharmacokinetic Data in Critically Ill Patients Receiving Renal Replacement Therapy.

Current approaches to antibiotic dose determination in critically ill patients requiring renal replacement therapy are primarily based on the assessment of highly heterogeneous data from small number of patients. The standard modelling approaches limit the scope of constructing robust confidence boundaries of the distribution of pharmacokinetics (PK) parameters, especially when the evaluation of possible association of demographic and clinical factors at different levels of the distribution of drug clearance is of interest. Commonly used compartmental models generally construct the inferences through a linear or non-linear mean regression, which is inadequate when the distribution is skewed, multi-modal or effected by atypical observation. In this study, we discuss the statistical challenges in robust estimation of the confidence boundaries of the PK parameters in the presence of highly heterogenous patient characteristics. A novel stepwise approach to evaluate the confidence boundaries of PK parameters is proposed by combining PK modelling with mixed-effects quantile regression (MEQR) methods. This method allows the assessment demographic and clinical factors' effects at any arbitrary quantiles of the outcome of interest, without restricting assumptions on the distributions. The MEQR approach allows us to investigate if the levels of association of the covariates are different at low, medium or high concentration. This methodological assessment is deemed as a background initial approach to support the development of a class of statistical algorithm in constructing robust confidence intervals of PK parameters which can be used for developing an optimised antibiotic dosing guideline for critically ill patients requiring renal replacement therapy.

Joint modeling for mixed-effects quantile regression of longitudinal data with detection limits and covariates measured with error, with application to AIDS studies

It is very common in AIDS studies that response variable (e.g., HIV viral load) may be subject to censoring due to detection limits while covariates (e.g., CD4 cell count) may be measured with error. Failure to take censoring in response variable and measurement errors in covariates into account may introduce substantial bias in estimation and thus lead to unreliable inference. Moreover, with non-normal and/or heteroskedastic data, traditional mean regression models are not robust to tail reactions. In this case, one may find it attractive to estimate extreme causal relationship of covariates to a dependent variable, which can be suitably studied in quantile regression framework. In this paper, we consider joint inference of mixed-effects quantile regression model with right-censored responses and errors in covariates. The inverse censoring probability weighted method and the orthogonal regression method are combined to reduce the biases of estimation caused by censored data and measurement errors. Under some regularity conditions, the consistence and asymptotic normality of estimators are derived. Finally, some simulation studies are implemented and a HIV/AIDS clinical data set is analyzed to to illustrate the proposed procedure.

Bayesian Approach to Nonlinear Mixed-Effects Quantile Regression Models for Longitudinal Data with Non-normality and Left-censoring

Bayesian Approach to Nonlinear Mixed-Effects Quantile Regression Models for Longitudinal Data with Non-normality and Left-censoring

A gradient search maximization algorithm for the asymmetric Laplace likelihood

The asymmetric Laplace likelihood naturally arises in the estimation of conditional quantiles of a response variable given covariates. The estimation of its parameters entails unconstrained maximization of a concave and non-differentiable function over the real space. In this note, we describe a maximization algorithm based on the gradient of the log-likelihood that generates a finite sequence of parameter values along which the likelihood increases. The algorithm can be applied to the estimation of mixed-effects quantile regression, Laplace regression with censored data, and other models based on Laplace likelihood. In a simulation study and in a number of real-data applications, the proposed algorithm has shown notable computational speed.