Random Effects Model

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So Young Sohn - One of the best experts on this subject based on the ideXlab platform.

  • Random Effects Model for credit rating transitions
    European Journal of Operational Research, 2008
    Co-Authors: Yoonseong Kim, So Young Sohn
    Abstract:

    This paper proposes a Random Effects multinomial regression Model to estimate transition probabilities of credit ratings. Unlike the previous studies on the rating transition, we applied a Random Effects Model, which accommodates not only the environmental characteristics of the exposures of a rating but also the uncertainty not explained by such factors. The rating category specific factors such as retained earning and market equity are included in our proposed Model. The Random Effects Model provides less diagonally dominant matrix, where the transition probabilities are over-dispersed from the diagonal elements. Our study is expected to incorporate potential chances of rating transitions due to extra Random variations.

  • Random Effects Model for the reliability management of modules of a fighter aircraft
    Reliability Engineering & System Safety, 2006
    Co-Authors: So Young Sohn, Kyung Bok Yoon, In Sang Chang
    Abstract:

    The operational availability of fighter aircrafts plays an important role in the national defense. Low operational availability of fighter aircrafts can cause many problems and ROKA (Republic of Korea Airforce) needs proper strategies to improve the current practice of reliability management by accurately forecasting both MTBF (mean time between failure) and MTTR (mean time to repair). In this paper, we develop a Random Effects Model to forecast both MTBF and MTTR of installed modules of fighter aircrafts based on their characteristics and operational conditions. Advantage of using such a Random Effects Model is the ability of accommodating not only the individual characteristics of each module and operational conditions but also the uncertainty caused by Random error that cannot be explained by them. Our study is expected to contribute to ROKA in improving operational availability of fighter aircrafts and establishing effective logistics management.

Eleazar Eskin - One of the best experts on this subject based on the ideXlab platform.

  • Random Effects Model aimed at discovering associations in meta analysis of genome wide association studies
    American Journal of Human Genetics, 2011
    Co-Authors: Buhm Han, Eleazar Eskin
    Abstract:

    Meta-analysis is an increasingly popular tool for combining multiple different genome-wide association studies (GWASs) in a single aggregate analysis in order to identify associations with very small effect sizes. Because the data of a meta-analysis can be heterogeneous, referring to the differences in effect sizes between the collected studies, what is often done in the literature is to apply both the fixed-Effects Model (FE) under an assumption of the same effect size between studies and the Random-Effects Model (RE) under an assumption of varying effect size between studies. However, surprisingly, RE gives less significant p values than FE at variants that actually show varying effect sizes between studies. This is ironic because RE is designed specifically for the case in which there is heterogeneity. As a result, usually, RE does not discover any associations that FE did not discover. In this paper, we show that the underlying reason for this phenomenon is that RE implicitly assumes a markedly conservative null-hypothesis Model, and we present a new Random-Effects Model that relaxes the conservative assumption. Unlike the traditional RE, the new method is shown to achieve higher statistical power than FE when there is heterogeneity, indicating that the new method has practical utility for discovering associations in the meta-analysis of GWASs.

  • Random Effects Model aimed at discovering associations in meta analysis of genome wide association studies
    American Journal of Human Genetics, 2011
    Co-Authors: Eleazar Eskin
    Abstract:

    Meta-analysis is an increasingly popular tool for combining multiple different genome-wide association studies (GWASs) in a single aggregate analysis in order to identify associations with very small effect sizes. Because the data of a meta-analysis can be heterogeneous, referring to the differences in effect sizes between the collected studies, what is often done in the literature is to apply both the fixed-Effects Model (FE) under an assumption of the same effect size between studies and the Random-Effects Model (RE) under an assumption of varying effect size between studies. However, surprisingly, RE gives less significant p values than FE at variants that actually show varying effect sizes between studies. This is ironic because RE is designed specifically for the case in which there is heterogeneity. As a result, usually, RE does not discover any associations that FE did not discover. In this paper, we show that the underlying reason for this phenomenon is that RE implicitly assumes a markedly conservative null-hypothesis Model, and we present a new Random-Effects Model that relaxes the conservative assumption. Unlike the traditional RE, the new method is shown to achieve higher statistical power than FE when there is heterogeneity, indicating that the new method has practical utility for discovering associations in the meta-analysis of GWASs.

