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ZIAQ: a quantile regression method for differential expression analysis of single-cell RNA-seq data

BIOINFORMATICS 2020年第10期36卷 3124-3130页

作者： Zhang, Wenfei Wei, Ying Zhang, Donghui Xu, Ethan Y. Sanofi Dept Biostat & Programming Framingham MA 01701 USA Columbia Univ Dept Biostat New York NY 10032 USA Sanofi Translat Sci Framingham MA 01701 USA

Motivation: Single-cell RNA sequencing (scRNA-seq) has enabled the simultaneous transcriptomic profiling of individual cells under different biological conditions. scRNA-seq data have two unique challenges that can affect the sensitivity and specificity of single-cell differential expression analysis: a large proportion of expressed genes with zero or low read counts ('dropout' events) and multimodal data distributions. Results: We have developed a zero-inflation-adjusted quantile (ZIAQ) algorithm, which is the first method to account for both dropout rates and complex scRNA-seq data distributions in the same model. ZIAQ demonstrates superior performance over several existing methods on simulated scRNA-seq datasets by finding more differentially expressed genes. When ZIAQ was applied to the comparison of neoplastic and non-neoplastic cells from a human glioblastoma dataset, the ranking of biologically relevant genes and pathways showed clear improvement over existing methods.

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Fast FSR Variable Selection with Applications to Clinical Trials

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BIOMETRICS 2009年第3期65卷 692-700页

作者： Boos, Dennis D. Stefanski, Leonard A. Wu, Yujun N Carolina State Univ Dept Stat Raleigh NC 27695 USA Sanofis Aventis Inc Dept Biostat & Programming Bridgewater NJ 08807 USA

A new version of the false selection rate variable selection method of Wu, Boos, and Stefanski (2007, Journal of the American Statistical Association 102, 235-243) is developed that requires no simulation. This version allows the tuning parameter in forward selection to be estimated simply by hand calculation from a summary table of output even for situations where the number of explanatory variables is larger than the sample size. Because of the computational simplicity, the method can be used in permutation tests and inside bagging loops for improved prediction. Illustration is provided in clinical trials for linear regression, logistic regression, and Cox proportional hazards regression.

关键词： Bagging False discovery rate False selection rate Forward selection LASSO Model error Model selection Regression

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Rejoinder to "on the alternative hypotheses for the win ratio"

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BIOMETRICS 2019年第1期75卷 352-354页

作者： Luo, Xiaodong Tian, Hong Mohanty, Surya Tsai, Wei Yann Sanofi US Dept Biostat & Programming Res & Dev Bridgewater NJ 08807 USA Janssen Res & Dev Raritan NJ 08869 USA Columbia Univ Dept Biostat New York NY 10032 USA

The article discusses about the potential use of the win ratio, focusing on the null hypothesis, expanding of the corresponding alternative hypothesis and the testing of null hypothesis against alternative hypothesis. It talks about the marginal hazards of hospitalization, the win probabilities, event time distribution, defining interpretable treatment that effect measures and eliminating the dependency on censoring distribution.

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Unified Inference for Sparse and Dense Longitudinal Data in Time-varying Coefficient Models

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SCANDINAVIAN JOURNAL OF STATISTICS 2017年第1期44卷 268-284页

作者： Chen, Yixin Yao, Weixin Sanofi Genzyme Dept Biostat & Programming Cambridge MA USA Univ Calif Riverside Dept Stat Riverside CA 92521 USA

Time-varying coefficient models are widely used in longitudinal data analysis. These models allow the effects of predictors on response to vary over time. In this article, we consider a mixed-effects time-varying coefficient model to account for the within subject correlation for longitudinal data. We show that when kernel smoothing is used to estimate the smooth functions in time-varying coefficient models for sparse or dense longitudinal data, the asymptotic results of these two situations are essentially different. Therefore, a subjective choice between the sparse and dense cases might lead to erroneous conclusions for statistical inference. In order to solve this problem, we establish a unified self-normalized central limit theorem, based on which a unified inference is proposed without deciding whether the data are sparse or dense. The effectiveness of the proposed unified inference is demonstrated through a simulation study and an analysis of Baltimore MACS data.

