Pharmaceutical Statistics最新文献

Single-arm trials (SATs), while not preferred, remain in use throughout the drug development cycle. They may be accepted by regulators in particular contexts (e.g., in oncology or rare diseases) when the potential effects of new treatments are very large and placebo treatment is unethical. However, in the postregulatory space, SATs are common, and perhaps even more poorly suited to address the questions of interest. In this manuscript, we review regulatory and HTA positions on SATs; challenges posed by SATs to address research questions beyond regulators, evolving statistical methods to provide context for SATs, case studies where SATs could and could not address questions of interest, and communication strategies to influence decision making and optimize study design to address evidence needs.

Delayed outcome is common in phase I oncology clinical trials. It causes logistic difficulty, wastes resources, and prolongs the trial duration. This article investigates this issue and proposes the time-to-event 3 + 3 (T3 + 3) design, which utilizes the actual follow-up time for at-risk patients with pending toxicity outcomes. The T3 + 3 design allows continuous accrual without unnecessary trial suspension and is costless and implementable with pretabulated dose decision rules. Besides, the T3 + 3 design uses the isotonic regression to estimate the toxicity rates across dose levels and therefore can accommodate for any targeted toxicity rate for maximum tolerated dose (MTD). It dramatically facilitates the trial preparation and conduct without intensive computation and statistical consultation. The extension to other algorithm-based phase I dose-finding designs (e.g., i3 + 3 design) is also studied. Comprehensive computer simulation studies are conducted to investigate the performance of the T3 + 3 design under various dose-toxicity scenarios. The results confirm that the T3 + 3 design substantially shortens the trial duration compared with the conventional 3 + 3 design and yields much higher accuracy in MTD identification than the rolling six design. In summary, the T3 + 3 design addresses the delayed outcome issue while keeping the desirable features of the 3 + 3 design, such as simplicity, transparency, and costless implementation. It has great potential to accelerate early-phase drug development.

Recombinant adeno-associated virus (AAV) has become a popular platform for many gene therapy applications. The strength of AAV-based products is a critical quality attribute that affects the efficacy of the drug and is measured as the concentration of vector genomes, or physical titer. Because the dosing of patients is based on the titer measurement, it is critical for manufacturers to ensure that the measured titer of the drug product is close to the actual concentration of the batch. Historically, dosing calculations have been performed using the measured titer, which is reported on the drug product label. However, due to recent regulatory guidance, sponsors are now expected to label the drug product with nominal or "target" titer. This new expectation for gene therapy products can pose a challenge in the presence of process and analytical variability. In particular, the manufacturer must decide if a dilution of the drug substance is warranted at the drug product stage to bring the strength in line with the nominal value. In this paper, we present two straightforward statistical methods to aid the manufacturer in the dilution decision. These approaches use the understanding of process and analytical variability to compute probabilities of achieving the desired drug product titer. We also provide an approach for determining an optimal assay replication strategy for achieving the desired probability of meeting drug product release specifications.

In this tutorial we explore the valuable partnership between statisticians and Institutional Animal Care and Use Committees (IACUCs) in the context of animal research, shedding light on the critical role statisticians play in ensuring the ethical and scientifically rigorous use of animals in research. Pharmaceutical statisticians have increasingly become vital members of these committees, contributing expertise in study design, data analysis, and interpretation, and working more generally to facilitate the integration of good statistical practices into experimental procedures. We review the "3Rs" principles (Replacement, Reduction, and Refinement) which are the foundation for the humane use of animals in scientific research, and how statisticians can partner with IACUC to help ensure robust and reproducible research while adhering to the 3Rs principles. We also highlight emerging areas of interest, such as the use of virtual control groups.

Animal models are used in cancer pre-clinical research to identify drug targets, select compound candidates for clinical trials, determine optimal drug dosages, identify biomarkers, and ensure compound safety. This tutorial aims to provide an overview of study design and data analysis from animal studies, focusing on tumor growth inhibition (TGI) studies used for prioritization of anticancer compounds. Some of the experimental design aspects discussed here include the selection of the appropriate biological models, the choice of endpoints to be used for the assessment of anticancer activity (tumor volumes, tumor growth rates, events, or categorical endpoints), considerations on measurement errors and potential biases related to this type of study, sample size estimation, and discussions on missing data handling. The tutorial also reviews the statistical analyses employed in TGI studies, considering both continuous endpoints collected at single time-point and continuous endpoints collected longitudinally over multiple time-points. Additionally, time-to-event analysis is discussed for studies focusing on event occurrences such as animal deaths or tumor size reaching a certain threshold. Furthermore, for TGI studies involving categorical endpoints, statistical methodology is outlined to compare outcomes among treatment groups effectively. Lastly, this tutorial also discusses analysis for assessing drug combination synergy in TGI studies, which involves combining treatments to enhance overall treatment efficacy. The tutorial also includes R sample scripts to help users to perform relevant data analysis of this topic.

