A bayesian multilevel modeling approach for data query in wireless sensor networks

In power-limited Wireless Sensor Network (WSN), it is important to reduce the communication load in order to achieve energy savings. This paper applies a novel statistic method to estimate the parameters based on the realtime data measured by local sensors. Instead of transmitting large real-time data, we proposed to transmit the small amount of dynamic parameters by exploiting both temporal and spatial correlation within and between sensor clusters. The temporal correlation is built on the level-1 Bayesian model at each sensor to predict local readings. Each local sensor transmits their local parameters learned from historical measurement data to their cluster heads which account for the spatial correlation and summarize the regional parameters based on level-2 Bayesian model. Finally, the cluster heads transmit the regional parameters to the sink node. By utilizing this statistical method, the sink node can predict the sensor measurements within a specified period without directly communicating with local sensors. We show that this approach can dramatically reduce the amount of communication load in data query applications and achieve significant energy savings

Multiple testing of treatment-effect-modifying biomarkers in a randomized clinical trial with a survival endpoint

The recent revolution in genomics and the advent of targeted therapies have increased interest in biomarker-defined subgroups of patients who respond to therapy or exhibit specific toxicities. Such biomarker-defined subgroups are also being investigated for non-targeted therapies (e.g. chemotherapy and statins). However, even when the targeting pathway has been identified, a broadly available test to identify the appropriate subgroup will rarely exist prior to the launch of the pivotal phase III trial. Our aim in this paper is to provide guidance for the analysis of a phase III clinical trial with a survival endpoint, in order to ascertain whether a therapy is more effective in the biomarker-positive patients as compared with biomarker-negative patients, when the trial is conducted on the entire population and when there are multiple candidate biomarkers. We studied treatment-by-biomarker interactions in a Weibull regression model. Different permutation procedures, using single-biomarker statistics and novel composite statistics, are proposed in order to control the family-wise error rate accounting for dependence structures among the biomarkers. A simulation study was performed to compare the operational characteristics of the permutation tests under different scenarios. The tests were applied to a phase III trial of adjuvant chemotherapy in early breast cancer, for which 10 biomarkers were measured in tumor samples from 798 patients. These permutation tests can be applied to retrospective biomarker studies and to prospective phase III trials of new drugs for which a few clues are known about the targeting pathway at the start of the trial. © 2011 John Wiley & Sons, Ltd.SCOPUS: ar.jFLWINinfo:eu-repo/semantics/publishe

Dead Heat: The 2006 Public Choice Society Election

In 2006, the Public Choice Society chose a new president using approval voting. There were five candidates, and the election was extremely close. We indicate the sources of support of the different candidates, based in part on spectral analysis, by voters who cast between one and five votes. Using preference information that was also gathered, we show that two candidates different from the approval voting winner, including the apparent Condorcet winner, might have won under different voting systems. Because most voters did not indicate their complete preference rankings, however, these differences are hardly robust, especially since the outcome was essentially a dead heat. Copyright Springer Science+Business Media B.V. 2006Public Choice Society, Approval voting, Spectral analysis, Condorcet winner, Borda count, Hare system,