A discrete survival model with random effects and covariate-dependent selection

Author(s):  
Thomas H. Scheike ◽  
Claus T. Ekstrøm
2008 ◽  
Vol 78 (2-3) ◽  
pp. 328-334 ◽  
Author(s):  
Alfred A. Bartolucci ◽  
Sejong Bae ◽  
Karan P. Singh

Biometrics ◽  
1997 ◽  
Vol 53 (1) ◽  
pp. 318 ◽  
Author(s):  
Thomas H. Scheike ◽  
Tina Kold Jensen

Crisis ◽  
2020 ◽  
pp. 1-5
Author(s):  
Shannon Lange ◽  
Courtney Bagge ◽  
Charlotte Probst ◽  
Jürgen Rehm

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.


Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff

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.


2005 ◽  
Author(s):  
Marci E. J. Gleason ◽  
Niall Bolger ◽  
Patrick E. Shrout ◽  
Masumi Iida

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