The current guidelines, ICH E14, for the evaluation of non-antiarrhythmic compounds require a 'thorough' QT study (TQT) conducted during clinical development (ICH Guidance for Industry E14, 2005). Owing to the regulatory choice of margin (10 ms), the TQT studies must be conducted to rigorous standards to ensure that variability is minimized. Some of the key sources of variation can be controlled by use of randomization, crossover design, standardization of electrocardiogram (ECG) recording conditions and collection of replicate ECGs at each time point. However, one of the key factors in these studies is the baseline measurement, which if not controlled and consistent across studies could lead to significant misinterpretation. In this article, we examine three types of baseline methods widely used in the TQT studies to derive a change from baseline in QTc (time-matched, time-averaged and pre-dose-averaged baseline). We discuss the impact of the baseline values on the guidance-recommended 'largest time-matched' analyses. Using simulation we have shown the impact of these baseline approaches on the type I error and power for both crossover and parallel group designs. In this article, we show that the power of study decreases as the number of time points tested in TQT study increases. A time-matched baseline method is recommended by several authors (Drug Saf. 2005; 28(2):115-125, Health Canada guidance document: guide for the analysis and review of QT/QTc interval data, 2006) due to the existence of the circadian rhythm in QT. However, the impact of the time-matched baseline method on statistical inference and sample size should be considered carefully during the design of TQT study. The time-averaged baseline had the highest power in comparison with other baseline approaches.

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Due to the existence of unfavorable factors such as turbid water quality and target occlusion, it is difficult to obtain valid data of target features. Due to the repeated calculation of similar data, the real-time performance of the algorithm is poor. In view of the above problems, this paper proposes a multi-AUV collaborative target recognition method based on transfer-reinforcement learning. The features of the target information which is collected by multi-AUV are fused based on wavelet transformation and affine invariance. The similarity of features is calculated by Mahalanobis distance and the learning model is selected autonomously based on the similarity threshold. Based on the Q-learning reinforcement learning model, the target information under the interference environment is trained intensively, and the effective features are extracted and stored in the source domain, which can reduce the impact of the environmental interference on the target recognition. The feature transfer learning model based on deep confidence network transfers the feature data of the source domain to the target domain, reducing the repeated calculation of similar data, and then ensuring the real-time performance of the algorithm. Simulation experiments are conducted in the SUN dataset under five underwater environments (turbid water, target occlusion, insufficient light, complex background, and overlapping targets), and the results demonstrate that the proposed model achieves better performance.

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This article develops a latent model and likelihood-based inference to detect temporal clustering of events. The model mimics typical processes generating the observed data. We apply model selection techniques to determine the number of clusters, and develop likelihood inference and a Monte Carlo expectation-maximization algorithm to estimate model parameters, detect clusters, and identify cluster locations. Our method differs from the classical scan statistic in that we can simultaneously detect multiple clusters of varying sizes. We illustrate the methodology with two real data applications and evaluate its efficiency through simulation studies. For the typical data-generating process, our methodology is more efficient than a competing procedure that relies on least squares.

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The time-varying ocean currents and the delay of underwater acoustic communication have caused the uncertainty of single autonomous underwater vehicle (AUV) tracking target and the inconsistency of multi-AUV coordination, which make it difficult for multiple AUVs to form a hunting alliance. To solve the above problems, this article proposes the multi-AUV consistent collaborative hunting method based on generative adversarial network (GAN). Firstly, the three-dimensional (3D) kinematic model of AUV is established for the underwater 3D environment. Secondly, combined with the Laplacian matrix, the topology of the hunting alliance in the ideal environment is established, and the control rate of AUV is calculated. Finally, using the GAN network model, the control relationship after environmental interference is used as the input of the generative model. The control rate in the ideal environment is used as the comparison object of the discriminative model. Using the iterative training of GAN to generate a control rate that adapts to the current interference environment and combining multi-AUV topological hunting model to achieve successful hunting of noncooperative target, the experimental results show that the algorithm reduces the average hunting time to 62.53 s and the success rate of hunting is increased to 84.69%, which is 1.17% higher than the particle swarm optimization-constant modulus algorithm (PSO-CMA) algorithm.

Different from the use of intention-to-treat (ITT) analysis for efficacy evaluation, many pharmaceutical companies currently use treatment emergent (TE) analysis for adverse event (AE) safety analysis. In the TE analysis, study period and AEs occurring after a pre-specified post-treatment window will not be included. One consideration for using the TE AE analysis is that including substantial off-drug period and events in the analysis may dilute the power for detecting safety signals especially if after discontinuation residual treatment effect diminishes quickly. We perform quantitative analyses to compare the unbiasedness and power of the ITT and TE AE analyses under several different settings and metrics (difference in rates and relative risk). Results show that unbiasedness and power are not always in the same direction. The choice of an approach for a particular trial should depend on the focus of the analysis. A data example is used to illustrate these points.