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Historical Control Data Borrowing for Noninferiority Assessment

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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Variable Selection in High-Dimensional Logistic Regression Models Using a Whitening Approach

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2025年第2期22卷 800-807页

作者： Zhu, Wencan Levy-Leduc, Celine Ternes, Nils Univ Paris Saclay UMR MIA Paris AgroParisTech INRAE F-91190 Gif Sur Yvette France Sanofi R&D Dept Biostat & Programming Bridgewater NJ 08807 USA Univ Paris Cite CNRS Lab Probabil Stat Modelisat LPSM F-75006 Paris France Sanofi R&D Dept Biostat & Programming Bridgewater NJ 08807 USA

In bioinformatics, the rapid development of sequencing technology has enabled us to collect an increasing amount of omics data. Classification based on omics data is one of the central problems in biomedical research. However, omics data usually has a limited sample size but high feature dimensions, and it is assumed that only a few features (biomarkers) are active, i.e. informative to discriminate between different categories. Identifying active biomarkers for classification has therefore become fundamental for omics data analysis. Focusing on binary classification, we propose an innovative feature selection method aiming at dealing with the high correlations between the biomarkers. Our method, WLogit, consists in whitening the design matrix to remove the correlations between biomarkers, then using a penalized criterion adapted to the logistic regression model to select features. The results from numerical experiments suggest that WLogit can identify almost all active biomarkers even in the cases where the biomarkers are highly correlated, while the other methods fail, which consequently leads to higher classification accuracy. The performance of WLogit is also evaluated on two publicly available datasets, and the obtained classifier outperformed other methods in terms of prediction accuracy. Our method is implemented in the WLogit R package available from the Comprehensive R Archive Network (CRAN).

关键词： Biomarkers Correlation Feature extraction Biological system modeling Bioinformatics Logistic regression Integrated circuit modeling Diseases Computational modeling Computational biology Feature selection highly correlated predictors binary classification regularized approches

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POD-BIN: A PROBABILITY OF DECISION BAYESIAN INTERVAL DESIGN FOR TIME-TO-EVENT DOSE-FINDING TRIALS WITH MULTIPLE TOXICITY GRADES

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ANNALS OF APPLIED STATISTICS 2025年第1期19卷 147-168页

作者： Liu, Meizi Lin, Ji Mi, Gu Lorenzato, Christelle Chen, Xun Ji, Yuan Takeda Stat & Quantitat Sci Cambridge MA 02142 USA Sanofi Dept Biostat & Programming Bridgewater NJ 08807 USA Univ Chicago Dept Publ Hlth Sci Chicago IL USA

We introduce a Bayesian framework centered on the "probability of decision" for designing dose-finding trials. The proposed PoD-BIN design evaluates the posterior predictive probabilities of up-and-down decisions. In PoDBIN, multiple grades of toxicity, categorized as mild toxicity (MT) and doselimiting toxicity (DLT), are simultaneously modeled, with the primary outcome being time-to-toxicity for both MT and DLT. This approach allows the enrollment of new patients while previously enrolled patients are still being monitored for toxicity, potentially reducing the trial duration. The Bayesian decision rules in PoD-BIN employ the probability of decisions to balance the trade-off between accelerating the trial and the risk of exposing patients to excessively toxic doses. Through numerical examples, we illustrate the trade-off between speed and safety of PoD-BIN and compare it with existing designs. PoD-BIN demonstrates the ability to control the frequency of risky decisions while simultaneously shortening trial duration in simulations.

关键词： Dose limiting toxicity equivalence interval latent probit regression moderate toxici-ties phase I trial time to toxicity

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Regional consistency assessment in multiregional clinical trials

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2024年第6期34卷 973-985页

作者： Li, Gang Quan, Hui Wang, Yining Eisai Inc Global Med Affairs Nutley NJ USA Sanofi Biostat & Programming Bridgewater NJ USA Janssen Res & Dev Stat Programming Raritan NJ USA Eisai Inc Global Med Affairs 200 Metro Blvd Nutley NJ 07110 USA

