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A CASE STUDY OF MODELING AND EXPOSURE-RESPONSE PREDICTION FOR COUNT DATA

JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2014年第5期24卷 1073-1090页

作者： Quan, Hui Mao, Xuezhou Wei, Lynn Wang, Lin Sanofi Biostat & Programming Bridgewater NJ 08807 USA

Even with two doses of an experimental drug in Phase III studies, with the commonly used approach for assessing treatment effects of individual doses, it may still be difficult to determine the final commercial dose. In such a scenario, with plasma concentration data collected in the studies, a modeling approach can be applied to predict treatment effects at different plasma concentration levels. Through an established relationship between plasma concentration and dose, the treatment effects of doses not studied in the Phase III studies can then be predicted. The results can further be applied to justify the final dose confirmation or selection. In this article, a Phase III program example with count data as the primary endpoint in the multiple sclerosis area is used to illustrate the application of such a technique for dose confirmation. Several models, such as the overdispersion Poisson model, negative binomial model, and recurrent event models, are considered. The negative binomial model is preferable due to better data fitting and the capability of within-treatment assessment and between-treatment comparison.

关键词： Annualized relapse rate Negative binomial Plasma concentration Recurrent event Risk ratio Trial simulation

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Sample size calculations for comparing two groups of count data

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2019年第1期29卷 115-127页

作者： Wang, Lin Fan, Chunpeng Sanofi US Inc Dept Biostat & Programming Bridgewater NJ USA

y A sample size formula for comparing two groups of count data is derived using the method of moments by matching the first and second moments of the distribution of the count data, and it does not need any further distributional assumption. Compared to sample size formulas derived using a likelihood-based approach or using simulations, the proposed sample size formula applies to count data following any distribution in addition to the negative binomial distribution. The proposed sample size formula can be used even when the study is analyzed with a likelihood-based approach. Because asymptotically, the method of moments is no more efficient than likelihood-based approaches, the proposed sample size formula can be viewed as an upper bound of the required sample size by likelihood-based approaches to start the study. Applications of the sample size formula are illustrated using an asthma study design.

关键词： Method of moments mixture of negative binomial distributions recurrent events

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Historical Control Data Borrowing for Noninferiority Assessment

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2022年第4期14卷 463-472页

作者： Quan, Hui Xu, Zhixing Cho, Meehyung Dong, Yingwen Jia, Nan Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

Noninferiority (NI) assessment is often performed to evaluate whether a test treatment can preserve certain proportion of the treatment effect of an active control compared to placebo. Two methods are usually applied for the NI assessment. One is the so-called synthesis method and the other is the two-confidence-interval method. To fully use available information, historical data of the active control may be borrowed not just for the NI margin specification but also for the actual NI assessment. In that case, the historical data will be used twice which may create some concern. Moreover, as the historical data have already been observed and fixed when the current NI trial is designed, the NI assessment with historical borrowing could be treated as a conditional analysis. In this research, we discuss the methods and the associated considerations including sample size calculation and Type I error probability control for historical data borrowing for the NI assessment. An example and simulations are used to illustrate the applications and compare the characteristics of the methods.

关键词： Data matching Noninferiority margin Power Synthesis method Two-confidence-interval method Type I error probability

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Multiple Comparisons in Microarray Data Analysis

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2010年第3期2卷 368-382页

作者： Zhang, Donghui Liu, Li Sanofi Aventis Biostat & Programming Bridgewater NJ 08807 USA

Multiplicity is a challenging statistical issue in drug discovery, and a particular example is microarray study. The traditional approach of controlling of the family-wise error rate (FWER) is conservative when the number of tests is large. A more appropriate approach is to control the false discovery rate (FDR). Since the development of the Benjamini and Hochberg (BH) FDR procedure in 1995, many modifications have been proposed aimed at relaxing the requirement for independent test statistics or improving the power of the BH FDR procedure. Comparisons of these procedures in the current literature are not comprehensive and the conclusions on performances are inconsistent. The objectives of this article are three-fold: (a) to perform a more comprehensive comparison of extant multiple testing procedures using two real microarray datasets and various simulated data sets;(b) to explore potential reasons for the inconsistencies in published simulation results;and (c) to identify suitable FDR procedures under different scenarios according to covariance structure, percent of true null hypotheses among multiple tests, and sample size.

关键词： False discovery rate Family-wise error rate Multiple testing

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Considerations of multiple imputation approaches for handling missing data in clinical trials

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CONTEMPORARY CLINICAL TRIALS 2018年 70卷 62-71页

作者： Quan, Hui Qi, Li Luo, Xiaodong Darchy, Loic Sanofi Biostat & Programming 55 Corp Dr Bridgewater MA 08807 USA

Missing data exist in all clinical trials and missing data issue is a very serious issue in terms of the interpretability of the trial results. There is no universally applicable solution for all missing data problems. Methods used for handling missing data issue depend on the circumstances particularly the assumptions on missing data mechanisms. In recent years, if the missing at random mechanism cannot be assumed, conservative approaches such as the control-based and returning to baseline multiple imputation approaches are applied for dealing with the missing data issues. In this paper, we focus on the variability in data analysis of these approaches. As demonstrated by examples, the choice of the variability can impact the conclusion of the analysis. Besides the methods for continuous endpoints, we also discuss methods for binary and time to event endpoints as well as consideration for non-inferiority assessment.