关键词： kernel smoothing longitudinal data self-normalization time-varying coefficient models

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Design and monitoring of survival trials in complex scenarios

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STATISTICS IN MEDICINE 2019年第2期38卷 192-209页

作者： Luo, Xiaodong Mao, Xuezhou Chen, Xun Qiu, Junshan Bai, Steven Quan, Hui Sanofi US Dept Biostat & Programming Res & Dev Bridgewater NJ 08807 USA FDA CDER Off Translat Sci Off BiostatDB1 Silver Spring MD USA

This paper proposes an approach to design and monitor survival trials accounting for complex scenarios such as delayed treatment effect, treatment dilution, and treatment crossover. These scenarios often lead to non-proportional hazards, making study design and monitoring more difficult. We demonstrate that, with event times following piecewise exponential distributions, the log-rank statistic as well as its variance-covariance structure can be easily computed, which greatly simplifies study design and monitoring. As the number of pieces in the exponential distributions can be arbitrary, this approach can handle a wide range of scenarios. Three hypothetical examples are used to demonstrate its potential use.

关键词： delayed treatment effect intent-to-treat non-uniform accrual piecewise exponential survival trials treatment crossover treatment dilution

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Evaluation of local treatment effect by borrowing information from similar countries in multi-regional clinical trials

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STATISTICS IN MEDICINE 2016年第5期35卷 671-684页

作者： Guo, Hua Chen, Joshua Quan, Hui Allergan Pharmaceut Inc Dept Stat Sci Jersey City NJ 07311 USA Sanofi Pasteur Biostat Swiftwater PA 18370 USA Sanofi Biostat & Programming Bridgewater NJ 08807 USA

One key objective of a multi-regional clinical trial (MRCT) is to quantify country-specific treatment effects to support local registration. Naive estimate of the treatment effects based on country-specific subsets of data are inefficient because of relatively sparse country-specific sample sizes, even in large MRCTs, and subject to many limitations. The recently developed shrinkage estimate approach improves efficiency by incorporating data from other countries but treating all other countries equally, thus ignoring similarity to the country of interest. Ideally, for the estimation of treatment effect in a country of interest, it is more efficient to use all relevant data such as that from countries that are similar to the one of interest. We propose a tree-based approach to incorporate similarity, with branches or forks used to represent the relationship of treatment effects between countries. Similarity in certain characteristics among countries in either continuous scale or categorical scale is used to build the tree. By allowing local treatment effects for countries to be random effects and follow a Gaussian process along the tree, we demonstrate that countries sharing the same internal parent nodes are more correlated and a country borrows more information from the neighbors sharing the same parent node. We illustrated this statistical framework using a MRCT with a continuous endpoint. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： multi-regional clinical trial hierarchical clustering Brownian diffusion process tree random effects

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Analysis of a binary composite endpoint with missing data in components

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STATISTICS IN MEDICINE 2007年第26期26卷 4703-4718页

作者： Quan, Hui Zhang, Daowen Zhang, Ji Devlamynck, Laure Sanofi Aventis Biostat & Programming Bridgewater NJ 08807 USA N Carolina State Univ Dept Stat Raleigh NC 27695 USA

Composite endpoints are often used in clinical trials in order to increase the overall event rates, reduce the sizes of the trials and achieve desired power. For example, in a trial to study the effect of a treatment on the prevention of venous thromboembolic events after a major orthopaedic surgery of the lower limbs, the primary endpoint is usually a composite endpoint consisting of any deep vein thrombosis identified by venography of lower limbs, symptomatic and well-documented non-fatal pulmonary embolism, and death from all causes. Just as any endpoints, missing data can occur in the components of the composite endpoint. If a patient has missing data on some of the components but not all the components, this patient may not have complete data but partial data for the composite endpoint. To be consistent with the intention-to-treat principle, the patient should not be discarded from the analysis. In this research, we propose an approach for the analysis of a composite endpoint with missing data in components. The main idea is to first derive the probabilities of all possible study outcomes based on the appropriate model and then to construct the overall rate for the composite endpoint. Simulations are conducted to compare the approach with several naive methods. A data example is used to illustrate the application of the approach. Copyright (C) 2007 John Wiley & Sons, Ltd.

