A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.

Simulation-based fully Bayesian experimental design for mixed effects models

In this paper, we present fully Bayesian experimental designs for nonlinear mixed effects models, in which we develop simulation-based optimal design methods to search over both continuous and discrete design spaces. Although Bayesian inference has commonly been performed on nonlinear mixed effects models, there is a lack of research into performing Bayesian optimal design for nonlinear mixed effects models that require searches to be performed over several design variables. This is likely due to the fact that it is much more computationally intensive to perform optimal experimental design for nonlinear mixed effects models than it is to perform inference in the Bayesian framework. In this paper, the design problem is to determine the optimal number of subjects and samples per subject, as well as the (near) optimal urine sampling times for a population pharmacokinetic study in horses, so that the population pharmacokinetic parameters can be precisely estimated, subject to cost constraints. The optimal sampling strategies, in terms of the number of subjects and the number of samples per subject, were found to be substantially different between the examples considered in this work, which highlights the fact that the designs are rather problem-dependent and require optimisation using the methods presented in this paper.

The utility and application of mixed-effects models in second language research

Second language acquisition researchers often face particular challenges when attempting to generalize study findings to the wider learner population. For example, language learners constitute a heterogeneous group, and it is not always clear how a study’s findings may generalize to other individuals who may differ in terms of language background and proficiency, among many other factors. In this paper, we provide an overview of how mixed-effects models can be used to help overcome these and other issues in the field of second language acquisition. We provide an overview of the benefits of mixed-effects models and a practical example of how mixed-effects analyses can be conducted. Mixed-effects models provide second language researchers with a powerful statistical tool in the analysis of a variety of different types of data.

Improved testing inference in mixed linear models

Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Often, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test, and also to a test obtained from a modified profile likelihood function. Our results generalize those in [Zucker, D.M., Lieberman, O., Manor, O., 2000. Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood. Journal of the Royal Statistical Society B, 62,827-838] by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report simulation results which show that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presented and discussed. (C) 2008 Elsevier B.V. All rights reserved.

Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty

Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.

A class of mixed integrable models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Parameterized Link Functions in Generalized Linear Random Effect Models: a Case Study on Breast Cancer Treatment

In non-linear random effects some attention has been very recently devoted to the analysis ofsuitable transformation of the response variables separately (Taylor 1996) or not (Oberg and Davidian 2000) from the transformations of the covariates and, as far as we know, no investigation has been carried out on the choice of link function in such models. In our study we consider the use of a random effect model when a parameterized family of links (Aranda-Ordaz 1981, Prentice 1996, Pregibon 1980, Stukel 1988 and Czado 1997) is introduced. We point out the advantages and the drawbacks associated with the choice of this data-driven kind of modeling. Difficulties in the interpretation of regression parameters, and therefore in understanding the influence of covariates, as well as problems related to loss of efficiency of estimates and overfitting, are discussed. A case study on radiotherapy usage in breast cancer treatment is discussed.

Cognitive-behavioural therapy has no effect on disease activity but improves quality of life in subgroups of patients with inflammatory bowel disease: a pilot randomised controlled trial.

BACKGROUND: Studies have demonstrated usefulness of cognitive-behavioural therapy (CBT) in managing distress in inflammatory bowel disease (IBD); however, few have focused on IBD course. The present trial aimed to investigate whether adding CBT to standard treatment prolongs remission in IBD in comparison to standard therapy alone. METHODS: A 2-arm parallel pragmatic randomised controlled trial (+CBT - standard care plus either face-to-face (F2F) or online CBT over 10 weeks versus standard care alone (SC)) was conducted with adult patients in remission. IBD remission at 12 months since baseline was the primary outcome measure while the secondary outcome measures were mental health status and quality of life (QoL). Linear mixed-effect models were used to compare groups on outcome variables while controlling for baseline. RESULTS: Participants were 174 patients with IBD (90 +CBT, 84 SC). There was no difference in remission rates between groups, with similar numbers flaring at 12 months. Groups did not differ in anxiety, depression or coping at 6 or 12 months (p >0.05). When only participants classified as 'in need' (young, high baseline IBD activity, recently diagnosed; poor mental health) were examined in the post-hoc analysis (n = 74, 34 CBT and 40 controls), CBT significantly improved mental QoL (p = .034, d = .56) at 6 months. Online CBT group had a higher score on Precontemplation than the F2F group, which is consistent with less developed coping with IBD in the cCBT group (p = .045). CONCLUSIONS: Future studies should direct psychological interventions to patients 'in need' and attempt to recruit larger samples to compensate for significant attrition when using online CBT. TRIAL REGISTRATION: The protocol was registered on 21/10/2009 with the Australian New Zealand Clinical Trials Registry (ID: ACTRN12609000913279).

