Mixed effects models are often used for estimating fixed effects and variance components in longitudinal studies of continuous data. When the outcome being modelled is a laboratory measurement, however, it may be subj...
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Mixed effects models are often used for estimating fixed effects and variance components in longitudinal studies of continuous data. When the outcome being modelled is a laboratory measurement, however, it may be subject to lower and upper detection limits (i.e., censoring). In this paper, the usual em estimation procedure for mixed effects models is modified to account for left and/or right censoring.
In earlier work, my colleagues and I described a loglinear model for genetic data from triads composed of affected probands and their parents. This model allows detection of and discrimination between effects of an in...
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In earlier work, my colleagues and I described a loglinear model for genetic data from triads composed of affected probands and their parents. This model allows detection of and discrimination between effects of an inherited haplotype versus effects of the maternal,haplotype, which presumably would be mediated by prenatal factors. Like the transmission disequilibrium test (TDT), the likelihood-ratio test (LRT) based on this model is not sensitive to associations that are due to genetic admixture. When used as a method for testing for linkage disequilibrium, the LRT can-be regarded as an alternative:to the TDT. When one or both parents are missing, the resulting incomplete triad must be discarded to ensure validity of the TDT, thereby sacrificing information. By contrast, when the problem is set in a likelihood framework, the :expectation-maximization algorithm allows the incomplete triads to contribute their information to the LRT without invalidation of the analysis. Simulations demonstrate that much of the lost statistical power can be recaptured by means of this missing-data technique. In fact, power is reasonably good even when no triad is complete-for example;when a study is designed to include only mothers of cases. Information from siblings also can be incorporated to further improve the statistical power when genetic data from parents or probands are missing.
作者：
Marchette, DJPoston, WLUSN
Computat Stat Grp Ctr Surface Warfare Dahlgren VA 22448 USA USN
Adv Processors Grp Ctr Surface Warfare Dahlgren VA 22448 USA
In automatic pattern recognition applications, numerous features that describe the classes are obtained in an attempt to ensure accurate classification of unknown observations. These features or dimensions must be red...
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In automatic pattern recognition applications, numerous features that describe the classes are obtained in an attempt to ensure accurate classification of unknown observations. These features or dimensions must be reduced to a smaller number before classification schemes can be applied, because classifiers become computationally and analytically unmanageable in high dimensions;Principal components and Fisher's Linear Discriminant offer global dimensionality reduction within the framework of linear algebra applied to covariance matrices. This report describes local methods that use both mixture-models and nearest neighbor calculations to construct local versions of these methods. These new versions for local dimensionality reduction will provide increased classification accuracy in lower dimensions.
We describe a semiparametric mixture model for human fertility studies. The probability of conception is a product of two components. The mixing distribution, the component that introduces the heterogeneity among the ...
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We describe a semiparametric mixture model for human fertility studies. The probability of conception is a product of two components. The mixing distribution, the component that introduces the heterogeneity among the menstrual cycles that come from different couples, is characterized nonparametrically by a finite number of moments. The second component, the intercourse-related probability is modeled parametrically to assess the possible exposure effects. We discuss an em algorithm-based estimating procedure that incorporates the natural order in the moments. (C) 1999 Elsevier Science B.V. All rights reserved.
Purpose. To develop a pharmacokinetic model for tenidap and to identify important relationships between the pharmacokinetic parameters and available covariates. Methods. Plasma concentration data from several phase I ...
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Purpose. To develop a pharmacokinetic model for tenidap and to identify important relationships between the pharmacokinetic parameters and available covariates. Methods. Plasma concentration data from several phase I and phase II studies were used to develop a pharmacokinetic model for tenidap, a novel anti-rheumatic drug. An appropriate pharmacokinetic model was selected on the basis of individual nonlinear regression analyses and an em algorithm was used to perform a nonlinear mixed-effects analysis. Scatter plots of posterior individual pharmacokinetic parameters were used to identify possible covariate effects. Results. predicted responses were in good agreement with the observed data. A biexponential model with zero order absorption was subsequently used to develop the mixed-effects model. Covariate relationships selected on the basis of differences in the objective function, although statistically significant, were not particularly strong. Conclusions. The pharmacokinetics of tenidap can be described by a bi-exponential model with zero order absorption. Based on differences in the log-likelihood, significant covariate-parameter relationships were identified between smoking and CL, and between gender and Vss and CLd Simulated sparse data analyses indicated that the model would be robust for the analysis of sparse data, generated in observational studies.
An important inferential objective in state space modelling is to recover unobserved states using fixed-interval smoothing. Thus, the identification of cases which have a substantial influence on the smoothers is a re...
