1,721,183 research outputs found
Simple moment estimates of the kappa-coefficient and its variance
No full textEstimating equations are used to develop simple non-iterative estimates of the kappa-coefficient that can be used when there are more than two random raters and/or unbalanced data (each subject is not judged by every rater). We show that there is a simple way to estimate the variance of any estimate of the kappa-coefficient that is a solution to an estimating equation. Two non-iterative estimates that are shown to be solutions to estimating equations are Fleiss's estimate and Schouten's estimate. Also, assuming that the underlying data are beta-binomial, we compare the asymptotic relative efficiency of the non-iterative estimators Of kappa relative to the iterative maximum likelihood estimator (MLE) of kappa from the beta-binomial distribution. Fleiss's estimator was found to have high efficiency. Finally, simulations are used to compare the finite sample performance of these estimators as well as the MLE from the beta-binomial distribution. In the simulations, the Newton-Raphson algorithm for the MLE from the beta-binomial model did not always converge in small samples, which also supports the use of a non-iterative estimate in small samples. The estimators are also compared by using a psychiatric data set given by Fleiss
Simple moment estimates of the kappa-coefficient and its variance
No full textEstimating equations are used to develop simple non-iterative estimates of the kappa-coefficient that can be used when there are more than two random raters and/or unbalanced data (each subject is not judged by every rater). We show that there is a simple way to estimate the variance of any estimate of the kappa-coefficient that is a solution to an estimating equation. Two non-iterative estimates that are shown to be solutions to estimating equations are Fleiss's estimate and Schouten's estimate. Also, assuming that the underlying data are beta-binomial, we compare the asymptotic relative efficiency of the non-iterative estimators Of kappa relative to the iterative maximum likelihood estimator (MLE) of kappa from the beta-binomial distribution. Fleiss's estimator was found to have high efficiency. Finally, simulations are used to compare the finite sample performance of these estimators as well as the MLE from the beta-binomial distribution. In the simulations, the Newton-Raphson algorithm for the MLE from the beta-binomial model did not always converge in small samples, which also supports the use of a non-iterative estimate in small samples. The estimators are also compared by using a psychiatric data set given by Fleiss
GEE with Gaussian estimation of the correlations when data are incomplete
No full textThis paper considers a modification of generalized estimating equations (GEE) for handling missing binary response data. The proposed method uses Gaussian estimation of the correlation parame- ters, i.e., the estimating function that yields an estimate of the correlation parameters is obtained from the multivariate normal likelihood. The proposed method yields consistent estimates of the regression param- eters when data are missing completely at random (MCAR). However, when data are missing at random (MAR), consistency may not hold. In a simulation study with repeated binary outcomes that are missing at random, the magnitude of the potential bias that can arise is examined. The results of the simulation study indicate that, when the working correlation matrix is correctly specified, the bias is almost negligible for the modified GEE. In the simulation study, the proposed modification of GEE is also compared to the standard GEE, multiple imputation, and weighted estimating equations approaches. Finally, the proposed method is illustrated using data from a longitudinal clinical trial comparing two therapeutic treatments, zidovudine (AZT) and didanosine (ddI), in patients with HIV.We are grateful for the support provided by grants CA 57253, CA 55576, CA 70101-01, CA 74015-01, and GM 29745 from the NIH, by funding from the National Fonds voor Weten- schappelijk Onderzoek (Belgium), and by NATO collabora- tive research grant G50648
A protective estimator for linear regression with nonignorably missing Gaussian outcomes
No full textWe propose a method for estimating the regression parameters in a linear regression model for Gaussian data when the outcome variable is missing for some subjects and missingness is thought to be nonignorable. Throughout, we assume that missingness is restricted to the outcome variable and that the covariates are fully observed. Although maximum likelihood estimation of the regression parameters is possible once joint models for the outcome variable and the nonignorable missing data mechanism have been specified, these models are fundamentally nonidentifiable unless unverifiable modeling assumptions are imposed. In this paper, rather than explicitly modeling the nonignorable missingness mechanism, we consider the use of a ‘protective’ estimator of the regression parameters (Brown, 1990). To implement the proposed method, it is necessary to assume that the outcome variable and one of the covariates have an approximate