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Statistical approaches to evaluate in vitro dissolution data against proposed dissolution specifications Pharm. Stat. (IF 1.5) Pub Date : 2024-03-17 Fasheng Li, Beverly Nickerson, Les Van Alstine, Ke Wang
In vitro dissolution testing is a regulatory required critical quality measure for solid dose pharmaceutical drug products. Setting the acceptance criteria to meet compendial criteria is required for a product to be filed and approved for marketing. Statistical approaches for analyzing dissolution data, setting specifications and visualizing results could vary according to product requirements, company's
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Optimal sample size allocation for two-arm superiority and non-inferiority trials with binary endpoints Pharm. Stat. (IF 1.5) Pub Date : 2024-03-12 Marietta Kirchner, Stefanie Schüpke, Meinhard Kieser
The sample size of a clinical trial has to be large enough to ensure sufficient power for achieving the aim the study. On the other side, for ethical and economical reasons it should not be larger than necessary. The sample size allocation is one of the parameters that influences the required total sample size. For two-arm superiority and non-inferiority trials with binary endpoints, we performed extensive
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Estimation of the odds ratio from multi‐stage randomized trials Pharm. Stat. (IF 1.5) Pub Date : 2024-03-11 Shiwei Cao, Sin‐Ho Jung
A multi‐stage design for a randomized trial is to allow early termination of the study when the experimental arm is found to have low or high efficacy compared to the control during the study. In such a trial, an early stopping rule results in bias in the maximum likelihood estimator of the treatment effect. We consider multi‐stage randomized trials on a dichotomous outcome, such as treatment response
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A propensity score-integrated approach for leveraging external data in a randomized controlled trial with time-to-event endpoints Pharm. Stat. (IF 1.5) Pub Date : 2024-03-05 Wei-Chen Chen, Nelson Lu, Chenguang Wang, Heng Li, Changhong Song, Ram Tiwari, Yunling Xu, Lilly Q. Yue
In a randomized controlled trial with time-to-event endpoint, some commonly used statistical tests to test for various aspects of survival differences, such as survival probability at a fixed time point, survival function up to a specific time point, and restricted mean survival time, may not be directly applicable when external data are leveraged to augment an arm (or both arms) of an RCT. In this
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Digital twins and Bayesian dynamic borrowing: Two recent approaches for incorporating historical control data Pharm. Stat. (IF 1.5) Pub Date : 2024-03-05 Carl‐Fredrik Burman, Erik Hermansson, David Bock, Stefan Franzén, David Svensson
Recent years have seen an increasing interest in incorporating external control data for designing and evaluating randomized clinical trials (RCT). This may decrease costs and shorten inclusion times by reducing sample sizes. For small populations, with limited recruitment, this can be especially important. Bayesian dynamic borrowing (BDB) has been a popular choice as it claims to protect against potential
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What they forgot to tell you about machine learning with an application to pharmaceutical manufacturing Pharm. Stat. (IF 1.5) Pub Date : 2024-02-28 Kjell Johnson, Max Kuhn
Predictive models (a.k.a. machine learning models) are ubiquitous in all stages of drug research, safety, development, manufacturing, and marketing. The results of these models are used inside and outside of pharmaceutical companies for the purpose of understanding scientific processes and for predicting characteristics of new samples or patients. While there are many resources that describe such models
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Sample size calculation for comparing two ROC curves Pharm. Stat. (IF 1.5) Pub Date : 2024-02-28 Sin‐Ho Jung
Biomarkers are key components of personalized medicine. In this paper, we consider biomarkers taking continuous values that are associated with disease status, called case and control. The performance of such a biomarker is evaluated by the area under the curve (AUC) of its receiver operating characteristic curve. Oftentimes, two biomarkers are collected from each subject to test if one has a larger
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Shrinkage priors for isotonic probability vectors and binary data modeling, with applications to dose–response modeling Pharm. Stat. (IF 1.5) Pub Date : 2024-02-24 Philip S. Boonstra, Daniel R. Owen, Jian Kang
