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Among clinical trialists, there has been a growing interest in using external data to improve decision-making and accelerate drug development in randomized clinical trials (RCTs). Here we propose a novel approach that combines the propensity score weighting (PW) and the multi-source exchangeability modelling (MEM) approaches to augment the control arm of a RCT in the rare disease setting. First, propensity score weighting is used to construct weighted external controls that have similar observed pre-treatment characteristics as the current trial population. Next, the MEM approach evaluates the similarity in outcome distributions between the weighted external controls and the concurrent control arm. The amount of external data we borrow is determined by the similarities in pretreatment characteristics and outcome distributions. The proposed approach can be applied to binary, continuous and count data. We evaluate the performance of the proposed PW-MEM method and several competing approaches based on simulation and re-sampling studies. Our results show that the PW-MEM approach improves the precision of treatment effect estimates while reducing the biases associated with borrowing data from external sources., (© 2024 John Wiley & Sons Ltd.)

Duration of response (DOR) and time to response (TTR) are typically evaluated as secondary endpoints in early-stage clinical studies in oncology when efficacy is assessed by the best overall response and presented as the overall response rate. Despite common use of DOR and TTR in particular in single-arm studies, the definition of these endpoints and the questions they are intended to answer remain unclear. Motivated by the estimand framework, we present relevant scientific questions of interest for DOR and TTR and propose corresponding estimand definitions. We elaborate on how to deal with relevant intercurrent events which should follow the same considerations as implemented for the primary response estimand. A case study in mantle cell lymphoma illustrates the implementation of relevant estimands of DOR and TTR. We close the paper with practical recommendations to implement DOR and TTR in clinical study protocols., (© 2023 John Wiley & Sons Ltd.)

Exposure-response (E-R) analyses are an integral component of understanding the benefit/risk profile of novel oncology therapeutics. These analyses are typically conducted using data from the treatment arm to characterize the relationship between drug exposure (low vs. high) and efficacy or safety outcomes. For example, outcomes of patients with lower exposure in the treatment arm (e.g., Q1) might be compared to outcomes of those with higher drug exposure (Q2, Q3, and Q4). Outcomes from the lowest exposure quartile may be also compared to the control arm to evaluate whether the Q1 subgroup derived clinical benefit. However, the sample size and the distribution of patient baseline characteristics and disease risk factors are not balanced in such a comparison (Q1 vs. control), which may bias the analysis and causal interpretation of clinical benefit in the Q1 subgroup. Herein, we report the use of case-control matching to account for this bias and better understand the E-R relationship for avelumab in urothelial carcinoma, a PD-L1 inhibitor approved for the treatment of several cancers. Data from JAVELIN-100 was utilized which is a phase III study of avelumab in first-line maintenance treatment in patients with urothelial carcinoma; this clinical study demonstrated superiority of avelumab versus best-supportive care leading to approval in the United States, Europe, and other countries. A post hoc case-control matching method was implemented to compare the efficacy outcome between Q1 avelumab subgroup and matched patients extracted from the control arm with similar baseline characteristics, which showed a clinically relevant difference in overall survival in favor of the Q1 avelumab subgroup. This analysis demonstrates the importance of accounting for imbalance in important baseline covariates when comparing efficacy outcomes between subgroups within the treatment arm versus the control arm., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The response of immunogenecity anti-drug antibody (ADA) generally includes biological and analytical variability. The nature of biological and analytical variations may lead to a variety of symmetric and asymmetric ADA data. As a result, current statistical methods may yield unreliable results because these methods assume special types of symmetric or asymmetric ADA data. In this paper, we survey and compare parametric models that are useful for analyzing a variety of asymmetric data that have rarely been used to calculate assay cut points. These models include symmetric distributions as limiting case; therefore, they are useful in the analysis of a variety of symmetric data. We also investigate two nonparametric approaches that have received little attention in screening cut point calculations. A simulation study was conducted to compare the performance of the methods. We evaluate the methods using four published different types of data, and make recommendations concerning the use of the methods., (© 2023 John Wiley & Sons Ltd.)

