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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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Implementation of tripartite estimands using adherence causal estimators under the causal inference framework

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PHARMACEUTICAL STATISTICS 2021年第1期20卷 55-67页

作者： Qu, Yongming Luo, Junxiang Ruberg, Stephen J. Eli Lilly & Co Dept Biometr Indianapolis IN 46285 USA Sanofi Biostat & Programming Bridgewater NJ USA Analytix Thinking LCC Indianapolis IN USA

Intercurrent events (ICEs) and missing values are inevitable in clinical trials of any size and duration, making it difficult to assess the treatment effect for all patients in randomized clinical trials. Defining the appropriate estimand that is relevant to the clinical research question is the first step in analyzing data. The tripartite estimands, which evaluate the treatment differences in the proportion of patients with ICEs due to adverse events, the proportion of patients with ICEs due to lack of efficacy, and the primary efficacy outcome for those who can adhere to study treatment under the causal inference framework, are of interest to many stakeholders in understanding the totality of treatment effects. In this manuscript, we discuss the details of how to estimate tripartite estimands based on a causal inference framework and how to interpret tripartite estimates through a phase 3 clinical study evaluating a basal insulin treatment for patients with type 1 diabetes.

关键词： adherence ICH E9 (R1) intercurrent events missing data principal stratification

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Integrated Data Analysis for Assessing Treatment Effect through Combining Information from All Sources

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STATISTICS IN BIOPHARMACEUTICAL RESEARCH 2017年第1期9卷 52-64页

作者： Quan, Hui Zhang, Bingzhi Chuang-Stein, Christy Jones, Byron Sanofi Biostat & Programming 55 Corp Dr Bridgewater NJ 08807 USA Chuang Stein Consulting Kalamazoo MI USA Novartis Basel Switzerland

It is critical to use a precise estimate of treatment effect when drawing conclusions, evaluating benefit/ risk, or designing a new study. Using data from all sources in an integrated data analysis/meta-analysis will help us move closer to meeting this need. Depending on the data sources and objectives, there are many approaches for integrated analyses. These include networkmeta-analysis, multivariatemeta-analysis, model-basedmeta-analysis as well as methods of borrowing historical data. In this article, we discuss these methods with details for implementation and interpretation. In addition, we consider information adaptive repeated cumulativemeta-analyses. We also discuss how to apply three approaches that take into account the variability of the overall treatment effect estimate obtained through integrated analysis to determine sample size for a new trial. Some computation and simulation results are provided.

关键词： Historical data Meta-analysis Sample size calculation Surrogate endpoint

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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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Eigengene-based linear discriminant model for tumor classification using gene expression microarray data

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BIOINFORMATICS 2006年第21期22卷 2635-2642页

作者： Shen, Ronglai Ghosh, Debashis Chinnaiyan, Arul Meng, Zhaoling Sanofi Aventis Biostat & Programming Bridgewater NJ 08807 USA Univ Michigan Dept Biostat Ann Arbor MI 48109 USA Univ Michigan Dept Pathol & Urol Ann Arbor MI 48109 USA

Motivation: The nearest shrunken centroids classifier has become a popular algorithm in tumor classification problems using gene expression microarray data. Feature selection is an embedded part of the method to select top-ranking genes based on a univariate distance statistic calculated for each gene individually. The univariate statistics summarize gene expression profiles outside of the gene co-regulation network context, leading to redundant information being included in the selection procedure. Results: We propose an Eigengene-based Linear Discriminant Analysis (ELDA) to address gene selection in a multivariate framework. The algorithm uses a modified rotated Spectral Decomposition (SpD) technique to select 'hub' genes that associate with the most important eigenvectors. Using three benchmark cancer microarray datasets, we show that ELDA selects the most characteristic genes, leading to substantially smaller classifiers than the univariate feature selection based analogues. The resulting de-correlated expression profiles make the gene-wise independence assumption more realistic and applicable for the shrunken centroids classifier and other diagonal linear discriminant type of models. Our algorithm further incorporates a misclassification cost matrix, allowing differential penalization of one type of error over another. In the breast cancer data, we show false negative prognosis can be controlled via a cost-adjusted discriminant function.

关键词： Artificial Intelligence Computer Simulation Diagnosis Computer-Assisted Discriminant Analysis Gene Expression Profiling Humans Linear Models Models Genetic Multivariate Analysis Neoplasm Proteins Neoplasms Oligonucleotide Array Sequence Analysis Pattern Recognition Automated Reproducibility of Results Sensitivity and Specificity Tumor Markers Biological

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Power and sample size calculation for log-rank test with a time lag in treatment effect

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STATISTICS IN MEDICINE 2009年第5期28卷 864-879页