Multiregional clinical trials (MRCTs) have become a favored strategy for new drug development. The accurate evaluation of treatment effects across different regions is crucial for interpreting the results of MRCTs. Consistency between regional and overall results ensures the extrapolability of the overall conclusions to individual regions. While numerous statistical methods have been proposed for consistency assessment, a notable proportion necessitate a substantial escalation in sample size, particularly in scenarios involving more than four regions within MRCTs. This, paradoxically, undermines the fundamental intent of MRCTs. In addition, standardized statistical criteria for concluding consistency are yet to be established. In this paper, we develop further consistency assessment approaches in the framework of two multivariate likelihood ratio test-based methods, namely mLRTa and mLRTb, wherein consistency is cast as the alternative and null hypotheses. Notably, our exploration unveils that qualitative methods such as the funnel approach and PMDA methods are special instances of mLRTa. Furthermore, our work underscores that these three qualitative methodologies roughly share the same level of assurance probability (AP). Intriguingly, when the number of regions in an MRCT surpasses five, even when the overall sample size guarantees a power of 90% or more and the true treatment effects remain uniform across regions, the AP remains below the 70% mark. Drawing from our meticulous examination of operational attributes, we recommend mLRTa with positive treatment effects in all regions in the alternative hypothesis with significance level 0.5 or mLRTb with all regional treatment effects being equal in the null and significance level of 0.2.

关键词： Multiregional clinical trial (MRCT) consistency assurance probability Multivariate likelihood ratio test (mLRT)

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Assessments of Conditional and Unconditional Type I Error Probabilities for Bayesian Hypothesis Testing with Historical Data Borrowing

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STATISTICS IN BIOSCIENCES 2022年第1期14卷 139-157页

作者： Quan, Hui Chen, Xiaofei Chen, Xun Luo, Xiaodong Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

Applications of Bayesian designs allow the borrowing of the strength of historical information and become more and more attractive in new drug developments. Nonetheless, according to the FDA guidance issued in 2020, Bayesian designs are classified as complex innovative designs that have rarely been used to provide substantial evidence of effectiveness in new drug applications. Moreover, as the historical data have already been observed and fixed, a question which arises is whether we should treat the Bayesian analysis as a conditional or unconditional analysis. Basically, it is essential to understand the frequentist operating characteristics of a Bayesian design either theoretically or through simulation in order to appropriately assess the right type I error probability and apply it to a clinical trial. In this research, we use a relatively simple setting of a normal distribution for the study endpoint to illustrate and compare the conditional and unconditional Bayesian analysis. Both scenarios of borrowing historical information of treatment effect and historical control data are considered. The thinking is applicable to the other settings or endpoints through the asymptotic normality of the distributions for the estimators of either the within or between treatment effects. Simulations are conducted to evaluate the characteristics of the methods.

关键词： Historical data borrowing Design prior Posterior probability Conditional power

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Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso

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BMC BIOINFORMATICS 2023年第1期24卷 1-20页

作者： Zhu, Wencan Levy-Leduc, Celine Ternes, Nils Univ Paris Saclay AgroParisTech INRAE UMR MIA Paris Saclay F-91120 Palaiseau France Sanofi R&D Biostat & Programming Dept F-91380 Chilly Mazarin France

In clinical trials, identification of prognostic and predictive biomarkers has became essential to precision medicine. Prognostic biomarkers can be useful for the prevention of the occurrence of the disease, and predictive biomarkers can be used to identify patients with potential benefit from the treatment. Previous researches were mainly focused on clinical characteristics, and the use of genomic data in such an area is hardly studied. A new method is required to simultaneously select prognostic and predictive biomarkers in high dimensional genomic data where biomarkers are highly correlated. We propose a novel approach called PPLasso, that integrates prognostic and predictive effects into one statistical model. PPLasso also takes into account the correlations between biomarkers that can alter the biomarker selection accuracy. Our method consists in transforming the design matrix to remove the correlations between the biomarkers before applying the generalized Lasso. In a comprehensive numerical evaluation, we show that PPLasso outperforms the traditional Lasso and other extensions on both prognostic and predictive biomarker identification in various scenarios. Finally, our method is applied to publicly available transcriptomic and proteomic data.

关键词： Variable selection Highly correlated predictors Genomic data

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Design and monitoring of clinical trials with an interim analysis and a negative binomial endpoint

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CONTEMPORARY CLINICAL TRIALS 2024年 138卷 107467-107467页

作者： Quan, Hui Xu, Yuqing Liu, Ying Chen, Xun Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA

There are very rich publications devoted to group sequential design, adaptive design and trial monitoring for continuous, binary and time to event endpoints. Many authors also discuss fixed design, blinded sample size reestimation design and group sequential design for studies with a negative binomial outcome. Nonetheless, literature is sparse in adaptive design for a trial with a negative binomial endpoint. The features of such an endpoint in a flexible trial design setting remains inadequately understood. In this research, we seek to bridge this knowledge gap by offering a thorough examination of utilizing data components from a two-stage adaptive design for unblinded conditional power calculation and corresponding sample size re-estimation. We also provide expression for calculating the probability of meeting the futility criterion to determine the appropriate timing for the interim analysis. To evaluate the performance of the design, we conduct simulations to assess its operation characteristics. Finally, we provide a helpful and illustrative example to demonstrate the practical applications of the methods.