Motivated by the need to model dose–response or dose‐toxicity curves in clinical trials, we develop a new horseshoe‐based prior for Bayesian isotonic regression modeling a binary outcome against an ordered categorical predictor, where the probability of the outcome is assumed to be monotonically non‐decreasing with the predictor. The set of differences between outcome probabilities in consecutive categories
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Introduction to qualification and validation of an immunoassay Pharm. Stat. (IF 1.5) Pub Date : 2024-02-13 Sarah Janssen
Immunoassays play an important role in drug development of products targeting the immune system. Consistent quality of the results from an immunoassay is essential to make unbiased and accurate claims about the drug product during preclinical and clinical development stages. Assay qualification and validation shed light on the performance of the assay. It is the first evaluation and the verification
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Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation Pharm. Stat. (IF 1.5) Pub Date : 2024-02-07 Björn Bornkamp, Silvia Zaoli, Michela Azzarito, Ruvie Martin, Carsten Philipp Müller, Conor Moloney, Giulia Capestro, David Ohlssen, Mark Baillie
We present the motivation, experience, and learnings from a data challenge conducted at a large pharmaceutical corporation on the topic of subgroup identification. The data challenge aimed at exploring approaches to subgroup identification for future clinical trials. To mimic a realistic setting, participants had access to 4 Phase III clinical trials to derive a subgroup and predict its treatment effect
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A case study: Assessing the efficacy of the revised dosage regimen via prediction model for recurrent event rate using biomarker data Pharm. Stat. (IF 1.5) Pub Date : 2024-02-05 Ahrim Youn, Jiarui Chi, Yue Cui, Hui Quan
In recently conducted phase III trials in a rare disease area, patients received monthly treatment at a high dose of the drug, which targets to lower a specific biomarker level, closely associated with the efficacy endpoint, to around 10% across patients. Although this high dose demonstrated strong efficacy, treatments were withheld due to the reports of serious adverse events. Dosing in these studies
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On the relative conservativeness of Bayesian logistic regression method in oncology dose-finding studies Pharm. Stat. (IF 1.5) Pub Date : 2024-02-05 Cheng-Han Yang, Guanghui Cheng, Ruitao Lin
The Bayesian logistic regression method (BLRM) is a widely adopted and flexible design for finding the maximum tolerated dose in oncology phase I studies. However, the BLRM design has been criticized in the literature for being overly conservative due to the use of the overdose control rule. Recently, a discussion paper titled “Improving the performance of Bayesian logistic regression model with overall
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The flaw of averages: Bayes factors as posterior means of the likelihood ratio Pharm. Stat. (IF 1.5) Pub Date : 2024-01-28 Charles C. Liu, Ron Xiaolong Yu, Murray Aitkin
As an alternative to the Frequentist p-value, the Bayes factor (or ratio of marginal likelihoods) has been regarded as one of the primary tools for Bayesian hypothesis testing. In recent years, several researchers have begun to re-analyze results from prominent medical journals, as well as from trials for FDA-approved drugs, to show that Bayes factors often give divergent conclusions from those of
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Transporting randomized trial results to estimate counterfactual survival functions in target populations Pharm. Stat. (IF 1.5) Pub Date : 2024-01-17 Zhiqiang Cao, Youngjoo Cho, Fan Li
When the distributions of treatment effect modifiers differ between a randomized trial and an external target population, the sample average treatment effect in the trial may be substantially different from the target population average treatment, and accurate estimation of the latter requires adjusting for the differential distribution of effect modifiers. Despite the increasingly rich literature
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Going beyond probability of success: Opportunities for statisticians to influence quantitative decision-making at the portfolio level Pharm. Stat. (IF 1.5) Pub Date : 2024-01-11 Stig-Johan Wiklund, Katharine Thorn, Heiko Götte, Kimberley Hacquoil, Gaëlle Saint-Hilary, Alex Carlton