Duplicate analysis is a strategy commonly used to assess precision of bioanalytical methods. In some cases, duplicate analysis may rely on pooling data generated across organizations. Despite being generated under comparable conditions, organizations may produce duplicate measurements with different precision. Thus, these pooled data consist of a heterogeneous collection of duplicate measurements. Precision estimates are often expressed as relative difference indexes (RDI), such as relative percentage difference (RPD). Empirical evidence indicates that the frequency distribution of RDI values from heterogeneous data exhibits sharper peaks and heavier tails than normal distributions. Therefore, traditional normal-based models may yield faulty or unreliable estimates of precision from heterogeneous duplicate data. In this paper, we survey application of the mixture models that satisfactorily represent the distribution of RDI values from heterogeneous duplicate data. A simulation study was conducted to compare the performance of the different models in providing reliable estimates and inferences of percentile calculated from RDI values. These models are readily accessible to practitioners for study implementation through the use of modern statistical software. The utility of mixture models are explained in detail using a numerical example., (© 2022 John Wiley & Sons Ltd.)

Rare diseases represent a highly heterogeneous group of disorders with high phenotypic and genotypic diversity within individual conditions. Due to the small numbers of people affected, there are unique challenges in understanding rare diseases and drug development for these conditions, including patient identification and recruitment, trial design, and costs. Natural history data and real-world data (RWD) play significant roles in defining and characterizing disease progression, final patient populations, novel biomarkers, genetic relationships, and treatment effects. This review provides an introduction to rare diseases, natural history data, RWD, and real-world evidence, the respective sources and applications of these data in several rare diseases. Considerations for data quality and limitations when using natural history and RWD are also elaborated. Opportunities are highlighted for cross-sector collaboration, standardized and high-quality data collection using new technologies, and more comprehensive evidence generation using quantitative approaches such as disease progression modeling, artificial intelligence, and machine learning. Advanced statistical approaches to integrate natural history data and RWD to further disease understanding and guide more efficient clinical study design and data analysis in drug development in rare diseases are also discussed., (© 2022 PTC Therapeutics Inc, Critical Path Institute and Pfizer Inc. The Journal of Clinical Pharmacology published by Wiley Periodicals LLC on behalf of American College of Clinical Pharmacology.)

In clinical trials, placebo response is considered a beneficial effect arising from multiple factors, including the patient's expectations for the treatment. Its presence makes the classical parallel study design suboptimal and can bias the inference. The sequential parallel comparison design (SPCD), a two-stage design where the first stage is a classical parallel study design, followed by another parallel design among placebo subjects from the first stage, was proposed to address the shortcomings of the classical design. In SPCD, in lieu of treatment effect, a weighted average of the mean treatment difference in Stage I among all randomized subjects and the mean treatment difference in Stage II among placebo non-responders was proposed as the efficacy measure. However, by linking two possibly different populations, this weighted average lacks interpretability, and the choice of weight remains controversial. In this work, under the principal stratification framework, we propose a causal estimand for the treatment effect under each of three clinically important principal strata: Always Responders, Never Responders, and Drug-only Responders. To make the stratum treatment effect identifiable, we introduce a set of assumptions and two sensitivity parameters. By further considering the strata as latent characteristics, the sensitivity parameters can be estimated. An extensive simulation study is conducted to evaluate the operating characteristics of the proposed method. Finally, we apply our method on the ADAPT-A study data to assess the benefit of low-dose aripiprazole adjunctive to antidepressant therapy treatment., (© 2022 John Wiley & Sons Ltd.)

Although the digital revolution has transformed many areas of human endeavor, pharmaceutical drug development has been relatively slow to embrace the emerging technologies to enhance efficiency and optimize value in clinical trials. The topic has garnered even greater attention in the face of the coronavirus disease 2019 (COVID-19) outbreak, which has caused unprecedented disruption in the conduct of clinical trials and presented considerable challenges and opportunities for clinical trialists and data analysts. In this paper, we highlight the potential opportunity with virtual or digital clinical trials as viable options to enhance efficiency in drug development and, more importantly, in offering diverse patients easier and attractive means to participate in clinical trials. Special reference is made to the implication of artificial intelligence and machine-learning tools in trial execution and data acquisition, processing, and analysis in a virtual trial setting. Issues of patient safety, measurement validity, and data integrity are reviewed, and considerations are put forth with reference to the mitigation of underlying regulatory and operational barriers., (© 2021 The Authors. Clinical Pharmacology & Therapeutics © 2021 American Society for Clinical Pharmacology and Therapeutics.)