关键词： Conditional power Probability of success Independent increment Adaptive design Posterior distribution Decision making

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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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Two-stage stratified designs with survival outcomes and adjustment for misclassification in predictive biomarkers

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STATISTICS IN MEDICINE 2024年第10期43卷 1883-1904页

作者： Chen, Yanping Lin, Yong Lu, Shou-En Shih, Weichung J. Quan, Hui Bristol Myers Squibb Dept Biostat Berkeley Hts NJ USA Rutgers State Univ Sch Publ Hlth Biostat & Epidemiol Dept Piscataway NJ USA Rutgers Canc Inst New Jersey Biometr Div New Brunswick NJ USA Sanofi Biostat & Programming Bridgewater NJ USA Rutgers State Univ Sch Publ Hlth Biostat & Epidemiol Dept 683 Hoes Lane WestRM 214 Piscataway NJ 08854 USA

Biomarker stratified clinical trial designs are versatile tools to assess biomarker clinical utility and address its relationship with clinical endpoints. Due to imperfect assays and/or classification rules, biomarker status is prone to errors. To account for biomarker misclassification, we consider a two-stage stratified design for survival outcomes with an adjustment for misclassification in predictive biomarkers. Compared to continuous and/or binary outcomes, the test statistics for survival outcomes with an adjustment for biomarker misclassification is much more complicated and needs to take special care. We propose to use the information from the observed biomarker status strata to construct adjusted log-rank statistics for true biomarker status strata. These adjusted log-rank statistics are then used to develop sequential tests for the global (composite) hypothesis and component-wise hypothesis. We discuss the power analysis with the control of the type-I error rate by using the correlations between the adjusted log-rank statistics within and between the design stages. Our method is illustrated with examples of the recent successful development of immunotherapy in nonsmall-cell lung cancer.

关键词： adjusted log-rank statistics composite Hypothesis predictive biomarker sensitivity and specificity survival outcome

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Bayesian dose regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics

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BIOMETRICS 2022年第1期78卷 300-312页

作者： Gerard, Emma Zohar, Sarah Hoai-Thu Thai Lorenzato, Christelle Riviere, Marie-Karelle Ursino, Moreno Univ Paris Sorbonne Univ Ctr Rech Cordeliers INSERM F-75006 Paris France Sanoli R&D Oncol Biostat Biostat & Programming Dept Vitry Sur Seine France Sanofi R&D Biostat & Programming Dept Stat Methodol Grp Chilly Mazarin France Sanofi R&D Translat Dis Modeling Digital & Data Sci Chilly Mazarin France Univ Paris AP HP F CRIN Partners Platform Paris France

Phase I dose-finding trials in oncology seek to find the maximum tolerated dose of a drug under a specific schedule. Evaluating drug schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose regimen assessment method (DRtox) using pharmacokinetics/pharmacodynamics (PK/PD) to estimate the maximum tolerated dose regimen (MTD-regimen) at the end of the dose-escalation stage of a trial. We modeled the binary toxicity via a PD endpoint and estimated the dose regimen toxicity relationship through the integration of a dose regimen PD model and a PD toxicity model. For the first model, we considered nonlinear mixed-effects models, and for the second one, we proposed the following two Bayesian approaches: a logistic model and a hierarchical model. In an extensive simulation study, the DRtox outperformed traditional designs in terms of proportion of correctly selecting the MTD-regimen. Moreover, the inclusion of PK/PD information helped provide more precise estimates for the entire dose regimen toxicity curve;therefore the DRtox may recommend alternative untested regimens for expansion cohorts. The DRtox was developed to be applied at the end of the dose-escalation stage of an ongoing trial for patients with relapsed or refractory acute myeloid leukemia (NCT03594955) once all toxicity and PK/PD data are collected.

关键词： Bayesian inference dose regimen early phase oncology hierarchical model pharmacokinetics pharmacodynamics toxicity

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