The pharmaceutical industry is plagued with long, costly development and high risk. Therefore, a company's effective management and optimisation of a portfolio of projects is critical for success. Project metrics such as the probability of success enable modelling of a company's pipeline accounting for the high uncertainty inherent within the industry. Making portfolio decisions inherently involves
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Application of hypothetical strategies in acute pain Pharm. Stat. (IF 1.5) Pub Date : 2024-01-11 Jinglin Zhong, David Petullo
Since the publication of ICH E9 (R1), “Addendum to statistical principles for clinical trials: on choosing appropriate estimands and defining sensitivity analyses in clinical trials,” there has been a lot of debate about the hypothetical strategy for handling intercurrent events. Arguments against the hypothetical strategy are twofold: (1) the clinical question has limited clinical/regulatory interest;
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Frailty model with change points for survival analysis Pharm. Stat. (IF 1.5) Pub Date : 2024-01-08 Masahiro Kojima, Shunichiro Orihara
We propose a novel frailty model with change points applying random effects to a Cox proportional hazard model to adjust the heterogeneity between clusters. In the specially focused eight Empowered Action Group (EAG) states in India, there are problems with different survival curves for children up to the age of five in different states. Therefore, when analyzing the survival times for the eight EAG
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Comparison of nonparametric estimators of the expected number of recurrent events Pharm. Stat. (IF 1.5) Pub Date : 2023-12-28 Alexandra Erdmann, Jan Beyersmann, Erich Bluhmki
We compare the performance of nonparametric estimators for the mean number of recurrent events and provide a systematic overview for different recurrent event settings. The mean number of recurrent events is an easily interpreted marginal feature often used for treatment comparisons in clinical trials. Incomplete observations, dependencies between successive events, terminating events acting as competing
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Sample size calculation for mixture model based on geometric average hazard ratio and its applications to nonproportional hazard Pharm. Stat. (IF 1.5) Pub Date : 2023-12-28 Zixing Wang, Qingyang Zhang, Allen Xue, James Whitmore
With the advent of cancer immunotherapy, some special features including delayed treatment effect, cure rate, diminishing treatment effect and crossing survival are often observed in survival analysis. They violate the proportional hazard model assumption and pose a unique challenge for the conventional trial design and analysis strategies. Many methods like cure rate model have been developed based
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Evaluation of a flexible piecewise linear mixed-effects model in the analysis of randomized cross-over trials Pharm. Stat. (IF 1.5) Pub Date : 2023-12-25 Moses Mwangi, Geert Verbeke, Edmund Njeru Njagi, Alvaro Jose Florez, Samuel Mwalili, Anna Ivanova, Zipporah N. Bukania, Geert Molenberghs
Cross-over designs are commonly used in randomized clinical trials to estimate efficacy of a new treatment. They have received a lot of attention, particularly in connection with regulatory requirements for new drugs. The main advantage of using cross-over designs over conventional parallel designs is increased precision, thanks to within-subject comparisons. In the statistical literature, more recent
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Cautionary note on regional consistency evaluation in multiregional clinical trials with binary outcomes Pharm. Stat. (IF 1.5) Pub Date : 2023-12-20 Gosuke Homma
Multiregional clinical trials (MRCTs) have become increasingly common during the development of new drugs to obtain simultaneous drug approvals worldwide. When planning MRCTs, a major statistical challenge is determination of the regional sample size. In general, the regional sample size must be determined as the sample size such that the regional consistency probability, defined as the probability
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An evolutionary algorithm for the direct optimization of covariate balance between nonrandomized populations Pharm. Stat. (IF 1.5) Pub Date : 2023-12-18 Stephen Privitera, Hooman Sedghamiz, Alexander Hartenstein, Tatsiana Vaitsiakhovich, Frank Kleinjung
Matching reduces confounding bias in comparing the outcomes of nonrandomized patient populations by removing systematic differences between them. Under very basic assumptions, propensity score (PS) matching can be shown to eliminate bias entirely in estimating the average treatment effect on the treated. In practice, misspecification of the PS model leads to deviations from theory and matching quality
