A Discrete Survival Model with Random Effects: An Application to Time to Pregnancy

Abstract Background: Pregnancy termination commonly known as abortion is the preventable causes for the maternal mortality worldwide that largely forgotten. About 45 % of these pregnancy terminations are unsafe causing death of more than 22,000 women every year and remains major public health problems in developing countries including Ethiopia. This study was also aimed to model and investigate risk factors associated with time to pregnancy termination in Ethiopia by applying survival model considering the clustering effects.Methods: The study considered 15,683 reproductive age group women from 2016 Ethiopian Demographic and Health Survey data. Kaplan-Meier(KM) was employed to estimate the survival curve and this estimated KM survival curve estimated for different groups were tested based on log rank test. To come up with appropriate model for the time to pregnancy termination and the associated risk factors both semi-parametric and parametric survival model with no frailty effects as wells as with shared frailty effects which handles random effects were employed and compared based AIC and BIC of the fitted models.Results: The result of the study showed generalized gamma and lognormal survival models were appropriate models compared with semi-parametric and other candidate parametric models.Fitting these survival model with frailty showed the improvement of the models which was an indication for the presence of unobservable random effects in clusters. Regarding the frailty models comparison, log normal with gamma frailty model was considered as appropriate model for fitting time to pregnancy termination model in Ethiopia compared with other candidate frailty models. Furthermore, the selected frailty model result showed that age of women, ever trying to avoid pregnancy, contraceptive method use, age at first sex, total number of children ever born and place of residence were the identified risk factors for the time to pregnancy termination at 5% level of significance.Conclusions: Based on the finding of this study, starting sex at early age, residing urban areas, having lower number of children, being in married marital status group, chewing chat and do not using contraceptive methods were the risk factors that results pregnancy termination at early age that needs serious consideration to prevent the problem in Ethiopia.

Download Full-text

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

Applied Stochastic Models and Data Analysis ◽

10.1002/(sici)1099-0747(199806)14:2<153::aid-asm343>3.0.co;2-9 ◽

1998 ◽

Vol 14 (2) ◽

pp. 153-163 ◽

Cited By ~ 1

Author(s):

Thomas H. Scheike ◽

Claus T. Ekstrøm

Keyword(s):

Random Effects ◽

Survival Model

Download Full-text

Establishing a Bayesian predictive survival model adjusting for random effects

Mathematics and Computers in Simulation ◽

10.1016/j.matcom.2008.01.035 ◽

2008 ◽

Vol 78 (2-3) ◽

pp. 328-334 ◽

Cited By ~ 2

Author(s):

Alfred A. Bartolucci ◽

Sejong Bae ◽

Karan P. Singh

Keyword(s):

Random Effects ◽

Survival Model

Download Full-text

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.

Download Full-text

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.

Download Full-text