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Assessing treatment efficacy in the presence of diagnostic errors

STATISTICS IN MEDICINE 2016年第29期35卷 5338-5355页

作者： Harrar, Solomon Amatya, Anup Kalachev, Leonid Univ Kentucky Dept Stat Multidisciplinary Sci 347 725 Rose St Lexington KY 40536 USA New Mexico State Univ Dept Hlth Sci 1335 Int MallSuite 326 Las Cruces NM 88003 USA Univ Montana Dept Math Sci 32 Campus Dr Missoula MT 59812 USA

This paper investigates estimating and testing treatment effects in randomized control trials where imperfect diagnostic device is used to assign subjects to treatment and control group(s). The paper focuses on pre-post design and proposes two new methods for estimating and testing treatment effects. Furthermore, methods for computing sample sizes for such design accounting for misclassification of the subjects are devised. The methods are compared with each other and with a traditional method that ignores the imperfection of the diagnostic device. In particular, the likelihood-based approach shows a significant advantage in terms of power, coverage probability and, consequently, in reduction of the required sample size. The application of the results are illustrated with data from an aging trial for dementia and data from electroencephalogram (EEG) recordings of alcoholic and non-alcoholic subjects. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： diagnostic accuracy expectation-maximization algorithm mixture of normals negative predictive value positive predictive value power sample size

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Multivariate mixtures of Erlangs for density estimation under censoring

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LIFETIME DATA ANALYSIS 2016年第3期22卷 429-455页

作者： Verbelen, Roel Antonio, Katrien Claeskens, Gerda Katholieke Univ Leuven Fac Econ & Business Leuven Stat Res Ctr Leuven Belgium Univ Amsterdam Fac Econ & Business Amsterdam Netherlands

Multivariate mixtures of Erlang distributions form a versatile, yet analytically tractable, class of distributions making them suitable for multivariate density estimation. We present a flexible and effective fitting procedure for multivariate mixtures of Erlangs, which iteratively uses the EM algorithm, by introducing a computationally efficient initialization and adjustment strategy for the shape parameter vectors. We furthermore extend the EMalgorithm for multivariatemixtures of Erlangs to be able to deal with randomly censored and fixed truncated data. The effectiveness of the proposed algorithm is demonstrated on simulated as well as real data sets.

关键词： Multivariate mixtures of Erlangs with a common scale parameter Density estimation Censored data expectation-maximization algorithm Maximum likelihood

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Composite likelihood and maximum likelihood methods for joint latent class modeling of disease prevalence and high-dimensional semicontinuous biomarker data

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COMPUTATIONAL STATISTICS 2016年第2期31卷 425-449页

作者： Zhang, Bo Liu, Wei Zhang, Hui Chen, Qihui Zhang, Zhiwei US FDA Ctr Devices & Radiol Hlth Off Surveillance & Biometr Div Biostat Silver Spring MD 20993 USA Harbin Inst Technol Dept Math Harbin 150001 Peoples R China St Jude Childrens Res Hosp Dept Biostat 332 N Lauderdale St Memphis TN 38105 USA China Agr Univ Coll Econ & Management Dept Appl Econ Beijing 100083 Peoples R China

Joint latent class modeling of disease prevalence and high-dimensional semicontinuous biomarker data has been proposed to study the relationship between diseases and their related biomarkers. However, statistical inference of the joint latent class modeling approach has proved very challenging due to its computational complexity in seeking maximum likelihood estimates. In this article, we propose a series of composite likelihoods for maximum composite likelihood estimation, as well as an enhanced Monte Carlo expectation-maximization (MCEM) algorithm for maximum likelihood estimation, in the context of joint latent class models. Theoretically, the maximum composite likelihood estimates are consistent and asymptotically normal. Numerically, we have shown that, as compared to the MCEM algorithm that maximizes the full likelihood, not only the composite likelihood approach that is coupled with the quasi-Newton method can substantially reduce the computational complexity and duration, but it can simultaneously retain comparative estimation efficiency.