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An important inferential objective in state space modelling is to recover unobserved states using fixed-interval smoothing. Thus, the identification of cases which have a substantial influence on the smoothers is a relevant practical problem. To facilitate this identification, we propose a case-deletion diagnostic which can be easily computed using the outputs of the standard filtering and smoothing algorithms. Our diagnostic is defined as the Kullback-Leibler directed divergence between two versions of the conditional density which determines the smoothers, one based on all the data, the other based on all the data except for the case or cases in question. We investigate the detection performance of the diagnostic in a practical application.
We propose a method for estimating parameters in generalized linear models with missing covariates and a non-ignorable missing data mechanism. We use a multinomial model for the missing data indicators and propose a j...
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We propose a method for estimating parameters in generalized linear models with missing covariates and a non-ignorable missing data mechanism. We use a multinomial model for the missing data indicators and propose a joint distribution for them which can be written as a sequence of one-dimensional conditional distributions, with each one-dimensional conditional distribution consisting of a logistic regression. We allow the covariates to be either categorical or continuous. The joint covariate distribution is also modelled via a sequence of one-dimensional conditional distributions, and the response variable is assumed to be completely observed. We derive the E- and M-steps of the em algorithm with non-ignorable missing covariate data. For categorical covariates, we derive a closed form expression for the E- and M-steps of the em algorithm for obtaining the maximum likelihood estimates (MLEs). For continuous covariates, we use a Monte Carlo version of the em algorithm to obtain the MLEs via the Gibbs sampler. Computational techniques for Gibbs sampling are proposed and implemented. The parametric form of the assumed missing data mechanism itself is not 'testable' from the data, and thus the non-ignorable modelling considered here can be viewed as a sensitivity analysis concerning a more complicated model. Therefore, although a model may have 'passed' the tests for a certain missing data mechanism, this does not mean that we have captured, even approximately, the correct missing data mechanism. Hence, model checking for the missing data mechanism and sensitivity analyses play an important role in this problem and are discussed in detail. Several simulations are given to demonstrate the methodology. In addition, a real data set from a melanoma cancer clinical trial is presented to illustrate the methods proposed.
We propose a nonstationary state space model for multivariate longitudinal count data driven by a latent gamma Markov process. The Poisson counts are assumed to be conditionally independent given the latent process, b...
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We propose a nonstationary state space model for multivariate longitudinal count data driven by a latent gamma Markov process. The Poisson counts are assumed to be conditionally independent given the latent process, both over time and across categories. We consider a regression model where time-varying covariates may enter via either the Poisson model or the latent gamma process. Estimation is based on the Kalman smoother, and we consider analysis of residuals from both the Poisson model and the latent process. A reanalysis of Zeger's (1988) polio data shows that the choice between a stationary and nonstationary model is crucial for the correct assessment of the evidence of a long-term decrease in the rate of U.S. polio infection.
This paper proposes a new approach to the treatment of item non-response in attitude scales. It combines the ideas of latent variable identification with the issues of non-response adjustment in sample surveys. The la...
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This paper proposes a new approach to the treatment of item non-response in attitude scales. It combines the ideas of latent variable identification with the issues of non-response adjustment in sample surveys. The latent variable approach allows missing values to be included in the analysis and, equally importantly, allows information about attitude to be inferred from non-response. We present a symmetric pattern methodology for handling item non-response in attitude scales. The methodology is symmetric in that all the variables are given equivalent status in the analysis (none is designated a 'dependent' variable) and is pattern based in that the pattern of responses and non-responses across individuals is a key element in the analysis. Our approach to the problem is through a latent variable model with two latent dimensions: one to summarize response propensity and the other to summarize attitude, ability or belief. The methodology presented here can handle binary, metric and mixed (binary and metric) manifest items with missing values. Examples using both artificial data sets and two real data sets are used to illustrate the mechanism and the advantages of the methodology proposed.
For contingency tables with extensive missing data, the unrestricted MLE under the saturated model, computed by the em algorithm, is generally unsatisfactory. In this case, it may be better to fit a simpler model by i...
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For contingency tables with extensive missing data, the unrestricted MLE under the saturated model, computed by the em algorithm, is generally unsatisfactory. In this case, it may be better to fit a simpler model by imposing some restrictions on the parameter space. Perlman and Wu (1999) propose lattice conditional independence (LCI) models for contingency tables with arbitrary missing data patterns. When this LCI model fits well, the restricted MLE under the LCI model is more accurate than the unrestricted MLE under the saturated model, but not in general. Here we propose certain empirical Bayes (EB) estimators that adaptively combine the best features of the restricted and unrestricted MLEs. These EB estimators appear to be especially useful when the observed data is sparse, even in cases where the suitability of the LCI model is uncertain. We also study a restricted em algorithm (called the ER algorithm) with similar desirable features.
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