Application of Bayesian analyses to doubly randomized delayed start, matched control designs to demonstrate disease modification.

Disease modification is a primary therapeutic aim when developing treatments for most chronic progressive diseases. The best treatments do not simply affect disease symptoms but fundamentally improve disease course by slowing, halting, or reversing disease progression. One of many challenges for establishing disease modification relates to the identification of adequate analytic tools to show differences in a disease course following intervention. Traditional approaches rely on the comparisons of slopes or noninferiority margins. However, it has proven difficult to conclusively demonstrate disease modification using such approaches. To address these challenges, we propose a novel adaptation of the delayed start study design that incorporates posterior probabilities identified by hierarchical Bayesian inference approaches to establish evidence for disease modification. Our models compare the size of treatment differences at the end of the delayed start period with those at the end of the early start period. Simulations that compare several models are provided. These include general linear models, repeated measures models, spline models, and model averaging. Our work supports the superiority of model averaging for accurately characterizing complex data that arise in real world applications. This novel approach has been applied to the design of an ongoing, doubly randomized, matched control study that aims to show disease modification in young persons with schizophrenia (the Disease Recovery Evaluation and Modification (DREaM) study). The application of this Bayesian methodology to the DREaM study highlights the value of this approach and demonstrates many practical challenges that must be addressed when implementing this methodology in a real world trial.

Assays for Qualification and Quality Stratification of Clinical Biospecimens Used in Research: A Technical Report from the ISBER Biospecimen Science Working Group.

This technical report presents quality control (QC) assays that can be performed in order to qualify clinical biospecimens that have been biobanked for use in research. Some QC assays are specific to a disease area. Some QC assays are specific to a particular downstream analytical platform. When such a qualification is not possible, QC assays are presented that can be performed to stratify clinical biospecimens according to their biomolecular quality.

Bayesian auxiliary particle filters for estimating neural tuning parameters.

A common challenge in neural engineering is to track the dynamic parameters of neural tuning functions. This work introduces the application of Bayesian auxiliary particle filters for this purpose. Based on Monte-Carlo filtering, Bayesian auxiliary particle filters use adaptive methods to model the prior densities of the state parameters being tracked. The observations used are the neural firing times, modeled here as a Poisson process, and the biological driving signal. The Bayesian auxiliary particle filter was evaluated by simultaneously tracking the three parameters of a hippocampal place cell and compared to a stochastic state point process filter. It is shown that Bayesian auxiliary particle filters are substantially more accurate and robust than alternative methods of state parameter estimation. The effects of time-averaging on parameter estimation are also evaluated.

Piecewise linear models with guaranteed closeness to the data.

This paper addresses the problem of piecewise linear approximation of point sets without any constraints on the order of data points or the number of model components (line segments). We point out two problems with the maximum likelihood estimate (MLE) that present serious drawbacks in practical applications. One is that the parametric models obtained using a classical MLE framework are not guaranteed to be close to data points. It is typically impossible, in this classical framework, to detect whether a parametric model fits the data well or not. The second problem is related to accurately choosing the optimal number of model components. We first fit a nonparametric density to the data points and use it to define a neighborhood of the data. Observations inside this neighborhood are deemed informative; those outside the neighborhood are deemed uninformative for our purpose. This provides us with a means to recognize when models fail to properly fit the data. We then obtain maximum likelihood estimates by optimizing the Kullback-Leibler Divergence (KLD) between the nonparametric data density restricted to this neighborhood and a mixture of parametric models. We prove that, under the assumption of a reasonably large sample size, the inferred model components are close to their ground-truth model component counterparts. This holds independently of the initial number of assumed model components or their associated parameters. Moreover, in the proposed approach, we are able to estimate the number of significant model components without any additional computation.

A test for measuring gustatory function.

OBJECTIVES/HYPOTHESIS: The purpose of this study was to determine the usefulness of edible taste strips for measuring human gustatory function. STUDY DESIGN: The physical properties of edible taste strips were examined to determine their potential for delivering threshold and suprathreshold amounts of taste stimuli to the oral cavity. Taste strips were then assayed by fluorescence to analyze the uniformity and distribution of bitter tastant in the strips. Finally, taste recognition thresholds for sweet taste were examined to determine whether or not taste strips could detect recognition thresholds that were equal to or better than those obtained from aqueous tests. METHODS: Edible strips were prepared from pullulan-hydroxypropyl methylcellulose solutions that were dried to a thin film. The maximal amount of a tastant that could be incorporated in a 2.54 cm2 taste strip was identified by including representative taste stimuli for each class of tastant (sweet, sour, salty, bitter, and umami) during strip formation. Distribution of the bitter tastant quinine hydrochloride in taste strips was assayed by fluorescence emission spectroscopy. The efficacy of taste strips for evaluating human gustatory function was examined by using a single series ascending method of limits protocol. Sucrose taste recognition threshold data from edible strips was then compared with results that were obtained from a standard "sip and spit" recognition threshold test. RESULTS: Edible films that formed from a pullulan-hydroxypropyl methylcellulose polymer mixture can be used to prepare clear, thin strips that have essentially no background taste and leave no physical presence after release of tastant. Edible taste strips could uniformly incorporate up to 5% of their composition as tastant. Taste recognition thresholds for sweet taste were over one order of magnitude lower with edible taste strips when compared with an aqueous taste test. CONCLUSION: Edible taste strips are a highly sensitive method for examining taste recognition thresholds in humans. This new means of presenting taste stimuli should have widespread applications for examining human taste function in the laboratory, in the clinic, or at remote locations.

Computational exploration of the activated pathways associated with DNA damage response in breast cancer.

Molecular signaling events regulate cellular activity. Cancer stimulating signals trigger cellular responses that evade the regulatory control of cell development. To understand the mechanism of signaling regulation in cancer, it is necessary to identify the activated pathways in cancer. We have developed RepairPATH, a computational algorithm that explores the activated signaling pathways in cancer. The RepairPATH integrates RepairNET, an assembled protein interaction network associated with DNA damage response, with the gene expression profiles derived from the microarray data. Based on the observation that cofunctional proteins often exhibit correlated gene expression profiles, it identifies the activated signaling pathways in cancer by systematically searching the RepairNET for proteins with significantly correlated gene expression profiles. Analyzing the gene expression profiles of breast cancer, we found distinct similarities and differences in the activated signaling pathways between the samples from the patients who developed metastases and the samples from the patients who were disease free within 5 years. The cellular pathways associated with the various DNA repair mechanisms and the cell-cycle checkpoint controls are found to be activated in both sample groups. One of the most intriguing findings is that the pathways associated with different cellular processes are functionally coordinated through BRCA1 in the disease-free sample group, whereas such functional coordination is absent in the samples from patients who developed metastases. Our analysis revealed the potential cellular pathways that regulate the signaling events in breast cancer.