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

The log-rank test is the most powerful non-parametric test for detecting a proportional hazards alternative and thus is the most commonly used testing procedure for comparing time-to-event distributions between different treatments in clinical trials. When the log-rank test is used for the primary data analysis, the sample size calculation should also be based on the test to ensure the desired power for the study. In some clinical trials, the treatment effect may not manifest itself right after patients receive the treatment. Therefore, the proportional hazards assumption may not hold. Furthermore, patients may discontinue the study treatment prematurely and thus may have diluted treatment effect after treatment discontinuation. If a patient's treatment termination time is independent of his/her time-to-event of interest, the termination time can be treated as a censoring time in the final data analysis. Alternatively, we may keep collecting time-to-event data until study termination from those patients who discontinued the treatment and conduct an intent-to-treat analysis by including them in the original treatment groups. We derive formulas necessary to calculate the asymptotic power of the log-rank test under this non-proportional hazards alternative for the two data analysis strategies. Simulation studies indicate that the formulas provide accurate power for a variety of trial settings. A clinical trial example is used to illustrate the application of the proposed methods. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： censoring intent-to-treat analysis treatment termination

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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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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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Multi-regional clinical trial design and consistency assessment of treatment effects

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STATISTICS IN MEDICINE 2014年第13期33卷 2191-2205页

作者： Quan, Hui Mao, Xuezhou Chen, Joshua Shih, Weichung Joe Ouyang, Soo Peter Zhang, Ji Zhao, Peng-Liang Binkowitz, Bruce Sanofi Rech Biostat & Programming Bridgewater NJ 08807 USA Merck Res Labs Rahway NJ 07065 USA Rutgers State Univ Sch Publ Hlth Dept Biostat Piscataway NJ 08854 USA Celgene Corp Biostat Summit NJ 07901 USA

We can apply both fixed and random effects models to multi-regional clinical trial (MRCT) design and data analysis. Thoroughly, understanding the features of these models in an MRCT setting will help assessing their applicability to an MRCT. In this paper, we discuss the interpretations of trial results from these models. We also evaluate the impact of the number of regions and the sample size configuration across the regions on the required total sample size for the overall treatment effect assessment. For quantifying treatment effects of individual regions, the empirical shrinkage estimator and the James-Stein type shrinkage estimator associate with smaller variability compared with the regular sample estimator. We conduct computation and simulation to compare the performance of these estimators when they are applied to assess consistency of treatment effects across regions. We use a multinational trial example to illustrate the application of these methods. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： sample size configuration random effects model between-region variability weighted estimator shrinkage estimator

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Bactericidal antibodies against hypervirulent Neisseria meningitidis C field strains following MenC-CRM or MenACWY-CRM priming and MenACWY-CRM booster in children

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HUMAN VACCINES & IMMUNOTHERAPEUTICS 2021年第5期17卷 1442-1449页

作者： Giuliani, Marzia Monica Biolchi, Alessia Keshavan, Pavitra Moriondo, Maria Tomei, Sara Santini, Laura Mori, Elena Brozzi, Alessandro Bodini, Margherita Nieddu, Francesco Ricci, Silvia Mzolo, Thembile Costantini, Marco Azzari, Chiara Pellegrini, Michele GSK Preclin Evidence Generat Siena Italy GSK Data Sci & Digital Innovat Siena Italy GSK Clin Res & Dev Siena Italy GSK Biostat & Stat Programming Siena Italy Univ Florence Dept Hlth Sci Florence Italy Meyer Childrens Univ Hosp Florence Italy GSK Biostat & Stat Programming Amsterdam Netherlands

An increase in invasive meningococcal disease (IMD) incidence was observed in Tuscany in 2015/2016, mainly due to hypervirulent clonal complex (cc) 11 strains. In a post-hoc analysis, we assessed bactericidal activity of antibodies in sera from children primed with MenACWY-CRM or MenC-CRM conjugate vaccines and receiving a MenACWY-CRM booster dose against 5 meningococcal C (MenC) strains isolated from IMD cases. Sera collected from 90 infants/toddlers who participated in a phase III, open-label study (NCT00667602) and its extension (NCT01345721) were tested by serum bactericidal activity assay with human complement (hSBA). Children were primed with either MenACWY-CRM at 6-8 and 12 months of age (group 2_MenACWY;N = 30), MenACWY-CRM (group 1_MenACWY;N = 30), or MenC-CRM at 12 months of age (group 1_MenC;N = 30);all received MenACWY-CRM booster dose at 22-45 months of age. Four tested strains (FI001-FI004) were C:P1.5-1,10-8:F3-6:ST-11 (cc11) and 1 (FI005) was C:P1.7-4,14-6:F3-9:ST-1031 (cc334). Overall, immune responses tended to be higher against Fl002-FI004 than Fl001 and Fl005. Geometric mean titers were high in group 2_MenACWY (range: 94.8 [FI005]-588.1 [FI004]) and very high post-boosting with MenACWY-CRM in all groups (176.9 [FI005]-3911.0 [FI004]). Seroresponse rates tended to be higher in group 1_MenC (33.3% [FI005]-93.3% [FI004]) than in group 1_MenACWY (16.7% [FI005]-73.3% [FI004]). Irrespective of strains tested or the identity/number of priming doses, >= 96.7% of children had hSBA titers >= 1:8 post-MenACWY-CRM booster dose. MenACWY-CRM and MenC-CRM elicited bactericidal antibodies and immunological memory against hypervirulent cc11 and cc334 MenC strains responsible for IMD outbreaks.

关键词： Hypervirulent MenC strains cc11 cc334 clonal complexes outbreak MenACWY-CRM conjugate vaccine MenC-CRM conjugate vaccine

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