Nonparametric regression analysis of multivariate longitudinal data

Xiang, Dongdong; Qiu, Peihua; Pu, Xiaolong

doi:10.5705/ss.2011.317

Cited by 29 publications

(34 citation statements)

References 16 publications

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“…In the first step, a flexible multivariate nonparametric model is used to estimate the regular multivariate longitudinal pattern from an IC dataset. To this end, the recent model fitting procedure proposed by Xiang, Qiu, and Pu is used, and it is briefly described in Section 2.1. Based on the fitted multivariate nonparametric model, we develop a new MDySS procedure for online monitoring of the multivariate longitudinal pattern of a new subject, which is described in detail in Section 2.2.…”

Section: Methodsmentioning

confidence: 99%

“…We use the following multivariate nonparametric longitudinal model to describe such data:

y_{i} false(t_{i j} false) = bold-italicμ false(t_{i j} false) + ϵ_{i} false(t_{i j} false), i = 1, ⋯, m, j = 1, ⋯, n_{i},

where μ ( t i j )=( μ 1 ( t i j ), μ 2 ( t i j ),…, μ q ( t i j )) T is the population mean vector of y i ( t i j ), and ϵ i ( t i j )=( ϵ i 1 ( t i j ), ϵ i 2 ( t i j ),…, ϵ i q ( t i j )) T is the q ‐dimensional error term with the covariance matrix function Σ( s , t )=Cov( ϵ i ( s ), ϵ i ( t )) for any s , t ∈[0, T ]. By the estimation procedure in Xiang, Qiu, and Pu, we can obtain estimates of μ ( t ) and Σ( s , t ) by the algorithm below. Step 1.…”

Section: Methodsmentioning

confidence: 99%

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Construction of an efficient multivariate dynamic screening system

Qiu

2017

Quality & Reliability Eng

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Recently, multivariate dynamic screening system (MDySS) has been proposed in the literature for monitoring processes whose in‐control distributions change over time. Multivariate dynamic screening system has broad applications, ranging from monitoring of dynamic engineering systems, such as nuclear reactors, airplanes, and other durable goods, to early disease detection. Conventional construction of MDySS is appropriate only in cases when sampling rate of observations is the same in Phase I and Phase II process monitoring, and the process observations are independent. In practice, these assumptions are rarely valid. In this paper, we propose a new construction of the MDySS method, which is shown to be more reliable than the conventional construction in various cases. The new construction is demonstrated using a real data from the SHARe Framingham Heart Study of the National Heart, Lung and Blood Institute.

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Section: Methodsmentioning

confidence: 99%

“…We use the following multivariate nonparametric longitudinal model to describe such data:

y_{i} false(t_{i j} false) = bold-italicμ false(t_{i j} false) + ϵ_{i} false(t_{i j} false), i = 1, ⋯, m, j = 1, ⋯, n_{i},

Section: Methodsmentioning

confidence: 99%

Construction of an efficient multivariate dynamic screening system

Qiu

2017

Quality & Reliability Eng

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“…In practice, the quantity V i is usually unknown and needs to be estimated. To this end, Xiang et al suggested the following estimation method. First, the local linear kernel smoothing procedure is used to provide an initial estimator of μ ( t ), denoted as

bold-italic \tilde{μ} (t) = ({\overset{μ}{true}}_{1} (t), \dots, {\overset{μ}{true}}_{q} (t))

.…”

Section: Proposed Multivariate Dynamic Screening System Methodsmentioning

confidence: 99%

Surveillance of cardiovascular diseases using a multivariate dynamic screening system

Qiu

Xiang

2015

Statistics in Medicine

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In the SHARe Framingham Heart Study of the National Heart, Lung and Blood Institute, one major task is to monitor several health variables (e.g., blood pressure and cholesterol level) so that their irregular longitudinal pattern can be detected as soon as possible and some medical treatments applied in a timely manner to avoid some deadly cardiovascular diseases (e.g., stroke). To handle this kind of applications effectively, we propose a new statistical methodology called multivariate dynamic screening system (MDySS) in this paper. The MDySS method combines the major strengths of the multivariate longitudinal data analysis and the multivariate statistical process control, and it makes decisions about the longitudinal pattern of a subject by comparing it with other subjects cross sectionally and by sequentially monitoring it as well. Numerical studies show that MDySS works well in practice.

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“…the data may have a skewed distribution at some timepoints but not others). Several approaches to nonparametric longitudinal data are discussed in the literature [43], and statistical software packages are adapting to provide nonparametric analysis options [44]. …”

Section: Overview Of Analytic Optionsmentioning

confidence: 99%

Analytic strategies to evaluate the association of time-varying exposures to HIV-related outcomes: Alcohol consumption as an example

Cook

Kelso²,

Brumback³

et al. 2016

CHR

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Background As persons with HIV are living longer, there is a growing need to investigate factors associated with chronic disease, rate of disease progression and survivorship. Many risk factors for this high-risk population change over time, such as participation in treatment, alcohol consumption and drug abuse. Longitudinal datasets are increasingly available, particularly clinical data that contain multiple observations of health exposures and outcomes over time. Several analytic options are available for assessment of longitudinal data; however, it can be challenging to choose the appropriate analytic method for specific combinations of research questions and types of data. The purpose of this review is to help researchers choose the appropriate methods to analyze longitudinal data, using alcohol consumption as an example of a time-varying exposure variable. When selecting the optimal analytic method, one must consider aspects of exposure (e.g. timing, pattern, and amount) and outcome (fixed or time-varying), while also addressing minimizing bias. In this article, we will describe several analytic approaches for longitudinal data, including developmental trajectory analysis, generalized estimating equations, and mixed effect models. For each analytic strategy, we describe appropriate situations to use the method and provide an example that demonstrates the use of the method. Clinical data related to alcohol consumption and HIV are used to illustrate these methods.

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Nonparametric regression analysis of multivariate longitudinal data

Cited by 29 publications

References 16 publications

Construction of an efficient multivariate dynamic screening system

Construction of an efficient multivariate dynamic screening system

Surveillance of cardiovascular diseases using a multivariate dynamic screening system

Analytic strategies to evaluate the association of time-varying exposures to HIV-related outcomes: Alcohol consumption as an example

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