In randomized clinical trials that use a long-term efficacy endpoint, the follow-up time necessary to observe the endpoint may be substantial. In such trials, an attractive option is to consider an interim analysis based solely on an early outcome that could be used to expedite the evaluation of treatment's efficacy. Garcia Barrado et al. (Pharm Stat. 2022; 21: 209-219) developed a methodology that allows introducing such an early interim analysis for the case when both the early outcome and the long-term endpoint are normally-distributed, continuous variables. We extend the methodology to any combination of the early-outcome and long-term-endpoint types. As an example, we consider the case of a binary outcome and a time-to-event endpoint. We further evaluate the potential gain in operating characteristics (power, expected trial duration, and expected sample size) of a trial with such an interim analysis in function of the properties of the early outcome as a surrogate for the long-term endpoint.

This paper proposes a trial design for locating group-specific doses when groups are partially or completely ordered by dose sensitivity. Previous trial designs for partially ordered groups are model-based, whereas the proposed method is model-assisted, providing clinicians with a design that is simpler. The proposed method performs similarly to model-based methods, providing simplicity without losing accuracy. Additionally, to the best of our knowledge, the proposed method is the first paper on dose-finding for partially ordered groups with convergence results. To generalize the proposed method, a framework is introduced that allows partial orders to be transferred to a grid format with a known ordering across rows but an unknown ordering within rows.

Difference in proportions is frequently used to measure treatment effect for binary outcomes in randomized clinical trials. The estimation of difference in proportions can be assisted by adjusting for prognostic baseline covariates to enhance precision and bolster statistical power. Standardization or g-computation is a widely used method for covariate adjustment in estimating unconditional difference in proportions, because of its robustness to model misspecification. Various inference methods have been proposed to quantify the uncertainty and confidence intervals based on large-sample theories. However, their performances under small sample sizes and model misspecification have not been comprehensively evaluated. We propose an alternative approach to estimate the unconditional variance of the standardization estimator based on the robust sandwich estimator to further enhance the finite sample performance. Extensive simulations are provided to demonstrate the performances of the proposed method, spanning a wide range of sample sizes, randomization ratios, and model specification. We apply the proposed method in a real data example to illustrate the practical utility.

What can be considered an appropriate statistical method for the primary analysis of a randomized clinical trial (RCT) with a time-to-event endpoint when we anticipate non-proportional hazards owing to a delayed effect? This question has been the subject of much recent debate. The standard approach is a log-rank test and/or a Cox proportional hazards model. Alternative methods have been explored in the statistical literature, such as weighted log-rank tests and tests based on the Restricted Mean Survival Time (RMST). While weighted log-rank tests can achieve high power compared to the standard log-rank test, some choices of weights may lead to type-I error inflation under particular conditions. In addition, they are not linked to a mathematically unambiguous summary measure. Test statistics based on the RMST, on the other hand, allow one to investigate the average difference between two survival curves up to a pre-specified time point $\tau$ -a mathematically unambiguous summary measure. However, by emphasizing differences prior to $\tau$ , such test statistics may not fully capture the benefit of a new treatment in terms of long-term survival. In this article, we introduce a graphical approach for direct comparison of weighted log-rank tests and tests based on the RMST. This new perspective allows a more informed choice of the analysis method, going beyond power and type I error comparison.

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 on mixture model to incorporate some of these features. In this work, we extend the mixture model to deal with multiple non-proportional patterns and develop its geometric average hazard ratio (gAHR) to quantify the treatment effect. We further derive a sample size and power formula based on the non-centrality parameter of the log-rank test and conduct a thorough analysis of the impact of each parameter on performance. Simulation studies showed a clear advantage of our new method over the proportional hazard based calculation across different non-proportional hazard scenarios. Moreover, the mixture modeling of two real trials demonstrates how to use the prior information on the survival distribution among patients with different biomarker and early efficacy results in practice. By comparison with a simulation-based design, the new method provided a more efficient way to compute the power and sample size with high accuracy of estimation. Overall, both theoretical derivation and empirical studies demonstrate the promise of the proposed method in powering future innovative trial designs.