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Analysis of two binomial proportions in noninferiority confirmatory trials Pharm. Stat. (IF 1.5) Pub Date : 2023-12-11 Hassan Lakkis, Andrew Lakkis
In this article, we propose considering an approximate exact score (AES) test for noninferiority comparisons and we derive its test-based confidence interval for the difference between two independent binomial proportions. This test was published in the literature, but not its associated confidence interval. The p-value for this test is obtained by using exact binomial probabilities with the nuisance
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Propensity score-incorporated adaptive design approaches when incorporating real-world data Pharm. Stat. (IF 1.5) Pub Date : 2023-11-28 Nelson Lu, Wei-Chen Chen, Heng Li, Changhong Song, Ram Tiwari, Chenguang Wang, Yunling Xu, Lilly Q. Yue
The propensity score-integrated composite likelihood (PSCL) method is one method that can be utilized to design and analyze an application when real-world data (RWD) are leveraged to augment a prospectively designed clinical study. In the PSCL, strata are formed based on propensity scores (PS) such that similar subjects in terms of the baseline covariates from both the current study and RWD sources
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Statistical analysis of actigraphy data with generalised additive models Pharm. Stat. (IF 1.5) Pub Date : 2023-11-16 Edoardo Lisi, Juan J. Abellan
There is a growing interest in the use of physical activity data in clinical studies, particularly in diseases that limit mobility in patients. High-frequency data collected with digital sensors are typically summarised into actigraphy features aggregated at epoch level (e.g., by minute). The statistical analysis of such volume of data is not straightforward. The general trend is to derive metrics
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A dynamic power prior approach to non-inferiority trials for normal means Pharm. Stat. (IF 1.5) Pub Date : 2023-11-14 Francesco Mariani, Fulvio De Santis, Stefania Gubbiotti
Non-inferiority trials compare new experimental therapies to standard ones (active control). In these experiments, historical information on the control treatment is often available. This makes Bayesian methodology appealing since it allows a natural way to exploit information from past studies. In the present paper, we suggest the use of previous data for constructing the prior distribution of the
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An illness–death multistate model to implement delta adjustment and reference-based imputation with time-to-event endpoints Pharm. Stat. (IF 1.5) Pub Date : 2023-11-08 Alberto García-Hernandez, Teresa Pérez, María del Carmen Pardo, Dimitris Rizopoulos
With a treatment policy strategy, therapies are evaluated regardless of the disturbance caused by intercurrent events (ICEs). Implementing this estimand is challenging if subjects are not followed up after the ICE. This circumstance can be dealt with using delta adjustment (DA) or reference-based (RB) imputation. In the survival field, DA and RB imputation have been researched so far using multiple
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Conditional power and information fraction calculations at an interim analysis for random coefficient models Pharm. Stat. (IF 1.5) Pub Date : 2023-11-02 Sandra A. Lewis, Kevin J. Carroll, Todd DeVries, Jonathan Barratt
Random coefficient (RC) models are commonly used in clinical trials to estimate the rate of change over time in longitudinal data. Trials utilizing a surrogate endpoint for accelerated approval with a confirmatory longitudinal endpoint to show clinical benefit is a strategy implemented across various therapeutic areas, including immunoglobulin A nephropathy. Understanding conditional power (CP) and
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Probability of success and group sequential designs Pharm. Stat. (IF 1.5) Pub Date : 2023-11-02 Andrew P. Grieve
In this article, I extend the use of probability of success calculations, previously developed for fixed sample size studies to group sequential designs (GSDs) both for studies planned to be analyzed by standard frequentist techniques or Bayesian approaches. The structure of GSDs lends itself to sequential learning which in turn allows us to consider how knowledge about the result of an interim analysis
