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On power and sample size of the ANOVA-type rank test

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2017年第4期46卷 3224-3241页

作者： Fan, Chunpeng Zhang, Donghui Sanofi US Inc Dept Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

When using nonparametric methods to analyze factorial designs with repeated measures, the ANOVA-type rank test has gained popularity due to its robustness and appropriate type I error control. This article proposes power and sample size calculation formulas under two scenarios where the nonparametric regression coefficients are known or they are unknown but a pilot study is available. When a pilot study is available, the formulas do not need any assumption on the underlying population distributions. Simulation results confirm the accuracy of the proposed methods. An STZ rat excisional wound study is used to demonstrate the application of the methods.

关键词： ANOVA-type statistic ATS Small sample Wald-type rank test Wald-type statistic WTS

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Estimating the Interaction Index in Drug Combination Experiments

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2014年第2期6卷 144-153页

作者： Fan, Chunpeng Zhang, Donghui Sanofi US Inc Dept Biostat & Programming Bridgewater NJ 08807 USA

Studies of interactions among biologically active agents have become increasingly important in many branches of biomedical research. Based on the Loewe additivity model, which is one of the best models to define drug interactions, synergy occurs when the interaction index is less than one, and antagonism occurs when it is greater than one. When a ray design experiment is conducted to assess drug synergy, two common issues may arise: the calculation of the interaction indexes together with their confidence intervals and how to design the experiment to achieve more accurate estimations of the interaction index. This article addresses these two issues. In terms of the calculation, we compare the joint fitting mechanism which combines all rays into one estimating equation and the separate fitting which fits each ray separately and conducts further calculations to get the inference, and weights are considered in the comparison. We derive that both fitting mechanisms give identical point estimates of the interaction indexes and simulation studies show that the weighted joint fitting and the separate fitting work similarly well in terms of the model convergence and the coverage proportion of the confidence intervals. Simulation studies also suggest that to reduce the bias in the estimation of the interaction indexes, one should increase sample sizes in all rays by adding more doses or more replicates in each dose, and increasing only the sample size in the rays of pure drugs may even enlarge the bias. A two-drug, seven-ray design is used to illustrate the application of the methods.

关键词： Confidence interval Drug interaction Ray design Synergy

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Bias in the Wagner-Nelson estimate of the fraction of drug absorbed

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PHARMACEUTICAL RESEARCH 2002年第4期19卷 470-476页

作者： Wang, YB Nedelman, J Novartis Pharmaceut Coporat Biostat & Stat Programming E Hanover NJ 07936 USA

Purpose. To examine and quantify bias in the Wagner-Nelson estimate of the fraction of drug absorbed resulting from the estimation error of the elimination rate constant (k), measurement error of the drug concentration, and the truncation error in the area under the curve. Methods. Bias in the Wagner-Nelson estimate was derived as a function of post-dosing time (t), k, ratio of absorption rate constant to k (r), and the coefficient of variation for estimates of k (CVk), or CVc for the observed concentration, by assuming a one-compartment model and using an independent estimate of k. The derived functions were used for evaluating the bias with r = 0.5, 3, or 6;k = 0.1 or 0.2;CVc = 0.2 or 0.4;and CVk =0.2 or 0.4;for t = 0 to 30 or 60. Results. Estimation error of k resulted in an upward bias in the Wagner-Nelson estimate that could lead to the estimate of the fraction absorbed being greater than unity. The bias resulting from the estimation error of k inflates the fraction of absorption vs. time profiles mainly in the early post-dosing period. The magnitude of the bias in the Wagner-Nelson estimate resulting from estimation error of k was mainly determined by CVk. The bias in the Wagner-Nelson estimate resulting from to estimation error in k can be dramatically reduced by use of the mean of several independent estimates of k, as in studies for development of an in vivo-in vitro correlation. The truncation error in the area under the curve can introduce a negative bias in the Wagner-Nelson estimate. This can partially offset the bias resulting from estimation error of k in the early post-dosing period. Measurement error of concentration does not introduce bias in the Wagner-Nelson estimate. Conclusions. Estimation error of k results in an upward bias in the Wagner-Nelson estimate, mainly in the early drug absorption phase. The truncation error in AUC can result in a downward bias, which may partially offset the upward bias due to estimation error of k in the early abso

关键词： Wagner-Nelson fraction absorbed absorption estimation error bias

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Letter to the Editor: Revisit the Combination Validation Test of Tsong et al. for Thorough QT/QTc Clinical Trials

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2010年第3期20卷 683-687页

作者： Sun, Guowen (Gordon) Quan, Hui Sanofi Aventis Biostat & Programming Bridgewater NJ 08807 USA

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关键词： Editors letter aluminium Clinical Trial Proof testing

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A pseudo likelihood approach to analyze rate difference of binary response data in longitudinal factorial studies

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2018年第7期47卷 2169-2183页

作者： Fan, Chunpeng Sanofi US Inc Dept Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

Although Fan showed that the mixed-effects model for repeated measures (MMRM) is appropriate to analyze complete longitudinal binary data in terms of the rate difference, they focused on using the generalized estimating equations (GEE) to make statistical inference. The current article emphasizes validity of the MMRM when the normal-distribution-based pseudo likelihood approach is used to make inference for complete longitudinal binary data. For incomplete longitudinal binary data with missing at random missing mechanism, however, the MMRM, using either the GEE or the normal-distribution-based pseudo likelihood inferential procedure, gives biased results in general and should not be used for analysis.