The estimand framework included in the addendum to the ICH E9 guideline facilitates discussions to ensure alignment between the key question of interest, the analysis, and interpretation. Therapeutic knowledge and drug mechanism play a crucial role in determining the strategy and defining the estimand for clinical trial designs. Clinical trials in patients with hematological malignancies often present unique challenges for trial design due to complexity of treatment options and existence of potential curative but highly risky procedures, for example, stem cell transplant or treatment sequence across different phases (induction, consolidation, maintenance). Here, we illustrate how to apply the estimand framework in hematological clinical trials and how the estimand framework can address potential difficulties in trial result interpretation. This paper is a result of a cross-industry collaboration to connect the International Conference on Harmonisation (ICH) E9 addendum concepts to applications. Three randomized phase 3 trials will be used to consider common challenges including intercurrent events in hematologic oncology trials to illustrate different scientific questions and the consequences of the estimand choice for trial design, data collection, analysis, and interpretation. Template language for describing estimand in both study protocols and statistical analysis plans is suggested for statisticians' reference., (© 2021 John Wiley & Sons Ltd.)

A randomized trial allows estimation of the causal effect of an intervention compared to a control in the overall population and in subpopulations defined by baseline characteristics. Often, however, clinical questions also arise regarding the treatment effect in subpopulations of patients, which would experience clinical or disease related events post-randomization. Events that occur after treatment initiation and potentially affect the interpretation or the existence of the measurements are called intercurrent events in the ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment, randomization alone is no longer sufficient to meaningfully estimate the treatment effect. Analyses comparing the subgroups of patients without the intercurrent events for intervention and control will not estimate a causal effect. This is well known, but post-hoc analyses of this kind are commonly performed in drug development. An alternative approach is the principal stratum strategy, which classifies subjects according to their potential occurrence of an intercurrent event on both study arms. We illustrate with examples that questions formulated through principal strata occur naturally in drug development and argue that approaching these questions with the ICH E9(R1) estimand framework has the potential to lead to more transparent assumptions as well as more adequate analyses and conclusions. In addition, we provide an overview of assumptions required for estimation of effects in principal strata. Most of these assumptions are unverifiable and should hence be based on solid scientific understanding. Sensitivity analyses are needed to assess robustness of conclusions., (© 2021 John Wiley & Sons Ltd.)

The recent 21st Century Cures Act propagates innovations to accelerate the discovery, development, and delivery of 21st century cures. It includes the broader application of Bayesian statistics and the use of evidence from clinical expertise. An example of the latter is the use of trial-external (or historical) data, which promises more efficient or ethical trial designs. We propose a Bayesian meta-analytic approach to leverage historical data for time-to-event endpoints, which are common in oncology and cardiovascular diseases. The approach is based on a robust hierarchical model for piecewise exponential data. It allows for various degrees of between trial-heterogeneity and for leveraging individual as well as aggregate data. An ovarian carcinoma trial and a non-small cell cancer trial illustrate methodological and practical aspects of leveraging historical data for the analysis and design of time-to-event trials., (© 2020 John Wiley & Sons, Ltd.)

Assessment of analytical similarity of tier 1 quality attributes is based on a set of hypotheses that tests the mean difference of reference and test products against a margin adjusted for standard deviation of the reference product. Thus, proper assessment of the biosimilarity hypothesis requires statistical tests that account for the uncertainty associated with the estimations of the mean differences and the standard deviation of the reference product. Recently, a linear reformulation of the biosimilarity hypothesis has been proposed, which facilitates development and implementation of statistical tests. These statistical tests account for the uncertainty in the estimation process of all the unknown parameters. In this paper, we survey methods for constructing confidence intervals for testing the linearized reformulation of the biosimilarity hypothesis and also compare the performance of the methods. We discuss test procedures using confidence intervals to make possible comparison among recently developed methods as well as other previously developed methods that have not been applied for demonstrating analytical similarity. A computer simulation study was conducted to compare the performance of the methods based on the ability to maintain the test size and power, as well as computational complexity. We demonstrate the methods using two example applications. At the end, we make recommendations concerning the use of the methods., (© 2019 John Wiley & Sons, Ltd.)