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Frequentist and Bayesian tolerance intervals for setting specification limits for left-censored gamma distributed drug quality attributes Pharm. Stat. (IF 1.5) Pub Date : 2023-10-23 Richard O. Montes
Tolerance intervals from quality attribute measurements are used to establish specification limits for drug products. Some attribute measurements may be below the reporting limits, that is, left-censored data. When data has a long, right-skew tail, a gamma distribution may be applicable. This paper compares maximum likelihood estimation (MLE) and Bayesian methods to estimate shape and scale parameters
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Enrollment forecast for clinical trials at the portfolio planning phase based on site-level historical data Pharm. Stat. (IF 1.5) Pub Date : 2023-10-23 Sheng Zhong, Yunzhao Xing, Mengjia Yu, Li Wang
An accurate forecast of a clinical trial enrollment timeline at the planning phase is of great importance to both corporate strategic planning and trial operational excellence. The naive approach often calculates an average enrollment rate from historical data and generates an inaccurate prediction based on a linear trend with the average rate. Under the traditional framework of a Poisson–Gamma model
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Group sequential design with maximin efficiency robust test for immunotherapy with generalized delayed treatment effect Pharm. Stat. (IF 1.5) Pub Date : 2023-10-19 Bosheng Li, Jingyi Zhang, Wenyun Yang, Liwen Su, Fangrong Yan
The delayed treatment effect is a common feature of immunotherapy, characterized by a gradual onset of action ranging from no effect to full effect. In this study, we propose a generalized delayed treatment effect function to depict the delayed effective process precisely and flexibly. To reduce potential power loss caused by the delayed treatment effect in a group sequential trial, we employ the maximin
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Effects of duration of follow-up and lag in data collection on the performance of adaptive clinical trials Pharm. Stat. (IF 1.5) Pub Date : 2023-10-14 Anders Granholm, Theis Lange, Michael O. Harhay, Aksel Karl Georg Jensen, Anders Perner, Morten Hylander Møller, Benjamin Skov Kaas-Hansen
Different combined outcome-data lags (follow-up durations plus data-collection lags) may affect the performance of adaptive clinical trial designs. We assessed the influence of different outcome-data lags (0–105 days) on the performance of various multi-stage, adaptive trial designs (2/4 arms, with/without a common control, fixed/response-adaptive randomisation) with undesirable binary outcomes according
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Duration of and time to response in oncology clinical trials from the perspective of the estimand framework Pharm. Stat. (IF 1.5) Pub Date : 2023-10-02 Hans-Jochen Weber, Stephen Corson, Jiang Li, François Mercier, Satrajit Roychoudhury, Martin Oliver Sailer, Steven Sun, Alexander Todd, Godwin Yung
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
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A conservative approach to leveraging external evidence for effective clinical trial design Pharm. Stat. (IF 1.5) Pub Date : 2023-09-26 Fabio Rigat
Prior probabilities of clinical hypotheses are not systematically used for clinical trial design yet, due to a concern that poor priors may lead to poor decisions. To address this concern, a conservative approach to Bayesian trial design is illustrated here, requiring that the operational characteristics of the primary trial outcome are stronger than the prior. This approach is complementary to current
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Evaluating hybrid controls methodology in early-phase oncology trials: A simulation study based on the MORPHEUS-UC trial Pharm. Stat. (IF 1.5) Pub Date : 2023-09-24 Guanbo Wang, Melanie Poulin-Costello, Herbert Pang, Jiawen Zhu, Hans-Joachim Helms, Irmarie Reyes-Rivera, Robert W. Platt, Menglan Pang, Artemis Koukounari
Phase Ib/II oncology trials, despite their small sample sizes, aim to provide information for optimal internal company decision-making concerning novel drug development. Hybrid controls (a combination of the current control arm and controls from one or more sources of historical trial data [HTD]) can be used to increase statistical precision. Here we assess combining two sources of Roche HTD to construct
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A marginalized two-part joint model for a longitudinal biomarker and a terminal event with application to advanced head and neck cancers Pharm. Stat. (IF 1.5) Pub Date : 2023-09-17 Denis Rustand, Laurent Briollais, Virginie Rondeau