关键词： Pseudo-likelihood expectation-maximization algorithm Markov chain Monte Carlo Shared latent class models Two-part models

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Misclassified group-tested current status data

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BIOMETRIKA 2016年第4期103卷 801-815页

作者： Petito, L. C. Jewell, N. P. Univ Calif Berkeley Sch Publ Hlth Div Biostat 101 Haviland Hall Berkeley CA 94720 USA

Group testing, introduced by Dorfman (1943), has been used to reduce costs when estimating the prevalence of a binary characteristic based on a screening test of k groups that include n independent individuals in total. If the unknown prevalence is low and the screening test suffers from misclassification, it is also possible to obtain more precise prevalence estimates than those obtained from testing all n samples separately (Tu et al., 1994). In some applications, the individual binary response corresponds to whether an underlying time-to-event variable T is less than an observed screening time C, a data structure known as current status data. Given sufficient variation in the observed C values, it is possible to estimate the distribution function F of T nonparametrically, at least at some points in its support, using the pool-adjacent-violators algorithm (Ayer et al., 1955). Here, we consider nonparametric estimation of F based on group-tested current status data for groups of size k where the group tests positive if and only if any individual's unobserved T is less than the corresponding observed C. We investigate the performance of the group-based estimator as compared to the individual test nonparametric maximum likelihood estimator, and show that the former can be more precise in the presence of misclassification for low values of F(t). Potential applications include testing for the presence of various diseases in pooled samples where interest focuses on the age-at-incidence distribution rather than overall prevalence. We apply this estimator to the age-at-incidence curve for hepatitis C infection in a sample of U.S. women who gave birth to a child in 2014, where group assignment is done at random and based on maternal age. We discuss connections to other work in the literature, as well as potential extensions.

关键词： Current status data expectation-maximization algorithm Group testing Pool-adjacent-violators algorithm

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A unified Bayesian mixture model framework via spatial information for grayscale image segmentation

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2016年第PartA期40卷 345-356页

作者： Xiong, Taisong Huang, Yuanyuan Gou, Jianping Hu, Jinrong Chengdu Univ Informat Technol Chengdu 610225 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China JiangSu Univ Sch Comp Sci & Telecommun Engn Zhenjiang 212013 Jiangsu Peoples R China Xihua Univ Sch Comp & Soft Engn Chengdu 610039 Peoples R China

Because of the Student-t distribution owning heavier tailed than the Gaussian distribution, under a Bayesian framework, a spatially variant finite mixture model with Student's t-distribution component function is proposed for grayscale image segmentation. To avoid additional computational step and improve the efficiency of the proposed model, a representation of contextual mixing proportion is adopted. Secondly, the spatial information of the pixels is closely related to the Gaussian distribution of their neighborhood system. Thirdly, the inherent relationship between the Gaussian distribution and the Student's t-distribution is adopted to optimize the unknown parameters of the proposed model, which simplifies the inference process and makes the proposed model to be easily implemented. Comprehensive experiments on synthetic noise images, simulated medical images and real-world grayscale images are presented to illustrate the superior performance of the proposed model in terms of the visual and quantitative comparison. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Spatially variant finite mixture model Bayesian framework Gaussian distribution Student's t-distribution Grayscale image segmentation expectation-maximization algorithm

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Nonparametric maximum likelihood estimation for the multisample Wicksell corpuscle problem

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BIOMETRIKA 2016年第2期103卷 273-286页

作者： Chan, Kwun Chuen Gary Qin, Jing Univ Washington Dept Biostat Seattle WA 98195 USA NIAID Biostat Res Branch Bethesda MD 20892 USA

We study nonparametric maximum likelihood estimation for the distribution of spherical radii using samples containing a mixture of one-dimensional, two-dimensional biased and three-dimensional unbiased observations. Since direct maximization of the likelihood function is intractable, we propose an expectation-maximization algorithm for implementing the estimator, which handles an indirect measurement problem and a sampling bias problem separately in the E- and M-steps, and circumvents the need to solve an Abel-type integral equation, which creates numerical instability in the one-sample problem. Extensions to ellipsoids are studied and connections to multiplicative censoring are discussed.