Lei Liu - One of the best experts on this subject based on the ideXlab platform.

  • a flexible two part Random Effects Model for correlated medical costs
    Journal of Health Economics, 2010
    Co-Authors: Lei Liu, Robert L Strawderman, Mark E Cowen, Yachen Tina Shih
    Abstract:

    In this paper, we propose a flexible "two-part" Random Effects Model (Olsen and Schafer, 2001; Tooze et al., 2002) for correlated medical cost data. Typically, medical cost data are right-skewed, involve a substantial proportion of zero values, and may exhibit heteroscedasticity. In many cases, such data are also obtained in hierarchical form, e.g., on patients served by the same physician. The proposed Model specification therefore consists of two generalized linear mixed Models (GLMM), linked together by correlated Random Effects. Respectively, and conditionally on the Random Effects and covariates, we Model the odds of cost being positive (Part I) using a GLMM with a logistic link and the mean cost (Part II) given that costs were actually incurred using a generalized gamma regression Model with Random Effects and a scale parameter that is allowed to depend on covariates (cf., Manning et al., 2005). The class of generalized gamma distributions is very flexible and includes the lognormal, gamma, inverse gamma and Weibull distributions as special cases. We demonstrate how to carry out estimation using the Gaussian quadrature techniques conveniently implemented in SAS Proc NLMIXED. The proposed Model is used to analyze pharmacy cost data on 56,245 adult patients clustered within 239 physicians in a mid-western U.S. managed care organization.

  • a multi level two part Random Effects Model with application to an alcohol dependence study
    Statistics in Medicine, 2008
    Co-Authors: Lei Liu, Jennie Z, Bankole A Johnson
    Abstract:

    Two-part Random Effects Models (J. Am. Statist. Assoc. 2001; 96:730-745; Statist. Methods Med. Res. 2002; 11:341-355) have been applied to longitudinal studies for semi-continuous outcomes, characterized by a large portion of zero values and continuous non-zero (positive) values. Examples include repeated measures of daily drinking records, monthly medical costs, and annual claims of car insurance. However, the question of how to apply such Models to multi-level data settings remains. In this paper, we propose a novel multi-level two-part Random Effects Model. Distinct Random Effects are used to characterize heterogeneity at different levels. Maximum likelihood estimation and inference are carried out through Gaussian quadrature technique, which can be implemented conveniently in freely available software-aML. The Model is applied to the analysis of repeated measures of the daily drinking record in a Randomized controlled trial of topiramate for alcohol-dependence treatment.

  • a shared Random Effects Model for censored medical costs and mortality
    Statistics in Medicine, 2007
    Co-Authors: Lei Liu, Robert A Wolfe, John D Kalbfleisch
    Abstract:

    In this paper, we propose a Model for medical costs recorded at regular time intervals, e.g. every month, as repeated measures in the presence of a terminating event, such as death. Prior Models have related monthly medical costs to time since entry, with extra costs at the final observations at the time of death. Our joint Model for monthly medical costs and survival time incorporates two important new features. First, medical cost and survival may be correlated because more 'frail' patients tend to accumulate medical costs faster and die earlier. A joint Random Effects Model is proposed to account for the correlation between medical costs and survival by a shared Random effect. Second, monthly medical costs usually increase during the time period prior to death because of the intensive care for dying patients. We present a method for estimating the pattern of cost prior to death, which is applicable if the pattern can be characterized as an additive effect that is limited to a fixed time interval, say b units of time before death. This 'turn back time' method for censored observations censors cost data b units of time before the actual censoring time, while keeping the actual censoring time for the survival data. Time-dependent covariates can be included. Maximum likelihood estimation and inference are carried out through a Monte Carlo EM algorithm with a Metropolis-Hastings sampler in the E-step. An analysis of monthly outpatient EPO medical cost data for dialysis patients is presented to illustrate the proposed methods.

Christian Gluud - One of the best experts on this subject based on the ideXlab platform.

