Revisiting the relation between change and initial value: a review and evaluation by Yu-Kang Tu and Mark S. Gilthorpe, Statistics in Medicine 2006; DOI: 10.1002/sim.2538.
Explore the source record for details and available documents.
Bias↗
Biomedical subjects
Takashi Funatogawa
Publications and source records attributed to Takashi Funatogawa.
An autoregressive linear mixed effects model for the analysis of longitudinal data which show profiles approaching asymptotes.
In longitudinal data, a continuous response sometimes shows a profile approaching an asymptote. For such data, we propose a new class of models, autoregressive linear mixed effects models in which the current response is regressed on the previous response, fixed effects, and random effects. Asymptotes can shift depending on treatment groups, individuals, and so on, and can be modelled by fixed and random effects. We also propose error structures that are useful in practice. The estimation methods of linear mixed effects models can be used as long as there is no intermittent missing.
Azathioprine↗
Profile likelihood-based confidence intervals using Monte Carlo integration for population pharmacokinetic parameters.
Population pharmacokinetic (PPK) analysis usually employs nonlinear mixed effects models using first-order linearization methods. It is well known that linearization methods do not always perform well in actual situations. To avoid linearization, the Monte Carlo integration method has been proposed. Moreover, we generally utilize asymptotic confidence intervals for PPK parameters based on Fisher information. It is known that likelihood-based confidence intervals are more accurate than those from the usual asymptotic confidence intervals. We propose profile likelihood-based confidence intervals using Monte Carlo integration. We have evaluated the performance of the proposed method through a simulation study, and analyzed the erythropoietin concentration data set by the method.
Computer Simulation↗
Extended information criterion (EIC) approach for linear mixed effects models under restricted maximum likelihood (REML) estimation.
In clinical data analysis, the restricted maximum likelihood (REML) method has been commonly used for estimating variance components in the linear mixed effects model. Under the REML estimation, however, it is not straightforward to compare several linear mixed effects models with different mean and covariance structures. In particular, few approaches have been proposed for the comparison of linear mixed effects models with different mean structures under the REML estimation. We propose an approach using extended information criterion (EIC), which is a bootstrap-based extension of AIC, for comparing linear mixed effects models with different mean and covariance structures under the REML estimation. We present simulation studies and applications to two actual clinical data sets.
Biometry↗