The sum of the longest diameter (SLD) of the target lesions is a longitudinal biomarker used to assess tumor response in cancer clinical trials, which can inform about early treatment effect. This biomarker is semicontinuous, often characterized by an excess of zeros and right skewness. Conditional two-part joint models were introduced to account for the excess of zeros in the longitudinal biomarker
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Interim decision making in seamless trial designs: An application in an adaptive dose-finding study in a rare kidney disease Pharm. Stat. (IF 1.5) Pub Date : 2023-09-11 Olympia Papachristofi, Björn Bornkamp, Melanie Wright, Tim Friede
Adaptive seamless trial designs, combining the learning and confirming cycles of drug development in a single trial, have gained popularity in recent years. Adaptations may include dose selection, sample size re-estimation and enrichment of the study population. Despite methodological advances and recognition of the potential efficiency gains such designs offer, their implementation, including how
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Sample size calculation in clinical trials with two co-primary endpoints including overdispersed count and continuous outcomes Pharm. Stat. (IF 1.5) Pub Date : 2023-09-07 Gosuke Homma, Takuma Yoshida
Count outcomes are collected in clinical trials for new drug development in several therapeutic areas and the event rate is commonly used as a single primary endpoint. Count outcomes that are greater than the mean value are termed overdispersion; thus, count outcomes are assumed to have a negative binomial distribution. However, in clinical trials for treating asthma and chronic obstructive pulmonary
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Simulating and reporting frequentist operating characteristics of clinical trials that borrow external information: Towards a fair comparison in case of one-arm and hybrid control two-arm trials Pharm. Stat. (IF 1.5) Pub Date : 2023-08-26 Annette Kopp-Schneider, Manuel Wiesenfarth, Leonhard Held, Silvia Calderazzo
Borrowing information from historical or external data to inform inference in a current trial is an expanding field in the era of precision medicine, where trials are often performed in small patient cohorts for practical or ethical reasons. Even though methods proposed for borrowing from external data are mainly based on Bayesian approaches that incorporate external information into the prior for
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Adaptive designs for best treatment identification with top-two Thompson sampling and acceleration Pharm. Stat. (IF 1.5) Pub Date : 2023-08-12 Jixian Wang, Ram Tiwari
We consider outcome adaptive phase II or phase II/III trials to identify the best treatment for further development. Different from many other multi-arm multi-stage designs, we borrow approaches for the best arm identification in multi-armed bandit (MAB) approaches developed for machine learning and adapt them for clinical trial purposes. The best arm identification in MAB focuses on the error rate
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Adaptive designs for IVPT data with mixed scaled average bioequivalence Pharm. Stat. (IF 1.5) Pub Date : 2023-08-09 Daeyoung Lim, Elena Rantou, Jessica Kim, Sungwoo Choi, Nam Hee Choi, Stella Grosser
In vitro permeation tests (IVPT) offer accurate and cost-effective development pathways for locally acting drugs, such as topical dermatological products. For assessment of bioequivalence, the FDA draft guidance on generic acyclovir 5% cream introduces a new experimental design, namely the single-dose, multiple-replicate per treatment group design, as IVPT pivotal study design. We examine the statistical
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“Super-covariates”: Using predicted control group outcome as a covariate in randomized clinical trials Pharm. Stat. (IF 1.5) Pub Date : 2023-08-08 Björn Holzhauer, Emmanuel Taiwo Adewuyi
The power of randomized controlled clinical trials to demonstrate the efficacy of a drug compared with a control group depends not just on how efficacious the drug is, but also on the variation in patients' outcomes. Adjusting for prognostic covariates during trial analysis can reduce this variation. For this reason, the primary statistical analysis of a clinical trial is often based on regression
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Calculation of Phase 2 dose-finding study sample size for reliable Phase 3 dose selection Pharm. Stat. (IF 1.5) Pub Date : 2023-08-07 Fang Liu, Qing Zhao, Anthony J. Rodgers, Devan V. Mehrotra