bivariate normal distribution, conditional on the remaining covariates. In addition, it is assumed that the missing data mechanism is conditionally independent of this covariate, given the outcome variable and the remaining covariates; the latter is referred to as the ‘protective’ assumption. A method of moments approach is used to obtain the protective estimator of the regression parameters; the jackknife (Quenouille, 1956) is used to estimate the variance. The method is illustrated using data on the persistence of maternal smoking from the Six Cities Study of the health effects of air pollution (Ware et al., 1984). The results of a simulation study are presented that examine the magnitude of any finite sample bias. © 2004, Sage Publications. All rights reserved.status: Publishe
Bias in Estimating Association Parameters for Longitudinal Binary Responses with Drop‐Outs
No full textThis paper considers the impact of bias in the estimation of the association parameters for longitudinal binary responses when there are drop-outs. A number of different estimating equation approaches are considered for the case where drop-out cannot be assumed to be a completely random process. In particular, standard generalized estimating equations (GEE), GEE based on conditional residuals, GEE based on multivariate normal estimating equations for the covariance matrix, and second-order estimating equations (GEE2) are examined. These different GEE estimators are compared in terms of finite sample and asymptotic bias under a variety of drop-out processes. Finally, the relationship between bias in the estimation of the association parameters and bias in the estimation of the mean parameters is explored.sponsorship: NIEHS NIH HHS|ES07142, NIGMS NIH HHS|GM29745, NIMH NIH HHS|MH17119status: Publishe
A protective estimator for longitudinal binary data subject to non-ignorable non-monotone missingness
No full textIn longitudinal studies missing data are the rule not the exception. We consider the analysis of longitudinal binary data with non-monotone missingness that is thought to be non-ignorable. In this setting a full likelihood approach is complicated algebraically and can be computationally prohibitive when there are many measurement occasions. We propose a 'protective' estimator that assumes that the probability that a response is missing at any occasion depends, in a completely unspecified way, on the value of that variable alone. Relying on this 'protectiveness' assumption, we describe a pseudolikelihood estimator of the regression parameters under non-ignorable missingness, without having to model the missing data mechanism directly. The method proposed is applied to CD4 cell count data from two longitudinal clinical trials of patients infected with the human immunodeficiency virus. Copyright 2005 Royal Statistical Society.
A weighted combination of pseudo-likelihood estimators for longitudinal binary data subject to non-ignorable non-monotone missingness
Full text linkFor longitudinal binary data with non-monotone non-ignorably missing outcomes over time, a full likelihood approach is complicated algebraically, and with many follow-up times, maximum likelihood estimation can be computationally prohibitive. As alternatives, two pseudo-likelihood approaches have been proposed that use minimal parametric assumptions. One formulation requires specification of the marginal distributions of the outcome and missing data mechanism at each time point, but uses an 'independence working assumption,' i.e. an assumption that observations are independent over time. Another method avoids having to estimate the missing data mechanism by formulating a 'protective estimator.' In simulations, these two estimators can be very inefficient, both for estimating time trends in the first case and for estimating both time-varying and time-stationary effects in the second. In this paper, we propose the use of the optimal weighted combination of these two estimators, and in simulations we show that the optimal weighted combination can be much more efficient than either estimator alone. Finally, the proposed method is used to analyze data from two longitudinal clinical trials of HIV-infected patients.sponsorship: The authors are grateful for constructive comments from two reviewers, and for the support provided by the following grants from the US National Institutes of Health: AI 60373, CA 68484, CA69222, CA 74015, CA 70101, GM 29745, MH 054693. Andrea Troxel gratefully acknowledges support from the Columbia University Institute for Scholars at Reid Hall, Paris. Geert Molenberghs gratefully acknowledges financial support from the Belgian Science Policy IAP research network #P6/03. (US National Institutes of Health|AI 60373, US National Institutes of Health|CA 68484, US National Institutes of Health|CA69222, US National Institutes of Health|CA 74015, US National Institutes of Health|CA 70101, US National Institutes of Health|GM 29745, US National Institutes of Health|MH 054693, Columbia University Institute for Scholars at Reid Hall, Paris, Belgian Science Policy IAP|P6/03)status: Publishe
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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