@article {821, title = {Practical Marginalized Multilevel Models.}, journal = {Stat}, volume = {2}, year = {2013}, month = {2013}, abstract = {

Clustered data analysis is characterized by the need to describe both systematic variation in a mean model and cluster-dependent random variation in an association model. Marginalized multilevel models embrace the robustness and interpretations of a marginal mean model, while retaining the likelihood inference capabilities and flexible dependence structures of a conditional association model. Although there has been increasing recognition of the attractiveness of marginalized multilevel models, there has been a gap in their practical application arising from a lack of readily available estimation procedures. We extend the marginalized multilevel model to allow for nonlinear functions in both the mean and association aspects. We then formulate marginal models through conditional specifications to facilitate estimation with mixed model computational solutions already in place. We illustrate the MMM and approximate MMM approaches on a cerebrovascular deficiency crossover trial using SAS and an epidemiological study on race and visual impairment using R. Datasets, SAS and R code are included as supplemental materials.

}, issn = {0038-9986}, doi = {10.1002/sta4.22}, author = {Griswold, Michael E and Swihart, Bruce J and Caffo, Brian S and Zeger, Scott L} } @article {805, title = {Practical recommendations for population PK studies with sampling time errors.}, journal = {Eur J Clin Pharmacol}, volume = {69}, year = {2013}, month = {2013 Dec}, pages = {2055-64}, abstract = {

PURPOSE: Population pharmacokinetic (PK) data collected from routine clinical practice offers a rich source of valuable information. However, in observational population PK data, accurate time information for blood samples is often missing, resulting in measurement errors (ME) in the sampling time variable. The goal of this study was to investigate the effects on model parameters when a scheduled time is used instead of the actual blood sampling time, and to propose ME correction methods.

METHODS: Simulation studies were conducted based on two major factors: the curvature in PK profiles and the size of ME. As ME correction methods, transform both sides (TBS) models were developed with application of Box-Cox power transformation and Taylor expansion. The TBS models were compared to a conventional population PK model using simulations.

RESULTS: The most important determinant of bias due to time ME was the degree of curvature (nonlinearity) in PK profiles; the smaller the curvature around sampling times, the smaller the associated bias. The second important determinant was the magnitude of ME; the larger the ME, the larger the bias. The proposed TBS models performed better than a conventional population PK modeling when curvature and ME were substantial.

CONCLUSIONS: Time ME in sampling time can lead to bias on the parameter estimators. The following practical recommendations are provided: 1) when the curvature of PK profiles is small, conventional population PK modeling is robust to even large ME; and 2) when the curvature is moderate or large, the proposed methodology reduces bias in parameter estimates.

}, keywords = {Humans, Models, Biological, Pharmacokinetics, Research Design, Time Factors}, issn = {1432-1041}, doi = {10.1007/s00228-013-1576-7}, author = {Choi, Leena and Crainiceanu, Ciprian M and Caffo, Brian S} }