Clinical trials with multiple strata are increasingly used in drug development. They may sometimes be the only option to study a new treatment, for example in small populations and rare diseases. In early phase trials, where data are often sparse, good statistical inference and subsequent decision-making can be challenging. Inferences from simple pooling or stratification are known to be inferior to hierarchical modeling methods, which build on exchangeable strata parameters and allow borrowing information across strata. However, the standard exchangeability (EX) assumption bears the risk of too much shrinkage and excessive borrowing for extreme strata. We propose the exchangeability-nonexchangeability (EXNEX) approach as a robust mixture extension of the standard EX approach. It allows each stratum-specific parameter to be exchangeable with other similar strata parameters or nonexchangeable with any of them. While EXNEX computations can be performed easily with standard Bayesian software, model specifications and prior distributions are more demanding and require a good understanding of the context. Two case studies from phases I and II (with three and four strata) show promising results for EXNEX. Data scenarios reveal tempered degrees of borrowing for extreme strata, and frequentist operating characteristics perform well for estimation (bias, mean-squared error) and testing (less type-I error inflation)., (Copyright © 2015 John Wiley & Sons, Ltd.)

Clinical trials rarely, if ever, occur in a vacuum. Generally, large amounts of clinical data are available prior to the start of a study, particularly on the current study's control arm. There is obvious appeal in using (i.e., 'borrowing') this information. With historical data providing information on the control arm, more trial resources can be devoted to the novel treatment while retaining accurate estimates of the current control arm parameters. This can result in more accurate point estimates, increased power, and reduced type I error in clinical trials, provided the historical information is sufficiently similar to the current control data. If this assumption of similarity is not satisfied, however, one can acquire increased mean square error of point estimates due to bias and either reduced power or increased type I error depending on the direction of the bias. In this manuscript, we review several methods for historical borrowing, illustrating how key parameters in each method affect borrowing behavior, and then, we compare these methods on the basis of mean square error, power and type I error. We emphasize two main themes. First, we discuss the idea of 'dynamic' (versus 'static') borrowing. Second, we emphasize the decision process involved in determining whether or not to include historical borrowing in terms of the perceived likelihood that the current control arm is sufficiently similar to the historical data. Our goal is to provide a clear review of the key issues involved in historical borrowing and provide a comparison of several methods useful for practitioners., (Copyright © 2013 John Wiley & Sons, Ltd.)

ADP-glucose pyrophosphorylase (AGPase) is the first rate limiting enzyme of starch biosynthesis pathway and has been exploited as the target for greater starch yield in several plants. The structure-function analysis and substrate binding specificity of AGPase have provided enormous potential for understanding the role of specific amino acid or motifs responsible for allosteric regulation and catalytic mechanisms, which facilitate the engineering of AGPases. We report the three-dimensional structure, substrate, and inhibitor binding specificity of AGPase small subunit from different monocot and dicot crop plants. Both monocot and dicot subunits were found to exploit similar interactions with the substrate and inhibitor molecule as in the case of their closest homologue potato tuber AGPase small subunit. Comparative sequence and structural analysis followed by molecular docking and electrostatic surface potential analysis reveal that rearrangements of secondary structure elements, substrate, and inhibitor binding residues are strongly conserved and follow common folding pattern and orientation within monocot and dicot displaying a similar mode of allosteric regulation and catalytic mechanism. The results from this study along with site-directed mutagenesis complemented by molecular dynamics simulation will shed more light on increasing the starch content of crop plants to ensure the food security worldwide.

Frequently, count data obtained from dilution assays are subject to an upper detection limit, and as such, data obtained from these assays are usually censored. Also, counts from the same subject at different dilution levels are correlated. Ignoring the censoring and the correlation may provide unreliable and misleading results. Therefore, any meaningful data modeling requires that the censoring and the correlation be simultaneously addressed. Such comprehensive approaches of modeling censoring and correlation are not widely used in the analysis of dilution assays data. Traditionally, these data are analyzed using a general linear model on a logarithmic-transformed average count per subject. However, this traditional approach ignores the between-subject variability and risks, providing inconsistent results and unreliable conclusions. In this paper, we propose the use of a censored negative binomial model with normal random effects to analyze such data. This model addresses, in addition to the censoring and the correlation, any overdispersion that may be present in count data. The model is shown to be widely accessible through the use of several modern statistical software., (Copyright © 2012 John Wiley & Sons, Ltd.)