关键词： EM algorithm event rate missing at random maximum likelihood estimate

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DESIGN AND SAMPLE SIZE CONSIDERATIONS FOR SIMULTANEOUS GLOBAL DRUG DEVELOPMENT PROGRAM

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2012年第5期22卷 1060-1073页

作者： Huang, Qin Chen, Gang Yuan, Zhilong Lan, K. K. Gordon SFDA Ctr Drug Evaluat Off Biostat Beijing 100038 Peoples R China Johnson & Johnson Biostat & Programming Raritan NJ USA Johnson & Johnson Quantitat Decis Strategies Raritan NJ USA

Due to the potential impact of ethnic factors on clinical outcomes, the global registration of a new treatment is challenging. China and Japan often require local trials in addition to a multiregional clinical trial (MRCT) to support the efficacy and safety claim of the treatment. The impact of ethnic factors on the treatment effect has been intensively investigated and discussed from different perspectives. However, most current methods are focusing on the assessment of the consistency or similarity of the treatment effect between different ethnic groups in exploratory nature. In this article, we propose a new method for the design and sample size consideration for a simultaneous global drug development program (SGDDP) using weighted z-tests. In the proposed method, to test the efficacy of a new treatment for the targeted ethnic (TE) group, a weighted test that combines the information collected from both the TE group and the nontargeted ethnic (NTE) group is used. The influence of ethnic factors and local medical practice on the treatment effect is accounted for by down-weighting the information collected from NTE group in the combined test statistic. This design controls rigorously the overall false positive rate for the program at a given level. The sample sizes needed for the TE group in an SGDDP for three most commonly used efficacy endpoints, continuous, binary, and time-to-event, are then calculated.

关键词： Adaptive design Bridging Drug development Ethnic group Regulatory registration Weighted Z-test

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Considerations on Testing Secondary Endpoints in Group Sequential Design

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2017年第4期9卷 333-337页

作者： Li, Xiaoming Wulfsohn, Michael S. Koch, Gary G. Atara Biotherapeut Biostat & Programming Westlake Village CA 91361 USA Gilead Sci Inc Biostat 353 Lakeside Dr Foster City CA 94404 USA Univ N Carolina Biostat Chapel Hill NC USA

In the fixed sample size design, when the null hypothesis for the primary endpoint is rejected at trial completion, secondary endpoints sometimes are tested at the full alpha level in a prespecified hierarchical order, and this strategy strongly controls the overall Type I error rate. However, in a group sequential setting, this hierarchical testing strategy may not control the overall Type I error rate for the secondary endpoints in the strong sense. Thus, when there is one interim analysis, there are proposals for an alpha spending function for testing secondary endpoints. Motivated by a Phase 3 oncology trial, we explored whether there is a less stringent approach to control the Type I error rate for testing one or more secondary endpoints in a prespecified order for the settings where there are more than one interim analysis.

关键词： Group sequential Hierarchical testing Interim analysis Primary endpoint Secondary endpoint Type I error

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Evaluation of alternative confidence intervals to address non-inferioritythrough the stratified difference between proportions

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PHARMACEUTICAL STATISTICS 2021年第1期20卷 146-162页

作者： Sui, Jiatong Jiao, Jenny Sun, Yijun Liu, Jeen Bastero, Rowena Koch, Gary Univ Rochester Biostat Rochester NY 14627 USA Allergan Plc Stat Sci & Programming Dublin Ireland Astex Pharmaceut Inc Biostat Pleasanton CA USA Univ N Carolina Dept Biostat Chapel Hill NC 27515 USA

The confidence interval (CI) for the difference between two proportions has been an important and active research topic, especially in the context of non-inferiority hypothesis testing. Issues concerning the Type 1 error rate, power, coverage rate and aberrations have been extensively studied for non-stratified cases. However, stratified confidence intervals are frequently used in non-inferiority trials and similar settings. In this paper, several methods for stratified confidence intervals for the difference between two proportions, including existing methods and novel extensions from unstratified CIs, are evaluated across different scenarios. When sparsity across the strata is not a concern, adding imputed observations to the stratification analysis can strengthen Type-1 error control without substantial loss of power. When sparseness of data is a concern, most of the evaluated methods fail to control Type-1 error;the modified stratifiedt-test CI is an exception. We recommend the modified stratifiedt-test CI as the most useful and flexible method across the respective scenarios;the modified stratified Wald CI may be useful in settings where sparsity is unlikely. These findings substantially contribute to the application of stratified CIs for non-inferiority testing of differences between two proportions.

关键词： aberration non-inferiority sparsity stratified difference between two proportions stratifiedt-test

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