Multi-peril frequency credibility premium via shared random effects

The development of academic English proficiency and the time it takes to reclassify to fluent English proficient status are key issues in English learner (EL) policy. This article develops a shared random effects model (SREM) to estimate English proficiency development and time to reclassification simultaneously, treating student-specific random effects as latent covariates in the time to reclassification model. Using data from a large Arizona school district, the SREM resulted in predictions of time to reclassification that were 93% accurate compared to 85% accuracy from a conventional discrete-time hazard model used in prior literature. The findings suggest that information about English-language development is critical for accurately predicting the grade an EL will reclassify.

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Combination of longitudinal biomarkers in predicting binary events

Biostatistics ◽

10.1093/biostatistics/kxu020 ◽

2014 ◽

Vol 15 (4) ◽

pp. 706-718 ◽

Cited By ~ 15

Author(s):

Danping Liu ◽

Paul S. Albert

Keyword(s):

Random Effects ◽

Mixed Model ◽

Linear Mixed Model ◽

Disease Risk ◽

Likelihood Ratio Statistic ◽

Disease Status ◽

Analytical Form ◽

Mixed Effects ◽

Mixed Effects Model ◽

Shared Random Effects

Abstract In disease screening, the combination of multiple biomarkers often substantially improves the diagnostic accuracy over a single marker. This is particularly true for longitudinal biomarkers where individual trajectory may improve the diagnosis. We propose a pattern mixture model (PMM) framework to predict a binary disease status from a longitudinal sequence of biomarkers. The marker distribution given the disease status is estimated from a linear mixed effects model. A likelihood ratio statistic is computed as the combination rule, which is optimal in the sense of the maximum receiver operating characteristic (ROC) curve under the correctly specified mixed effects model. The individual disease risk score is then estimated by Bayes’ theorem, and we derive the analytical form of the 95% confidence interval. We show that this PMM is an approximation to the shared random effects (SRE) model proposed by Albert (2012. A linear mixed model for predicting a binary event from longitudinal data under random effects mis-specification. Statistics in Medicine31(2), 143–154). Further, with extensive simulation studies, we found that the PMM is more robust than the SRE model under wide classes of models. This new PPM approach for combining biomarkers is motivated by and applied to a fetal growth study, where the interest is in predicting macrosomia using longitudinal ultrasound measurements.

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Modeling Longitudinal Microbiome Compositional Data: A Two-Part Linear Mixed Model with Shared Random Effects

Statistics in Biosciences ◽

10.1007/s12561-021-09302-w ◽

2021 ◽

Author(s):

Yongli Han ◽

Courtney Baker ◽

Emily Vogtmann ◽

Xing Hua ◽

Jianxin Shi ◽

...

Keyword(s):

Random Effects ◽

Mixed Model ◽

Linear Mixed Model ◽

Compositional Data ◽

Shared Random Effects

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Proportion of Individuals With Past-Year Suicidal Ideation Who Attempted Suicide Over the Past 10 Years in the United States, and the Influence of Age and Sex

Crisis ◽

10.1027/0227-5910/a000690 ◽

2020 ◽

pp. 1-5

Author(s):

Shannon Lange ◽

Courtney Bagge ◽

Charlotte Probst ◽

Jürgen Rehm

Keyword(s):

United States ◽

Suicidal Ideation ◽

Random Effects ◽

Attempted Suicide ◽

Suicide Attempts ◽

The United States ◽

Individual Level ◽

The Past ◽

Meta Analyses ◽

Age Category

Abstract. Background: In recent years, the rate of death by suicide has been increasing disproportionately among females and young adults in the United States. Presumably this trend has been mirrored by the proportion of individuals with suicidal ideation who attempted suicide. Aim: We aimed to investigate whether the proportion of individuals in the United States with suicidal ideation who attempted suicide differed by age and/or sex, and whether this proportion has increased over time. Method: Individual-level data from the National Survey on Drug Use and Health (NSDUH), 2008–2017, were used to estimate the year-, age category-, and sex-specific proportion of individuals with past-year suicidal ideation who attempted suicide. We then determined whether this proportion differed by age category, sex, and across years using random-effects meta-regression. Overall, age category- and sex-specific proportions across survey years were estimated using random-effects meta-analyses. Results: Although the proportion was found to be significantly higher among females and those aged 18–25 years, it had not significantly increased over the past 10 years. Limitations: Data were self-reported and restricted to past-year suicidal ideation and suicide attempts. Conclusion: The increase in the death by suicide rate in the United States over the past 10 years was not mirrored by the proportion of individuals with past-year suicidal ideation who attempted suicide during this period.

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Positional Estimation Within a Latent Space Model for Networks

Methodology ◽

10.1027/1614-2241.2.1.24 ◽

2006 ◽

Vol 2 (1) ◽

pp. 24-33 ◽

Cited By ~ 17

Author(s):

Susan Shortreed ◽

Mark S. Handcock ◽

Peter Hoff

Keyword(s):

Random Effects ◽

Network Data ◽

Random Effects Models ◽

Space Model ◽

Network Analyses ◽

Recent Advances ◽

Latent Space ◽

Latent Space Model ◽

Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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