Decreasing menopausal symptoms in women undertaking a web-based multi-modal lifestyle intervention: The Women's Wellness Program

Menopausal transition can be challenging for many women. This study tested the effectiveness of an intervention delivered in different modes in decreasing menopausal symptoms in midlife women. The Women's Wellness Program (WWP) intervention was delivered to 225 Australian women aged between 40 and 65 years through three modes (i.e., on-line independent, face-to-face with nurse consultations, and on-line with virtual nurse consultations). All women in the study were provided with a 12-week Program Book outlining healthy lifestyle behaviors while women in the consultation groups were supported by a registered nurse who provide tailored health education and assisted with individual goal setting for exercise, healthy eating, smoking and alcohol consumption. Pre- and post-intervention data were collected on menopausal symptoms (Greene Climacteric Scale), health related quality of life (SF12), and modifiable lifestyle factors. Linear mixed-effect models showed an average 0.87 and 1.23 point reduction in anxiety (p < 0.01) and depression scores (p < 0.01) over time in all groups. Results also demonstrated reduced vasomotor symptoms (β = −0.19, SE = 0.10, p = 0.04) and sexual dysfunction (β = −0.17, SE = 0.06, p < 0.01) in all participants though women in the face-to-face group generally reported greater reductions than women in the other groups. This lifestyle intervention embedded within a wellness framework has the potential to reduce menopausal symptoms and improve quality of life in midlife women thus potentially enhancing health and well-being in women as they age. Of course, study replication is needed to confirm the intervention effects.

Efeitos adversos da poluição atmosférica em crianças e adolescentes devido a queimadas na Amazônia: uma abordagem de modelos mistos em estudos de painel

Esta tese investiga os efeitos agudos da poluição atmosférica no pico de fluxo expiratório (PFE) de escolares com idades entre 6 e 15 anos, residentes em municípios da Amazônia Brasileira. O primeiro artigo avaliou os efeitos do material particulado fino (PM2,5) no PFE de 309 escolares do município de Alta Floresta, Mato Grosso (MT), durante a estação seca de 2006. Modelos de efeitos mistos foram estimados para toda a amostra e estratificados por turno escolar e presença de sintomas de asma. O segundo artigo expõe as estratégias utilizadas para a determinação da função de variância do erro aleatório dos modelos de efeitos mistos. O terceiro artigo analisa os dados do estudo de painel com 234 escolares, realizado na estação seca de 2008 em Tangará da Serra, MT. Avaliou-se os efeitos lineares e com defasagem distribuída (PDLM) do material particulado inalável (PM10), do PM2,5 e do Black Carbon (BC) no PFE de todos os escolares e estratificados por grupos de idade. Nos três artigos, os modelos de efeitos mistos foram ajustados por tendência temporal, temperatura, umidade e características individuais. Os modelos também consideraram o ajuste da autocorrelação residual e da função de variância do erro aleatório. Quanto às exposições, foram avaliados os efeitos das exposições de 5hs, 6hs, 12hs e 24hs, no dia corrente, com defasagens de 1 a 5 dias e das médias móveis de 2 e 3 dias. No que se refere aos resultados de Alta Floresta, os modelos para todas as crianças indicaram reduções no PFE variando de 0,26 l/min (IC95%: 0,49; 0,04) a 0,38 l/min (IC95%: 0,71; 0,04), para cada aumento de 10g/m3 no PM2,5. Não foram observados efeitos significativos da poluição no grupo das crianças asmáticas. A exposição de 24hs apresentou efeito significativo no grupo de alunos da tarde e no grupo dos não asmáticos. A exposição de 0hs a 5:30hs foi significativa tanto para os alunos da manhã quanto para a tarde. Em Tangará da Serra, os resultados mostraram reduções significativas do PFE para aumentos de 10 unidades do poluente, principalmente para as defasagens de 3, 4 e 5 dias. Para o PM10, as reduções variaram de 0,15 (IC95%: 0,29; 0,01) a 0,25 l/min (IC95%: 0,40 ; 0,10). Para o PM2,5, as reduções estiveram entre 0,46 l/min (IC95%: 0,86 to 0,06 ) e 0,54 l/min (IC95%: 0,95; 0,14). E no BC, a redução foi de aproximadamente 0,014 l/min. Em relação ao PDLM, efeitos mais importantes foram observados nos modelos baseados na exposição do dia corrente até 5 dias passados. O efeito global foi significativo apenas para o PM10, com redução do PFE de 0,31 l/min (IC95%: 0,56; 0,05). Esta abordagem também indicou efeitos defasados significativos para todos os poluentes. Por fim, o estudo apontou as crianças de 6 a 8 anos como grupo mais sensível aos efeitos da poluição. Os achados da tese sugerem que a poluição atmosférica decorrente da queima de biomassa está associada a redução do PFE de crianças e adolescentes com idades entre 6 e 15 anos, residentes na Amazônia Brasileira.