The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Lue Ping Zhao - One of the best experts on this subject based on the ideXlab platform.

  • a weighted Estimating Equation for missing covariate data with properties similar to maximum likelihood
    Journal of the American Statistical Association, 1999
    Co-Authors: Stuart R Lipsitz, Joseph G Ibrahim, Lue Ping Zhao
    Abstract:

    Abstract In regression analysis, missing covariate data occurs often. A recent approach to analyzing such data is weighted Estimating Equations. With weighted Estimating Equations, the contribution to the Estimating Equation from a complete observation is weighted by the inverse probability of being observed. In this article we propose a weighted Estimating Equation that is almost identical to the maximum likelihood Estimating Equations. As such, we propose an EM-type algorithm to solve these weighted Estimating Equations. Although the weighted Estimating Equations are a special case of those proposed earlier by Robins et al., our EM-type algorithm to solve them is new. Similar to Robins and Ritov, we give the result that to obtain a consistent estimate of the regression parameters, either the missing-data mechanism or the distribution of the missing data given the observed data must be correctly specified. We compare the weighted Estimating Equations to maximum likelihood via two examples, a simulation a...

  • combined association and aggregation analysis of data from case control family studies
    Biometrika, 1998
    Co-Authors: Lue Ping Zhao, L I Hsu, Sarah Holte, Yan Chen, Filemon Quiaoit, Ross L Prentice
    Abstract:

    Genetic epidemiologists are increasingly interested in family studies using the case-control family study design, and this has motivated several recent developments in related statistical methodology. By summarising and extending some of these developments, this paper proposes an Estimating-Equation-based framework for analysing family data collected from case-control studies, motivated by the likelihood approach proposed by Whittemore (1995). The proposed Estimating Equation approach requires modelling assumptions about means, correlation coefficients and, at most, trivariate correlation coefficients, and it can be used for analysing family data with arbitrary pedigree structures, which would be prohibitive with the likelihood-based approach. Further, this new approach utilises all available data from case and control probands and from their relatives in assessing association between disease outcome and covariates, and data from relatives in evaluating aggregation of disease phenotypes within families, thus enhancing its efficiency. The approach is illustrated by analysing data collected from case-control studies on ovarian and colorectal cancers.

Yong Zhou - One of the best experts on this subject based on the ideXlab platform.

  • an embedded Estimating Equation for the additive risk model with biased sampling data
    Science China-mathematics, 2018
    Co-Authors: Feipeng Zhang, Xingqiu Zhao, Yong Zhou
    Abstract:

    This paper presents a novel class of semiparametric Estimating functions for the additive model with right-censored data that are obtained from general biased-sampling. The new estimator can be obtained using a weighted Estimating Equation for the covariate coeffcients, by embedding the biased-sampling data into left-truncated and right-censored data. The asymptotic properties (consistency and asymptotic normality) of the proposed estimator are derived via the modern empirical processes theory. Based on the cumulative residual processes, we also propose graphical and numerical methods to assess the adequacy of the additive risk model. The good finite-sample performance of the proposed estimator is demonstrated by simulation studies and two applications of real datasets.

  • composite Estimating Equation approach for additive risk model with length biased and right censored data
    Statistics & Probability Letters, 2015
    Co-Authors: Feipeng Zhang, Yong Zhou
    Abstract:

    Abstract We develop composite estimators and its large sample properties for the additive risk model with length-biased and right-censored data. We also conduct simulation studies to confirm the good finite sample performance of our methods and then give a real data example.

Yongtao Guan - One of the best experts on this subject based on the ideXlab platform.

  • a conditional Estimating Equation approach for recurrent event data with additional longitudinal information
    Statistics in Medicine, 2016
    Co-Authors: Ye Shen, Hui Huang, Yongtao Guan
    Abstract:

    : Recurrent event data are quite common in biomedical and epidemiological studies. A significant portion of these data also contain additional longitudinal information on surrogate markers. Previous studies have shown that popular methods using a Cox model with longitudinal outcomes as time-dependent covariates may lead to biased results, especially when longitudinal outcomes are measured with error. Hence, it is important to incorporate longitudinal information into the analysis properly. To achieve this, we model the correlation between longitudinal and recurrent event processes using latent random effect terms. We then propose a two-stage conditional Estimating Equation approach to model the rate function of recurrent event process conditioned on the observed longitudinal information. The performance of our proposed approach is evaluated through simulation. We also apply the approach to analyze cocaine addiction data collected by the University of Connecticut Health Center. The data include recurrent event information on cocaine relapse and longitudinal cocaine craving scores. Copyright © 2016 John Wiley & Sons, Ltd.

  • a weighted Estimating Equation approach for inhomogeneous spatial point processes
    Biometrika, 2010
    Co-Authors: Yongtao Guan, Ye Shen
    Abstract:

    We introduce a new estimation method for parametric intensity function models of inhomogeneous spatial point processes based on weighted Estimating Equations. The weights can incorporate information on both inhomogeneity and dependence of the process. Simulations show that significant efficiency gains can be achieved for non-Poisson processes, compared to the Poisson maximum likelihood estimator. An application to tropical forest data illustrates the use of the proposed method. Copyright 2010, Oxford University Press.