Background: A subset of head and neck squamous cell carcinoma (HNSCC) exhibits a microsatellite instability (MIN) phenotype. The authors correlated alterations in the mismatch-repair genes human mutL homolog 1 (hMLH1) and human mutS homolog 2 (hMSH2) in primary head and neck squamous cell carcinoma (HNSCC) tumors and in samples of leukoplakia with the MIN phenotype., Methods: One hundred twenty-three paired HNSCC normal and tumor tissues and 27 leukoplakia samples were examined for hypermethylation of hMLH1 and hMSH2 promoters. The hypermethylation status of the tissues was confirmed by expression studies. Sixty-three of 123 randomly selected tumors and all 27 leukplakia samples were genotyped with 8 microsatellite markers to determine MIN., Results: Fifty percent of HNSCC tumors and 63% of leukoplakia samples harbored hypermethylation at either or both hMLH1 and hMSH2 promoters. Normal tissues adjacent to methylation-positive tumors also demonstrated hypermethylation of both promoters at a high frequency (25%). A positive correlation between tobacco habit and promoter hypermethylation was observed (P = .001). A correlation was observed between MIN and the frequency of promoter hypermethylation in the leukoplakia samples, but no such trend was observed in the HNSCC tumors. It is noteworthy that patients who had a high frequency of MIN-positive tumors exhibited hypermethylation in both the affected tissues and the adjacent normal tissues (P = .007). Patients with a tobacco habit who had promoter hypermethylation at both the affected tissues and the adjacent normal tissues had tumors that mostly were MIN positive (P = .047)., Conclusions: The current results suggested that tobacco-addicted individuals are more susceptible to promoter hypermethylation of hMLH1 and hMSH2 and that, if such hypermethylation occurs in the normal squamous epithelium of the head and neck region, then those tissues are likely to develop into tumors that involve the MIN pathway.

Deletions in chromosome (chr.) 13q occur frequently in head and neck squamous cell carcinoma (HNSCC). Previous studies failed to identify common deleted regions in chr.13q, though several candidate tumour suppressor genes (TSGs) loci, e.g. BRCA2, RB1 and BRCAX have been localized in this chromosome, as well as no prognostic significance of the deletion has been reported. Thus, in the present study, deletion mapping of chr. 13q has been done in 55 primary HNSCC samples of Indian patients using 11 highly polymorphic microsatellite markers of which three were intragenic to BRCA2 gene, one intragenic to RB1 gene and another from BRCAX locus. The deletion in chr.13q was significantly associated with progression of HNSCC. High frequencies (27-39%) of loss of heterozygosity were found in 13q13.1 (BRCA2), 13q14.2 (RB1), 13q21.2-22.1 (BRCAX) and 13q31.1 regions. Deletions in the BRCA2 and RB1 regions were significantly correlated. The four highly deleted regions were associated with clinical stage and histological grades of the tumour as well as poor patient outcome. Deletion in the 13q31.1 region was only found to be associated with HPV infection. High frequencies (11-23%) of microsatellite size alteration (MA) were seen to overlap with the highly deleted regions. Forty per cent of the samples showed rare biallelic alteration whereas loss of normal copy of chromosome 13q was seen in five tumours. Thus, it seems that the putative TSGs located in the BRCAX and 13q31.1 regions as well as the BRCA2 and RB1 genes may have some cumulative effect in progression and poor prognosis of HNSCC. Significant association between deletion in BRCA2 and RB1 gene loci may indicate functional relationship between the genes in this tumour progression.

Growth and alpha-amylase production characteristics of Bacillus amyloliquefaciens strain F (ATCC 23350) in batch cultures are examined using glucose or maltose as the carbon source. While the cell growth is rapid when glucose is used as the carbon source, higher cell mass, higher total and specific enzyme activities, and higher enzyme production rates are obtained when maltose is used as the carbon source. The overall specific enzyme activity decreases with an increase in the initial concentration of carbon source. The oxygen requirement and carbon dioxide generation vary linearly with the maximum amount of cell mass produced. For experiments conducted using glucose as the carbon source, the kinetics of cell growth and glucose consumption are described using a special form of the Vavilin equation. For a given amount of initial carbon source, the enzyme synthesis capability is retained by the microorganism, although at a substantially reduced level, under severe oxygen limitation.