Sample sizes of Phase 2 dose-finding studies, usually determined based on a power requirement to detect a significant dose–response relationship, will generally not provide adequate precision for Phase 3 target dose selection. We propose to calculate the sample size of a dose-finding study based on the probability of successfully identifying the target dose within an acceptable range (e.g., 80%–120%
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A Bayesian optimal interval design for dose optimization with a randomization scheme based on pharmacokinetics outcomes in oncology Pharm. Stat. (IF 1.5) Pub Date : 2023-08-06 Kentaro Takeda, Jing Zhu, Ran Li, Yusuke Yamaguchi
The primary objective of an oncology dose-finding trial for novel therapies, such as molecularly targeted agents and immune-oncology therapies, is to identify the optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. Pharmacokinetic (PK) information is considered an appropriate indicator for evaluating the level of drug intervention in humans
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Type-I-error rate inflation in mixed models for repeated measures caused by ambiguous or incomplete model specifications Pharm. Stat. (IF 1.5) Pub Date : 2023-07-30 Sebastian Häckl, Armin Koch, Florian Lasch
Pre-specification of the primary analysis model is a pre-requisite to control the family-wise type-I-error rate (T1E) at the intended level in confirmatory clinical trials. However, mixed models for repeated measures (MMRM) have been shown to be poorly specified in study protocols. The magnitude of a resulting T1E rate inflation is still unknown. This investigation aims to quantify the magnitude of
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Information fraction estimation: Strategies for a phase 3 non-inferiority maximum duration design with time to event outcome Pharm. Stat. (IF 1.5) Pub Date : 2023-07-26 Ha M. Dang, Mark D. Krailo, Todd A. Alonzo, Wendy J. Mack, John A. Kairalla
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
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Adherence, per-protocol effects, and the estimands framework Pharm. Stat. (IF 1.5) Pub Date : 2023-07-21 Oliver Keene
In the statistical literature, treatment effects in clinical trials are frequently described as either ITT or per-protocol effects. The estimand given for the per-protocol effect is the effect in adherers, where adherers are typically defined as adhering to the intervention as specified in the trial protocol. This dichotomy of treatment effects is unhelpful when there are in reality multiple treatment
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Inclusion of binary proxy variables in logistic regression improves treatment effect estimation in observational studies in the presence of binary unmeasured confounding variables Pharm. Stat. (IF 1.5) Pub Date : 2023-07-16 Cornelius Rosenbaum, Qingzhao Yu, Sarah Buzhardt, Elizabeth Sutton, Andrew G. Chapple
We present a simulation study and application that shows inclusion of binary proxy variables related to binary unmeasured confounders improves the estimate of a related treatment effect in binary logistic regression. The simulation study included 60,000 randomly generated parameter scenarios of sample size 10,000 across six different simulation structures. We assessed bias by comparing the probability
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PubPredict: Prediction of progression and survival in oncology leveraging publications and early efficacy data Pharm. Stat. (IF 1.5) Pub Date : 2023-07-13 Jianqi Zhang, Yufei Guo, Junyi Zhou, Hans Erik Rasmussen
In oncology/hematology early phase clinical trials, efficacies were often observed in terms of response rate, depth, timing, and duration. However, the true clinical benefits that eventually support registrational purpose are progression-free survival (PFS) and/or overall survival (OS), the follow-up of which are typically not long enough in early phase trials. This gap imposes challenges in strategies
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Leading beyond regulatory approval: Opportunities for statisticians to optimize evidence generation and impact clinical practice Pharm. Stat. (IF 1.5) Pub Date : 2023-07-11 Jenny Devenport, Alexander Schacht
The role and value of statistical contributions in drug development up to the point of health authority approval are well understood. But health authority approval is only a true ‘win’ if the evidence enables access and adoption into clinical practice. In today's complex and evolving healthcare environment, there is additional strategic evidence generation, communication, and decision support that
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Two-sample inference procedures under nonproportional hazards Pharm. Stat. (IF 1.5) Pub Date : 2023-07-10 Yi-Cheng Tai, Weijing Wang, Martin T. Wells
