In biomedical and public health research, both repeated measures of biomarkers Y as well as times T to key clinical events are often collected for a subject. The scienti®c question is how the distribution of the responses [T, Y |X ] changes with covariates X. [T |X ] may be the focus of the estimation where Y can be used as a surrogate for T. Alternatively, T may be the time to drop-out in a study in which [Y |X ] is the target for estimation. Also, the focus of a study might be on the effects of covariates X on both T and Y or on some underlying latent variable which is thought to be manifested in the observable outcomes. In this paper, we present a general model for the joint analysis of [T, Y |X ] and apply the model to estimate [T |X ] and other related functionals by using the relevant information in both T and Y. We adopt a latent variable formulation like that of Fawcett and Thomas and use it to estimate several quantities of clinical relevance to determine the ef®cacy of a treatment in a clinical trial setting. We use a Markov chain Monte Carlo algorithm to estimate the model's parameters. We illustrate the methodology with an analysis of data from a clinical trial comparing risperidone with a placebo for the treatment of schizophrenia.
Surrogate endpoints are desirable because they typically result in smaller, faster efficacy studies compared with the ones using the clinical endpoints. Research on surrogate endpoints has received substantial attention lately, but most investigations have focused on the validity of using a single biomarker as a surrogate. Our paper studies whether the use of multiple markers can improve inferences about a treatment's effects on a clinical endpoint. We propose a joint model for a time to clinical event and for repeated measures over time on multiple biomarkers that are potential surrogates. This model extends the formulation of Xu and Zeger (2001, in press) and Fawcett and Thomas (1996, Statistics in Medicine 15, 1663-1685). We propose two complementary measures of the relative benefit of multiple surrogates as opposed to a single one. Markov chain Monte Carlo is implemented to estimate model parameters. The methodology is illustrated with an analysis of data from a schizophrenia clinical trial.
The smart water quality monitoring, regarded as the future water quality monitoring technology, catalyzes progress in the capabilities of data collection, communication, data analysis, and early warning. In this article, we survey the literature till 2014 on the enabling technologies for the Smart Water Quality Monitoring System. We explore three major subsystems, namely the data collection subsystem, the data transmission subsystem, and the data management subsystem from the view of data acquiring, data transmission, and data analysis. Specifically, for the data collection subsystem, we explore selection of water quality parameters, existing technology of online water quality monitoring, identification of the locations of sampling stations, and determination of the sampling frequencies. For the data transmission system, we explore data transmission network architecture and data communication management. For the data management subsystem, we explore water quality analysis and prediction, water quality evaluation, and water quality data storage. We also propose possible challenges and future directions for each subsystem.
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