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Guest Editors' Note on the Special Issue Real-World Evidence

JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2022年第1期32卷 1-3页

作者： Lin, Junjing Li, Qian Takeda Pharmaceut Stat & Quantitat Sci Cambridge MA 02139 USA Stat & Data Corp Dept Biostat & Programming Tempe AZ USA

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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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Increased risk of urinary tract infection and pyelonephritis under concomitant use of sodium-dependent glucose cotransporter 2 inhibitors with antidiabetic, antidyslipidemic, and antihypertensive drugs: An observational study

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FUNDAMENTAL & CLINICAL PHARMACOLOGY 2022年第6期36卷 1106-1114页

作者： Tada, Keisuke Gosho, Masahiko Sanofi KK Biostat & Programming Tokyo Opera City Tower Tokyo Japan Univ Tsukuba Grad Sch Comprehens Human Sci Ibaraki Japan Univ Tsukuba Fac Med Dept Biostat Ibaraki Japan

Urinary tract infection (UTI) and pyelonephritis cause urosepsis, which can become life threating. Sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors, a class of oral anti-diabetic drugs, may increase the risk of UTI and pyelonephritis, as observed from their mechanism of action. Patients with diabetes receiving SGLT2 inhibitors are often concomitantly administered other antidiabetic, antidyslipidemic, or antihypertensive drugs. To determine if drug-drug interactions (DDIs) between SGLT2 inhibitors and these medications increased the risk of UTI and pyelonephritis, we analyzed the Food and Drug Administration Adverse Event Reporting System (FAERS) database. We applied Noren's and Gosho's methods for detecting DDIs. FAERS contains records describing 14 526 398 patient characteristics, 54 290 663 drug properties, and 47 252 416 reactions/events. We found 23 drug combinations that could induce UTI and pyelonephritis, such as SGLT2 inhibitors administered with dipeptidyl peptidase-4 inhibitors, thiazolidinediones, glinides, statins, angiotensin II receptor blockers, and calcium channel blockers. Combination therapy with the drugs detected in our analyses would show potential interactions that could result in UTI and pyelonephritis in patients with diabetes mellitus. However, owing to various limitations, these results must be confirmed by additional analyses.

关键词： Bayesian confidence propagation neural network method drug-drug interaction reporting odds ratio signal detection spontaneous reporting system

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Some Multiplicity Adjustment Procedures for Clinical Trials with Sequential Design and Multiple Endpoints

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2024年第1期16卷 104-115页

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

This article proposes some new multiplicity adjustment procedures for clinical trials with multiple endpoints and multiple interim analyses. The proposed sequential procedures, adapting the popular multiple comparison procedures for fixed time-point design and using a-spending for each endpoint, are shown to strongly control the family-wise Type-1 error rate and provide powerful yet versatile multiplicity adjustment solutions for monitoring multiple endpoints via interim analyses.

关键词： Generalized Simes inequality Group-sequential p-values Multiplicity q-values early submission alpha-propagation

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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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Evaluating Treatment Efficacy by Combining Multiple Measures in Clinical Trial Applications

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PHARMACEUTICAL MEDICINE 2023年第1期37卷 7-16页

作者： Al Masud, Abdullah Weerahandi, Samaradasa Yu, Ching-Ray Sanofi US Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA X Tech LLC Edison NJ USA Pfizer Inc New York NY USA

A variety of clinical and laboratory measures can be used in clinical trials to assess the benefit of a new treatment over the standard of care. Data from clinical studies are often analyzed by combining individual outcomes into one primary outcome. That primary outcome is then referred to as a composite endpoint or a combined endpoint. We propose an analysis on the composite endpoint with Gehan's (1965) ranking approach where each subject in the treatment group is compared with each subject in the control group in a pair-wise manner. Our approach reduces computational time and complexity to construct a subject-level pairwise composite score. We develop a statistical testing procedure for the analysis of composite endpoints when using the hierarchical scores. In this article, we propose two tests (a parametric test and a non-parametric bootstrap procedure) for evaluating the effect of treatment. The proposed parametric test has an asymptotic F-distribution based on standard statistical assumptions. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods and to compare them with an existing method. We illustrate the methods using publicly available data from two clinical studies.

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A confirmatory basket design considering non-inferiority and superiority testing

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JOURNAL OF BIOPHARMACEUTICAL STATISTICS 2024年第2期34卷 205-221页

作者： Zhang, Yaohua Chu, Chenghao Beckman, Robert A. Gao, Lei Laird, Glen Yi, Bingming Vertex Pharmaceut Inc Dept Biometr Boston MA 02210 USA Georgetown Univ Lombardi Comprehens Canc Ctr Dept Oncol Med Ctr Washington DC USA Georgetown Univ Lombardi Comprehens Canc Ctr Dept Biostat Bioinformat & Biomath Med Ctr Washington DC USA Georgetown Univ Innovat Ctr Biomed Informat Med Ctr Washington DC USA Moderna Dept Biostat & Programming Cambridge MA USA

For multiple rare diseases as defined by a common biomarker signature, or a disease with multiple disease subtypes of low frequency, it is often possible to provide confirmatory evidence for these disease or subtypes (baskets) as a combined group. A novel drug, as a second generation, may have marginal improvement in efficacy overall but superior efficacy in some baskets. In this situation, it is appealing to test hypotheses of both non-inferiority overall and superiority on certain baskets. The challenge is designing a confirmatory study efficient to address multiple questions in one trial. A two-stage adaptive design is proposed to test the non-inferiority hypothesis at the interim stage, followed by pruning and pooling before testing a superiority hypothesis at the final stage. Such a design enables an efficient and novel registration pathway, including an early claim of non-inferiority followed by a potential label extension with superiority on certain baskets and an improved benefit-risk profile demonstrated by longer term efficacy and safety data. Operating characteristics of this design are examined by simulation studies, and its appealing features make it ready for use in a confirmatory setting, especially in emerging markets, where both the need and the possibility for efficient use of resources may be the greatest.