  • comparison of statistical inferences from the dersimonian laird and alternative Random Effects Model meta analyses an empirical assessment of 920 cochrane primary outcome meta analyses
    Research Synthesis Methods, 2011
    Co-Authors: Kristian Thorlund, Jorn Wetterslev, Tahany Awad, Lehana Thabane, Christian Gluud
    Abstract:

    In Random-Effects Model meta-analysis, the conventional DerSimonian-Laird (DL) estimator typically underestimates the between-trial variance. Alternative variance estimators have been proposed to address this bias. This study aims to empirically compare statistical inferences from Random-Effects Model meta-analyses on the basis of the DL estimator and four alternative estimators, as well as distributional assumptions (normal distribution and t-distribution) about the pooled intervention effect. We evaluated the discrepancies of p-values, 95% confidence intervals (CIs) in statistically significant meta-analyses, and the degree (percentage) of statistical heterogeneity (e.g. I(2)) across 920 Cochrane primary outcome meta-analyses. In total, 414 of the 920 meta-analyses were statistically significant with the DL meta-analysis, and 506 were not. Compared with the DL estimator, the four alternative estimators yielded p-values and CIs that could be interpreted as discordant in up to 11.6% or 6% of the included meta-analyses pending whether a normal distribution or a t-distribution of the intervention effect estimates were assumed. Large discrepancies were observed for the measures of degree of heterogeneity when comparing DL with each of the four alternative estimators. Estimating the degree (percentage) of heterogeneity on the basis of less biased between-trial variance estimators seems preferable to current practice. Disclosing inferential sensitivity of p-values and CIs may also be necessary when borderline significant results have substantial impact on the conclusion. Copyright © 2012 John Wiley & Sons, Ltd.

  • estimating required information size by quantifying diversity in Random Effects Model meta analyses
    BMC Medical Research Methodology, 2009
    Co-Authors: Jorn Wetterslev, Kristian Thorlund, Jesper Brok, Christian Gluud
    Abstract:

    Background There is increasing awareness that meta-analyses require a sufficiently large information size to detect or reject an anticipated intervention effect. The required information size in a meta-analysis may be calculated from an anticipated a priori intervention effect or from an intervention effect suggested by trials with low-risk of bias.

Kristian Thorlund - One of the best experts on this subject based on the ideXlab platform.

  • comparison of statistical inferences from the dersimonian laird and alternative Random Effects Model meta analyses an empirical assessment of 920 cochrane primary outcome meta analyses
    Research Synthesis Methods, 2011
    Co-Authors: Kristian Thorlund, Jorn Wetterslev, Tahany Awad, Lehana Thabane, Christian Gluud
    Abstract:

    In Random-Effects Model meta-analysis, the conventional DerSimonian-Laird (DL) estimator typically underestimates the between-trial variance. Alternative variance estimators have been proposed to address this bias. This study aims to empirically compare statistical inferences from Random-Effects Model meta-analyses on the basis of the DL estimator and four alternative estimators, as well as distributional assumptions (normal distribution and t-distribution) about the pooled intervention effect. We evaluated the discrepancies of p-values, 95% confidence intervals (CIs) in statistically significant meta-analyses, and the degree (percentage) of statistical heterogeneity (e.g. I(2)) across 920 Cochrane primary outcome meta-analyses. In total, 414 of the 920 meta-analyses were statistically significant with the DL meta-analysis, and 506 were not. Compared with the DL estimator, the four alternative estimators yielded p-values and CIs that could be interpreted as discordant in up to 11.6% or 6% of the included meta-analyses pending whether a normal distribution or a t-distribution of the intervention effect estimates were assumed. Large discrepancies were observed for the measures of degree of heterogeneity when comparing DL with each of the four alternative estimators. Estimating the degree (percentage) of heterogeneity on the basis of less biased between-trial variance estimators seems preferable to current practice. Disclosing inferential sensitivity of p-values and CIs may also be necessary when borderline significant results have substantial impact on the conclusion. Copyright © 2012 John Wiley & Sons, Ltd.

  • estimating required information size by quantifying diversity in Random Effects Model meta analyses
    BMC Medical Research Methodology, 2009
    Co-Authors: Jorn Wetterslev, Kristian Thorlund, Jesper Brok, Christian Gluud
    Abstract:

    Background There is increasing awareness that meta-analyses require a sufficiently large information size to detect or reject an anticipated intervention effect. The required information size in a meta-analysis may be calculated from an anticipated a priori intervention effect or from an intervention effect suggested by trials with low-risk of bias.