An overview of group sequential methods in longitudinal clinical trials

During the last decade, several papers have been published on group sequential methods in general and on sequential longitudinal clinical trials in particular. This paper gives an overview of the proposed methods, emphasizing longitudinal clinical trials. Furthermore, it tries to answer some practical questions that may arise during the conduct of interim analyses in longitudinal trials. Simulations have been carried out to obtain insight in these practical considerations.     

Investigation of LSPR Coupling Effects toward the Rational Design of CsxWO3–δ Based Solar NIR Filtering Coatings

The optical range of localized surface plasmon resonance (LSPR) is extended into the infrared region, thanks to the development of highly doped semiconductor nanocrystals. Particularly, the near-infrared (NIR) range holds a significant interest in managing solar radiation. However, practical applications necessitate the arrangement of particles, which is known to possibly impact their optical properties through LSPR coupling effects. How such coupling modifies the LSPR response in semiconductor hosts remains largely unexplored. In this study, a protocol for producing composite coatings composed of cesium-doped tungsten bronze nanocrystals embedded in a silica matrix is presented. Achieving individual dispersion of nanocrystals is made possible through careful selection of a surface polyglycerol ligand exchange. This allows to tune the interparticle distance by adjusting the nanocrystal volume fraction in the composite. The findings demonstrate that LSPR coupling effects significantly influence the LSPR intensity of nanocrystals in the composite when the nanocrystal-to-nanocrystal distance matches their size. Beyond elucidating the LSPR coupling effect, this study provides insights into the potential use of Cs-HTB nanocrystals for solar control applications. Through the optimization of morphology and film structure, remarkable selectivity is obtained in terms of maintaining good transparency in the visible range while achieving high absorption in the NIR.     

The detection of residual serial correlation in linear mixed models

Diggle (1988) described how the empirical semi-variogram of ordinary least squares residuals can be used to suggest an appropriate serial correlation structure in stationary linear mixed models. In this paper, this approach is extended to non-stationary models which include random effects other than intercepts, and will be applied to prostate cancer data, taken from the Baltimore Longitudinal Study of Aging. A simulation study demonstrates the effectiveness of this extended variogram for improving the covariance structure of the linear mixed model used to describe the prostate data.     

Generalized linear mixed model with a penalized Gaussian mixture as a random effects distribution

Generalized linear mixed models are popular for regressing a discrete response when there is clustering, e.g. in longitudinal studies or in hierarchical data structures. It is standard to assume that the random effects have a normal distribution. Recently, it has been examined whether wrongly assuming a normal distribution for the random effects is important for the estimation of the fixed effects parameters. While it has been shown that misspecifying the distribution of the random effects has a minor effect in the context of linear mixed models, the conclusion for generalized mixed models is less clear. Some studies report a minor impact, while others report that the assumption of normality really matters especially when the variance of the random effect is relatively high. Since it is unclear whether the normality assumption is truly satisfied in practice, it is important that generalized mixed models are available which relax the normality assumption. A replacement of the normal distribution with a mixture of Gaussian distributions specified on a grid whereby only the weights of the mixture components are estimated using a penalized approach ensuring a smooth distribution for the random effects is proposed. The parameters of the model are estimated in a Bayesian context using MCMC techniques. The usefulness of the approach is illustrated on two longitudinal studies using R-functions.     

An application of maximum likelihood and generalized estimating equations to the analysis of ordinal data from a longitudinal study with cases missing at random

Data are analysed from a longitudinal psychiatric study in which there are no dropouts that do not occur completely at random. A marginal proportional odds model is fitted that relates the response (severity of side effects) to various covariates. Two methods of estimation are used: generalized estimating equations (GEE) and maximum likelihood (ML). Both the complete set of data and the data from only those subjects completing the study are analysed. For the completers-only data, the GEE and ML analyses produce very similar results. These results differ considerably from those obtained from the analyses of the full data set. There are also marked differences between the results obtained from the GEE and ML analysis of the full data set. The occurrence of such differences is consistent with the presence of a non-completely-random dropout process and it can be concluded in this example that both the analyses of the completers only and the GEE analysis of the full data set produce misleading conclusions about the relationships between the response and covariates.     

Theoretical study of acousto-optical coherence tomography using random phase jumps on ultrasound and light

Acousto-optical coherence tomography (AOCT) is a variant of acousto-optic imaging (also called ultrasonic modulation imaging) that makes it possible to get the z resolution with acoustic and optic continuous wave beams. We describe here theoretically the AOCT effect, and we show that the acousto-optic "tagged photons" remain coherent if they are generated within a specific z region of the sample. We quantify the z selectivity for both the "tagged photon" field and for the Lesaffre et al. [Opt. Express 17, 18211 (2009)] photorefractive signal.     

Drainage of Sloping Lands with Variable Recharge: Analytical Formulas and Model Development

Semianalytical transient equations for shallow subsurface transverse drainage systems installed in sloping lands are developed. They provide a general relationship between drain flow rates, water table elevations, and recharge rates. This relationship demonstrates that, depending on the recharge intensity, several drain flow rates can be observed at a given water table elevation. The recharge contribution is shown to depend on a water table shape factor and to decrease when the water table is low or the slope is steep. For very steep slopes, the recharge intensity no longer influences the drain flow rate. These equations can be used to confirm previous results obtained in steady-state conditions and to determine precisely under which conditions slope needs to be considered in drainage design. They have been incorporated into the field drainage model SIDRA, which simulates hourly values of water table elevations and drain flow rates. The model predictions are compared with the predictions of a steady-state equation and a numerical model, which solves the Boussinesq equation (SLOP model).     

Tree-based versus distance-based key recognition in musical audio

A tree-based method for the recognition of the tonal center or key in a musical audio signal is presented. Time-varying key feature vectors of 264 synthesized sounds are extracted from an auditory-based pitch model and converted into character strings using PCA-analysis and classification trees. The results are compared with distance-based methods. The characteristics of the new tonality analysis tool are illustrated on various examples. The potential of this method as a building stone in a music retrieval system is discussed.