关键词： Basket design non-inferiority two-stage design

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Estimating the treatment effect for adherers using multiple imputation

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PHARMACEUTICAL STATISTICS 2022年第3期21卷 525-534页

作者： Luo, Junxiang Ruberg, Stephen J. Qu, Yongming Moderna Inc Dept Biostat & Programming Cambridge MA USA Analytix Thinking LCC Indianapolis IN USA Eli Lilly & Co Dept Stat Data & Analyt Indianapolis IN 46285 USA

Randomized controlled trials are considered the gold standard to evaluate the treatment effect (estimand) for efficacy and safety. According to the recent International Council on Harmonization (ICH)-E9 addendum (R1), intercurrent events (ICEs) need to be considered when defining an estimand, and principal stratum is one of the five strategies to handle ICEs. Qu et al. (2020, Statistics in Biopharmaceutical Research 12:1-18) proposed estimators for the adherer average causal effect (AdACE) for estimating the treatment difference for those who adhere to one or both treatments based on the causal-inference framework, and demonstrated the consistency of those estimators;however, this method requires complex custom programming related to high-dimensional numeric integrations. In this article, we implemented the AdACE estimators using multiple imputation (MI) and constructed confidence intervals (CIs) through bootstrapping. A simulation study showed that the MI-based estimators provided consistent estimators with the nominal coverage probabilities of CIs for the treatment difference for the adherent populations of interest. As an illustrative example, the new method was applied to data from a real clinical trial comparing two types of basal insulin for patients with type 1 diabetes.

关键词： adherer average causal effect counterfactual effect principal stratum tripartite estimands

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Information fraction estimation: Strategies for a phase 3 non-inferiority maximum duration design with time to event outcome

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PHARMACEUTICAL STATISTICS 2023年第6期22卷 1031-1045页

作者： Dang, Ha M. Krailo, Mark D. Alonzo, Todd A. Mack, Wendy J. Kairalla, John A. Univ Southern Calif Dept Populat & Publ Hlth Sci Los Angeles CA USA Childrens Oncol Grp Monrovia CA USA Johnson & Johnson Med Devices Co Biostat Programming & Data Management 29B Technol Dr Irvine CA 92618 USA Univ Florida Coll Med Dept Biostat Gainesville FL USA Univ Florida Coll Publ Hlth & Hlth Profess Dept Biostat Gainesville FL USA

There is considerable debate surrounding the choice of methods to estimate information fraction for futility monitoring in a randomized non-inferiority maximum duration trial. This question was motivated by a pediatric oncology study that aimed to establish non-inferiority for two primary outcomes. While non-inferiority was determined for one outcome, the futility monitoring of the other outcome failed to stop the trial early, despite accumulating evidence of inferiority. For a one-sided trial design for which the intervention is inferior to the standard therapy, futility monitoring should provide the opportunity to terminate the trial early. Our research focuses on the Total Control Only (TCO) method, which is defined as a ratio of observed events to total events exclusively within the standard treatment regimen. We investigate its properties in stopping a trial early in favor of inferiority. Simulation results comparing the TCO method with alternative methods, one based on the assumption of an inferior treatment effect (TH0), and the other based on a specified hypothesis of a non-inferior treatment effect (THA), were provided under various pediatric oncology trial design settings. The TCO method is the only method that provides unbiased information fraction estimates regardless of the hypothesis assumptions and exhibits a good power and a comparable type I error rate at each interim analysis compared to other methods. Although none of the methods is uniformly superior on all criteria, the TCO method possesses favorable characteristics, making it a compelling choice for estimating the information fraction when the aim is to reduce cancer treatment-related adverse outcomes.

关键词： futility analysis group sequential analysis information fraction interim monitoring analysis maximum duration clinical trials non-inferiority design pediatric oncology survival outcome trials

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A simulation-free group sequential design with max-combo tests in the presence of non-proportional hazards

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PHARMACEUTICAL STATISTICS 2021年第4期20卷 879-897页

作者： Wang, Lili Luo, Xiaodong Zheng, Cheng Univ Michigan Dept Biostat Ann Arbor MI 48109 USA Sanofi US Dept Biostat & Programming Res & Dev Bridgewater NJ 08807 USA

Non-proportional hazards (NPH) have been observed in many immuno-oncology clinical trials. Weighted log-rank tests (WLRT) with suitable weights can be used to improve the power of detecting the difference between survival curves in the presence of NPH. However, it is not easy to choose a proper WLRT in practice. A versatile max-combo test was proposed to achieve the balance of robustness and efficiency, and has received increasing attention recently. Survival trials often warrant interim analyses due to their high cost and long durations. The integration and implementation of max-combo tests in interim analyses often require extensive simulation studies. In this report, we propose a simulation-free approach for group sequential designs with the max-combo test in survival trials. The simulation results support that the proposed method can successfully control the type I error rate and offer excellent accuracy and flexibility in estimating sample sizes, with light computation burden. Notably, our method displays strong robustness towards various model misspecifications and has been implemented in an R package.

关键词： group sequential design interim analysis max‐ combo non‐ proportional hazard weighted log‐ rank test

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