关键词： Abel-type integral equation expectation-maximization algorithm Indirect measurement Particle size

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Two-stage marker-stratified clinical trial design in the presence of biomarker misclassification

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2016年第4期65卷 585-601页

作者： Zang, Yong Lee, J. Jack Yuan, Ying Florida Atlantic Univ Boca Raton FL 33431 USA Univ Texas MD Anderson Canc Ctr Houston TX 77030 USA

The marker-stratified design (MSD) is an important design to assess treatment and marker effects in personalized medicine. The MSD stratifies patients into marker positive and marker negative subgroups on the basis of their biomarker profiles and then randomizes them to the standard treatment or a new treatment within each subgroup. The performance of the MSD can be seriously undermined when the biomarker is measured with error (or misclassified). A recently proposed analytic method corrects the biomarker misclassification in the MSD under the assumptions that the biomarker classification rates are known and no other covariates need to be adjusted. We propose a two-stage MSD to relax these assumptions. We analytically investigate the bias in the estimation of prognostic and predictive marker effects and treatment effects caused by biomarker misclassification in the presence of covariates, and we propose an expectation-maximization algorithm to correct such biases. The design does not require prespecification of the misclassification rates and can incorporate any covariates that potentially confound the prognostic and predictive marker effects and treatment effect. Numerical trial applications show that the method has desirable operating characteristics.

关键词： Clinical trial expectation-maximization algorithm Marker-stratified design Misclassification Molecularly targeted agent Personalized medicine

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Meta-Analysis With a Continuous Covariate That Is Differentially Categorized Across Studies

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AMERICAN JOURNAL OF EPIDEMIOLOGY 2016年第5期183卷 507-514页

作者： Perin, Jamie Walker, Christa L. Fischer Black, Robert E. Aryee, Martin J. Johns Hopkins Bloomberg Sch Publ Hlth Dept Int Hlth 615 N Wolfe St Baltimore MD 21205 USA

We propose taking advantage of methodology for missing data to estimate relationships and adjust outcomes in a meta-analysis where a continuous covariate is differentially categorized across studies. The proposed method incorporates all available data in an implementation of the expectation-maximization algorithm. We use simulations to demonstrate that the proposed method eliminates bias that would arise by ignoring a covariate and generalizes the meta-analytical approach for incorporating covariates that are not uniformly categorized. The proposed method is illustrated in an application for estimating diarrhea incidence in children aged a parts per thousand currency sign59 months.

关键词： age diarrhea incidence expectation-maximization algorithm incomplete data meta-regression

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Estimating Allele Frequencies

Estimating Allele Frequencies

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作者： Indra Adrianto Courtney Montgomery

Methods of estimating allele frequencies from data on unrelated and related individuals are described in this chapter. For samples of unrelated individuals with simple codominant markers, the natural estimators of allele frequencies can be used. For genetic data on related individuals, maximum likelihood estimation (MLE) can be applied to compute allele frequencies. Factors that influence allele frequencies in populations are also explained. less

关键词： Genome-Wide Association Study DNA Sequencing Linkage Analysis Migration Mutation Population genetics Genotype Allele Phenotype Maximum likelihood estimation Hardy–Weinberg equilibrium Families Genetic drift Natural selection Pedigree Natural estimator Unrelated individuals Related individuals Relatives Founder Nonfounder Disease research Log-likelihood expectation-maximization algorithm ABO blood group Nonrandom mating

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Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies

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JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS 2016年第2期41卷 146-179页

作者： Bartolucci, Francesco Pennoni, Fulvia Vittadini, Giorgio Univ Perugia Dept Econ Stat I-06123 Perugia Italy Univ Milano Bicocca Dept Stat & Quantitat Methods Stat I-20126 Milan Italy Univ Milano Bicocca Dept Stat & Quantitat Methods Methodol Stat I-20126 Milan Italy

We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects on subpopulations of individuals from a dynamic perspective, as it relies on a latent Markov (LM) model that is estimated taking into account propensity score weights based on individual pretreatment covariates. These weights are involved in the expression of the likelihood function of the LM model and allow us to balance the groups receiving different treatments. This likelihood function is maximized through a modified version of the traditional expectation-maximization algorithm, while standard errors for the parameter estimates are obtained by a nonparametric bootstrap method. We study in detail the asymptotic properties of the causal effect estimator based on the maximization of this likelihood function, and we illustrate its finite sample properties through a series of simulations showing that the estimator has the expected behavior. As an illustration, we consider an application aimed at assessing the relative effectiveness of certain degree programs on the basis of three ordinal response variables in which the work path of a graduate is considered as the manifestation of his or her human capital-level across time.

关键词： causal inference expectation-maximization algorithm hidden Markov models multiple treatments policy evaluation propensity score

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