Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.

Parametric accelerated failure time models with random effects and an application to kidney transplant survival.

Abstract: Accelerated failure time models with a shared random component are described, and are used to evaluate the effect of explanatory factors and different transplant centres on survival times following kidney transplantation. Different combinations of the distribution of the random effects and baseline hazard function are considered and the fit of such models to the transplant data is critically assessed. A mixture model that combines short- and long-term components of a hazard function is then developed, which provides a more flexible model for the hazard function. The model can incorporate different explanatory variables and random effects in each component. The model is straightforward to fit using standard statistical software, and is shown to be a good fit to the transplant data.

Random-effects models, for longitudinal data using Gibbs sampling.

Abstract: Analysis of longitudinal studies is often complicated through differences amongst individuals in the number and spacing of observations. Laird and Ware (1982, Biometrics 38, 963-974) proposed a linear random-effects model to deal with this problem. We propose a generalisation of this model to accommodate multiple random effects, and show how Gibbs sampling can be used to estimate it. We illustrate the methodology with an analysis of long-term response to hepatitis B vaccination, and demonstrate that the methodology can be easily and effectively extended to deal with censoring in the dependent variable.

Frailty Models For Multiple Event Times

Abstract: In some clinical, epidemiologic and animal studies multiple events, possibly of different types, may occur to the same experimental unit at different times. Examples of such data include times to tumor detection, times from remission to relapse into an acute disease phase, and times to discontinuation of an experimental medication. Methods for the statistical analysis of such data need to account for heterogeneity between subjects. This can be achieved by incorporation of additional unobserved random effects into standard survival models. We concentrate on models including frailties — unobserved random proportionality factors applied to the time-dependent intensity function. In this paper we survey some such models, exhibit connections with extensions of the standard Andersen-Gill (1982) model for multiple event times that are reminiscent of the classical results of Greenwood and Yule (1920) on “accident — proneness”, and discuss methods of inference about the frailty distribution and regression parameters. The methods are illustrated by application to some animal tumor data of Gail, Santner and Brown (1980) and to data from a recently completed large multicenter clinical trial.

Crossed and Nested Mixed-Effects Approaches for Enhanced Model Development and Removal of the Ergodic Assumption in Empirical Ground-Motion Models

Abstract: Limitations in the size of strong‐motion databases that are used for the development of empirical ground‐motion models has necessitated the use of the ergodic assumption. Several recent efforts, using different databases from around the world, have been made to estimate the single‐station standard deviation of spectral accelerations. The computed estimates have been found to be very stable globally, despite the various researchers using quite different approaches. This paper demonstrates that the multistage procedures that have been adopted by previous researchers can be replaced by the use of more elaborate mixed‐effects regression analyses. Additionally, the traditional use of additive random effects to capture source, path, and site effects is shown to have conceptual shortcomings that are addressed through the use of a more complex treatment of mixed‐effects models. A model for the residual variance (within‐event single‐station variance) is presented using these advanced approaches.