  • a weighted Estimating Equation approach for inhomogeneous spatial point processes
    Biometrika, 2010
    Co-Authors: Yongtao Guan, Ye Shen
    Abstract:

    We introduce a new estimation method for parametric intensity function models of inhomogeneous spatial point processes based on weighted Estimating Equations. The weights can incorporate information on both inhomogeneity and dependence of the process. Simulations show that significant efficiency gains can be achieved for non-Poisson processes, compared to the Poisson maximum likelihood estimator. An application to tropical forest data illustrates the use of the proposed method.

Silvia S Chiang - One of the best experts on this subject based on the ideXlab platform.

  • using changes in weight for age z score to predict effectiveness of childhood tuberculosis therapy
    Journal of the Pediatric Infectious Diseases Society, 2020
    Co-Authors: Silvia S Chiang, Sangshin Park, Emily I White, Jennifer F Friedman, Andrea T Cruz, Hernan Del Castillo, Leonid Lecca
    Abstract:

    BACKGROUND International guidelines recommend monitoring weight as an indicator of therapeutic response in childhood tuberculosis (TB) disease. This recommendation is based on observations in adults. In the current study, we evaluated the association between weight change and treatment outcome, the accuracy of using weight change to predict regimen efficacy, and whether successfully treated children achieve catch-up weight gain. METHODS We enrolled children treated for drug-susceptible TB disease (group 1) and multidrug-resistant TB disease (group 2) in Peru. We calculated the change in weight-for-age z score (ΔWAZ) between baseline and the end of treatment months 2-5 for group 1, and between baseline and months 2-8 for group 2. We used logistic regression and generalized Estimating Equation models to evaluate the relationship between ΔWAZ and outcome. We plotted receiver operating characteristic curves to determine the accuracy of ΔWAZ for predicting treatment failure or death. RESULTS Groups 1 and 2 included 100 and 94 children, respectively. In logistic regression, lower ΔWAZ in months 3-5 and month 7 was associated with treatment failure or death in groups 1 and 2, respectively. In generalized Estimating Equation models, children in both groups who experienced treatment failure or death had lower ΔWAZ than successfully treated children. The ΔWAZ predicted treatment failure or death with 60%-90% sensitivity and 60%-86% specificity in months 2-5 for group 1 and months 7-8 for group 2. All successfully treated children-except group 2 participants with unknown microbiologic confirmation status-achieved catch-up weight gain. CONCLUSIONS Weight change early in therapy can predict the outcome of childhood TB treatment.

Ye Shen - One of the best experts on this subject based on the ideXlab platform.

  • a conditional Estimating Equation approach for recurrent event data with additional longitudinal information
    Statistics in Medicine, 2016
    Co-Authors: Ye Shen, Hui Huang, Yongtao Guan
    Abstract:

    : Recurrent event data are quite common in biomedical and epidemiological studies. A significant portion of these data also contain additional longitudinal information on surrogate markers. Previous studies have shown that popular methods using a Cox model with longitudinal outcomes as time-dependent covariates may lead to biased results, especially when longitudinal outcomes are measured with error. Hence, it is important to incorporate longitudinal information into the analysis properly. To achieve this, we model the correlation between longitudinal and recurrent event processes using latent random effect terms. We then propose a two-stage conditional Estimating Equation approach to model the rate function of recurrent event process conditioned on the observed longitudinal information. The performance of our proposed approach is evaluated through simulation. We also apply the approach to analyze cocaine addiction data collected by the University of Connecticut Health Center. The data include recurrent event information on cocaine relapse and longitudinal cocaine craving scores. Copyright © 2016 John Wiley & Sons, Ltd.

  • a weighted Estimating Equation approach for inhomogeneous spatial point processes
    Biometrika, 2010
    Co-Authors: Yongtao Guan, Ye Shen
    Abstract:

    We introduce a new estimation method for parametric intensity function models of inhomogeneous spatial point processes based on weighted Estimating Equations. The weights can incorporate information on both inhomogeneity and dependence of the process. Simulations show that significant efficiency gains can be achieved for non-Poisson processes, compared to the Poisson maximum likelihood estimator. An application to tropical forest data illustrates the use of the proposed method. Copyright 2010, Oxford University Press.

  • a weighted Estimating Equation approach for inhomogeneous spatial point processes
    Biometrika, 2010
    Co-Authors: Yongtao Guan, Ye Shen
    Abstract:

    We introduce a new estimation method for parametric intensity function models of inhomogeneous spatial point processes based on weighted Estimating Equations. The weights can incorporate information on both inhomogeneity and dependence of the process. Simulations show that significant efficiency gains can be achieved for non-Poisson processes, compared to the Poisson maximum likelihood estimator. An application to tropical forest data illustrates the use of the proposed method.