关键词： Variance estimate Likelihood-based approach Imputation model Control-based approach Returning to baseline

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Comparing Two Tests for Two Rates

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AMERICAN STATISTICIAN 2017年第3期71卷 275-281页

作者： Fan, Chunpeng Wang, Lin Wei, Lynn Sanofi US Inc Dept Biostat & Programming Bridgewater NJ 08807 USA

This article rigorously proves superiority of the proportion chi(2) test to the logistic regression Wald test in terms of power when comparing two rates, despite their asymptotic equivalence under the null hypothesis ... 详细信息

关键词： Logistic regression Parabola Proportion chi(2) test

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Applications of Bayesian analysis to proof-of-concept trial planning and decision making

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PHARMACEUTICAL STATISTICS 2020年第4期19卷 468-481页

作者： Quan, Hui Chen, Xun Lan, Yu Luo, Xiaodong Kubiak, Rene Bonnet, Nicolas Paux, Gautier Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

Decision making is a critical component of a new drug development process. Based on results from an early clinical trial such as a proof of concept trial, the sponsor can decide whether to continue, stop, or defer the development of the drug. To simplify and harmonize the decision-making process, decision criteria have been proposed in the literature. One of them is to exam the location of a confidence bar relative to the target value and lower reference value of the treatment effect. In this research, we modify an existing approach by moving some of the "stop" decision to "consider" decision so that the chance of directly terminating the development of a potentially valuable drug can be reduced. As Bayesian analysis has certain flexibilities and can borrow historical information through an inferential prior, we apply the Bayesian analysis to the trial planning and decision making. Via a design prior, we can also calculate the probabilities of various decision outcomes in relationship with the sample size and the other parameters to help the study design. An example and a series of computations are used to illustrate the applications, assess the operating characteristics, and compare the performances of different approaches.

关键词： decision making design prior historical data borrowing inferential prior posterior distribution

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SAMPLE SIZE DETERMINATION IN TWO-SIDED DISTRIBUTION-FREE TREATMENT VERSUS CONTROL MULTIPLE COMPARISONS

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2013年第6期23卷 1308-1329页

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

The problem of power and sample size determination for distribution-free multiple comparison tests of K treatments versus a control group is addressed. We define the power as the probability of correctly rejecting one specified or all K hypotheses, corresponding to the per-pair and all-pairs power, respectively. The power formulas are derived for both joint ranking and pairwise ranking mechanism for general multiple comparison problems, followed by explicit form of these formulas when the single-step, step-down, or step-up adjustments are applied. The proposed power and sample size calculation methods apply to scenarios both when the underlying distributions are known and when they are unknown but a pilot study is available. Numerical methods via quasi-Monte Carlo integration and Monte Carlo integration are assessed. Our simulation studies show the accuracy of the power and sample size calculation formulas. We recommend the Monte Carlo integration as the calculation algorithm. An example from a mouse peritoneal cavity study is used to demonstrate the application of the methods.

关键词： FWER Joint ranking Nonparametric Pairwise ranking Power

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ROBUST SMALL SAMPLE INFERENCE FOR GENERALISED ESTIMATING EQUATIONS: AN APPLICATION OF THE ANOVA-TYPE TEST

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AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS 2014年第3期56卷 237-255页

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

Asymptotically, the Wald-type test for generalised estimating equations (GEE) models can control the type I error rate at the nominal level. However in small sample studies, it may lead to inflated type I error rates. Even with currently available small sample corrections for the GEE Wald-type test, the type I error rate inflation is still serious when the tested contrast is multidimensional. This paper extends the ANOVA-type test for heteroscedastic factorial designs to GEE and shows that the proposed ANOVA-type test can also control the type I error rate at the nominal level in small sample studies while still maintaining robustness with respect to mis-specification of the working correlation matrix. Differences of inference between the Wald-type test and the proposed test are observed in a two-way repeated measures ANOVA model for a diet-induced obesity study and a two-way repeated measures logistic regression for a collagen-induced arthritis study. Simulation studies confirm that the proposed test has better control of the type I error rate than the Wald-type test in small sample repeated measures models. Additional simulation studies further show that the proposed test can even achieve larger power than the Wald-type test in some cases of the large sample repeated measures ANOVA models that were investigated.

关键词： GEE ANOVA-type statistic Wald-type statistic quadratic form

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Statistical Evaluation of Four Central Reading Processes

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2021年第1期13卷 82-90页

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

In medical and clinical studies, videos, images, or other form of information carriers may be centrally read to provide scores or measurements for medically or clinically meaningful endpoints. Since the simple one central reading process may lead to biased final reported scores, different central reading processes that aim to reduce the potential bias are used including the double central reading with adjudication, the two central reading without adjudication, and the three central reading processes. The current article compares these four central reading processes and rigorously proves three propositions under several common scenarios of interest. Analytical calculations and simulation studies were used to numerically compare these four central reading processes. Theoretical and numerical assessments suggest the use of the three central reading process if cost allows. With a reduced cost, the double central reading with adjudication process with a relatively large cutoff C should be used, while C needs to be carefully selected using appropriate numerical assessments or clinical justifications.

关键词： Adjudication Bias Conditional mean

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