We introduce a new two-sample inference procedure to assess the relative performance of two groups over time. Our model-free method does not assume proportional hazards, making it suitable for scenarios where nonproportional hazards may exist. Our procedure includes a diagnostic tau plot to identify changes in hazard timing and a formal inference procedure. The tau-based measures we develop are clinically
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Modeling immunogenecity data to establish screening bioassays cut point Pharm. Stat. (IF 1.5) Pub Date : 2023-07-06 Jorge Quiroz, Satrajit Roychoudhury, Thomas Steinmetz, Harry Yang
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
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Determining the minimum duration of treatment in tuberculosis: An order restricted non-inferiority trial design Pharm. Stat. (IF 1.5) Pub Date : 2023-07-06 Alessandra Serra, Pavel Mozgunov, Geraint Davies, Thomas Jaki
Tuberculosis (TB) is one of the biggest killers among infectious diseases worldwide. Together with the identification of drugs that can provide benefits to patients, the challenge in TB is also the optimisation of the duration of these treatments. While conventional duration of treatment in TB is 6 months, there is evidence that shorter durations might be as effective but could be associated with fewer
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Defining estimands for efficacy assessment in single arm phase 1b or phase 2 clinical trials in oncology early development Pharm. Stat. (IF 1.5) Pub Date : 2023-07-04 Stefan Englert, François Mercier, Elizabeth A. Pilling, Victoria Homer, Christina Habermehl, Stefan Zimmermann, Natalia Kan-Dobrosky
The addendum of the ICH E9 guideline on the statistical principles for clinical trials introduced the estimand framework. The framework is designed to strengthen the dialog between different stakeholders, to introduce greater clarity in the clinical trial objectives and to provide alignment between the estimand and statistical analysis. Estimand framework related publications thus far have mainly focused
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Alone, together: On the benefits of Bayesian borrowing in a meta-analytic setting Pharm. Stat. (IF 1.5) Pub Date : 2023-06-15 Ofir Harari, Mohsen Soltanifar, Andre Verhoek, Bart Heeg
It is common practice to use hierarchical Bayesian model for the informing of a pediatric randomized controlled trial (RCT) by adult data, using a prespecified borrowing fraction parameter (BFP). This implicitly assumes that the BFP is intuitive and corresponds to the degree of similarity between the populations. Generalizing this model to any K ≥ 1 historical studies, naturally leads to empirical
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Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects Pharm. Stat. (IF 1.5) Pub Date : 2023-06-01 Stig Johan Wiklund, Magnus Önnheim, Magnus Ytterstad
Large pharmaceutical companies maintain a portfolio of assets, some of which are projects under development while others are on the market and generating revenue. The budget allocated to R&D may not always be sufficient to fund all the available projects for development. Much attention has been paid to the selection of optimal subsets of available projects to fit within the available budget. In this
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Variance estimation of the risk difference when using propensity-score matching and weighting with time-to-event outcomes Pharm. Stat. (IF 1.5) Pub Date : 2023-05-31 Guy Cafri, Peter C. Austin
Observational studies are increasingly being used in medicine to estimate the effects of treatments or exposures on outcomes. To minimize the potential for confounding when estimating treatment effects, propensity score methods are frequently implemented. Often outcomes are the time to event. While it is common to report the treatment effect as a relative effect, such as the hazard ratio, reporting
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Bayesian borrowing from historical control data in a vaccine efficacy trial Pharm. Stat. (IF 1.5) Pub Date : 2023-05-24 Lin Peng, Jing Jin, Laurent Chambonneau, Xing Zhao, Juying Zhang
In the context of vaccine efficacy trial where the incidence rate is very low and a very large sample size is usually expected, incorporating historical data into a new trial is extremely attractive to reduce sample size and increase estimation precision. Nevertheless, for some infectious diseases, seasonal change in incidence rates poses a huge challenge in borrowing historical data and a critical