关键词： Bivariate binary data MAR MCAR Missing at random Missing completely at random Trivariate binary data

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Rank repeated measures analysis of covariance

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第3期46卷 1158-1183页

作者： Fan, Chunpeng Zhang, Donghui Sanofi US Inc Dept Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

When analyzing a response variable at the presence of both factors and covariates, with potentially correlated responses and violated assumptions of the normal residual or the linear relationship between the response and the covariates, rank-based tests can be an option for inferential procedures instead of the parametric repeated measures analysis of covariance (ANCOVA) models. This article derives a rank-based method for multi-way ANCOVA models with correlated responses. The generalized estimating equations (GEE) technique is employed to construct the proposed rank tests. Asymptotic properties of the proposed tests are derived. Simulation studies confirmed the performance of the proposed tests.

关键词： ANCOVA ANOVA-type test GEE generalized estimating equations longitudinal data Wald-type test 62G08

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Testing factor-covariate interaction in rank repeated-measures analysis of covariance models

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2018年第11期47卷 2760-2778页

作者： Fan, Chunpeng Zhang, Donghui Sanofi US Inc Dept Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

This article derives the asymptotic properties of rank-based tests for the covariate effects in rank repeated-measures analysis of covariance (ANCOVA) models (Fan and Zhang 2017) employing generalized estimating equation (GEE) techniques. One interested application of the proposed tests is to check the validity of the assumption of homogeneous covariate effects in different levels of the factors. Performance of the proposed tests has been confirmed by simulation studies and illustrated using the famous seizure count data. While the article mainly focuses on interaction tests, the scope of the proposed tests includes testing any contrast of the covariate effect such as the null of no overall covariate effect.

关键词： ANCOVA ANOVA-type test GEE Generalized estimating equations Longitudinal data Wald-type test

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Empirical shrinkage estimator for consistency assessment of treatment effects in multi-regional clinical trials

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STATISTICS IN MEDICINE 2013年第10期32卷 1691-1706页

作者： Quan, Hui Li, Mingyu Shih, Weichung Joe Ouyang, Soo Peter Chen, Joshua Zhang, Ji Zhao, Peng-Liang Sanofi Biostat & Programming Bridgewater NJ 08807 USA Celgene Corp Biostat & Programming Basking Ridge NJ 07920 USA UMDNJ Sch Publ Hlth Dept Biostat Piscataway NJ 08854 USA Merck Res Labs Rahway NJ 07065 USA

Multi-regional clinical trials have been widely used for efficient global new drug developments. Both a fixed-effect model and a random-effect model can be used for trial design and data analysis of a multi-regional clinical trial. In this paper, we first compare these two models in terms of the required sample size, type I error rate control, and the interpretability of trial results. We then apply the empirical shrinkage estimation approach based on the random-effect model to two criteria of consistency assessment of treatment effects across regions. As demonstrated in our computations, compared with the sample estimator, the shrinkage estimator of the treatment effect of an individual region borrowing information from the other regions is much closer to the estimator of the overall treatment effect, has smaller variability, and therefore provides much higher probability for demonstrating consistency. We use a multinational trial example with time to event endpoint to illustrate the application of the method. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： sample size type I error rate random-effect model weighted estimator

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A Note on Sequential Monitoring of Select-the-Winner Design

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Journal of Biopharmaceutical Statistics 2015年第3期25卷 408-416页

作者： Lan, K. K. Gordon Chen, Fei Zhong, Bob Shun, Zhenming Janssen Res & Dev LLC Model Based Drug Dev Raritan NJ 08869 USA Janssen Res & Dev LLC Biostat & Programming Raritan NJ 08869 USA Sanofi Biostat & Programming Bridgewater MA USA

Consider a trial comparing two treatments or doses A and B with a control C. Based on a unblinded interim look, a winner W between A and B will be chosen, and future patients will be randomized to W and C and compared at the end of a study. The naive test statistic Z under this setting follows an approximate normal distribution, as shown by Lan et al. (2006) and Shun et al. (2008). Results of these two articles apply only to the fixed sample size design. With simple modifications, this manuscript extends the previous works to the group sequential setting.

关键词： Alpha spending Sample size and power Brownian motion process Simulations

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Benefit-Risk Assessments in Dose-Finding Trials

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2010年第2期2卷 153-164页

作者： Quan, Hui Wu, Yujun Ren, Haobo Seko, Noriko Bizet, Florence Koch, Gary Sanofi Aventis Biostat & Programming Bridgewater NJ 08807 USA Regeneron Pharmaceut Inc Biostat & Data Management Bridgewater NJ 08807 USA Sanofi Aventis Biostat & Programming Shinjuku Ku Tokyo 1631488 Japan Sanofi Aventis Biostat & Programming F-92165 Antony France Univ N Carolina Chapel Hill NC 27599 USA

The objective of dose-finding trials is to identify the doses that are sufficiently effective and safe for either future late-phase trials or the ultimate patients. Most methodologies for analyzing data from Phase II dose-finding trials mainly focus on finding the effective doses or minimum effective dose through hypothesis testing, while Phase I studies focus on finding the maximum tolerated dose. In this article, we focus on benefit-risk assessment through both hypothesis testing and point estimation. For purposes of efficiency, monotone dose-response relationships will be assumed for the safety parameter. However, the monotone assumption cannot be made directly for the benefit-risk measure. This consideration can limit the use of downward or upward sequential procedures for multiplicity adjustment for benefit-risk assessment. Several different approaches will be discussed and evaluated. Numerical and data examples will be used to illustrate the properties of these approaches.

关键词： Confidence interval Monotone assumption Multiplicity Restricted estimate

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