Journal of Biopharmaceutical Statistics最新文献

Over the past decades, the primary interest in vaccine efficacy evaluation has mostly been on the effect observed in trial participants complying with the protocol requirements (per protocol analysis). The ICH E9 (R1) addendum provides a structured framework to formulate the clinical questions of interest and formalize them as estimands. In this paper, the estimand framework is retrospectively implemented in a human papillomavirus (HPV) phase 3 trial, where the vaccine efficacy was originally estimated on the per protocol set. We focus on two strategies for dealing with the presence of intercurrent events: the hypothetical and the principal stratum strategies. We address the interpretation of these two estimands, their estimation as well as articulation of the underlying identifiability assumptions. Finally, we leverage the results of the HPV application to formulate general considerations regarding the implementation of the ICH E9 (R1) addendum in vaccine efficacy studies.

When evaluating the effectiveness of a drug, a randomized controlled trial (RCT) is often considered the gold standard due to its ability to balance effect modifiers through randomization. While RCT assures strong internal validity, its restricted external validity poses challenges in extending treatment effects to the broader real-world population due to possible heterogeneity in covariates. In this paper, we introduce a procedure to generalize the RCT findings to the real-world trial-eligible population based on the adaption of existing statistical methods. We utilized the augmented inversed probability of sampling weighting (AIPSW) estimator for the estimation and omitted variable bias framework to assess the robustness of the estimate against the assumption violation caused by potentially unmeasured confounders. We analyzed an RCT comparing the effectiveness of lowering hypertension between Songling Xuemaikang Capsule (SXC) - a traditional Chinese medicine (TCM), and Losartan as an illustration. Based on current evidence, the generalization results indicated that by adjusting covariates distribution shift, although SXC is less effective in lowering blood pressure than Losartan on week 2, there is no statistically significant difference among the trial-eligible population at weeks 4-8. In addition, sensitivity analysis further demonstrated that the generalization is robust against potential unmeasured confounders.

The use of biomarkers to guide adaptive enrichment designs in oncology trials presents a promising strategy for increasing trial efficiency and improving the chance of identifying efficacious treatment in the right population. With a well-defined biomarker, such designs can enhance study power and reduce costs by adapting the trial focus to promising populations. However, existing adaptive enrichment designs may not have sufficiently flexible interim decision-making rules, testing procedures, and sample size re-estimation, limiting their full potential. In this research, we propose an improved biomarker-guided adaptive enrichment design that supports dynamic interim decision-making based on treatment effects observed in biomarker-positive, biomarker-negative, and overall populations. The design includes options for early stopping for efficacy or futility in both biomarker-positive and overall populations and incorporates sample size re-estimation using an improved conditional power method to optimize study power. Simulation results show that the proposed design maintains strong control of type I error and delivers high statistical power, with a high probability of correct interim decisions in cases where treatment is effective in either the biomarker-positive or overall population. This novel framework provides a more flexible and efficient approach to conducting oncology trials with heterogenous populations, ensuring that the most appropriate patient populations are selected as the trial progresses.

With the ever-growing cost of conducting traditional clinical trials and evolving regulatory paradigms, the need to deliver new medicines with speed and efficiency has become increasingly urgent. There are complex and innovative design approaches, emerging technologies, and abundant data sources that can be leveraged to address these challenges. However, their potential is not fully realized due to operational constraints and regulatory hurdles. We review the vast array of tools and technologies that are available, discuss their capabilities and limitations, and propose strategies for maximizing the efficiency of clinical trials through effective deployment of existing and new approaches.

Product ranking according to pre-specified criteria is essential for developing new technologies, allowing identification of more preferable candidates for further development. Such ranking often builds on the results of a network meta-analysis, where the relative or absolute performances of the various products are synthesized across multiple clinical studies, each of which considered only a subset of the products. Ranking involving both a negative and a positive reference enables the scientist to directly compare tested products against known benchmarks. Here, more preferable candidates are those products that approach the positive reference while remaining distant from the negative reference. We provide a new metric to quantify this multivariate distance following Bayesian meta-analysis. Our method does not simply rely on point estimates to perform the comparisons, but also accounts for their uncertainties via their posterior distributions. For each product, posterior probabilities of being comparable to the positive reference are computed, and subsequently penalized by the posterior probability of performing worse than the negative reference. Each product is then compared to a hypothetical product about which we have no knowledge, as captured by a uniform distribution. The result is a prospective metric that is directly interpretable as the improvement of any product beyond this state of ignorance. We illustrate our approach using a case study, in which the goal is to rank 16 antiperspirant products. Here, the FDA-recommended summary statistic (a measure of the relative sweat reduction between each product and no treatment) intrinsically features both positive and negative references. We then offer a brief simulation study to check our metric's performance in less complex, idealized settings where the true ranking is known. Our results indicate that our Bayesian approach is a novel and useful addition to the statistical ranking toolkit.

The role of regulatory flexibility in the review and approval process of rare disease drug and biologics development was recently studied by a Consensus Committee of the National Academy of Sciences, Engineering and Medicine (NASEM 2024). In this article, regulatory flexibility is referred to as the exercise of scientific judgement by the regulatory agencies such as the United States Food and Drug Administration (FDA), in the review and oversight of a wide range of products, diseases and circumstances (see e.g. 21CFR Subpart E). This flexibility is intended to assist the sponsors in obtaining substantial evidence regarding safety and effectiveness of a test treatment under investigation. Applying general scientific principles, regulatory flexibility should be transparent, objective, and applied without undermining the integrity, quality and scientific validity of clinical investigation of the test treatment under study. This article attempts to provide an overview regarding the application of regulatory flexibility in rare disease drug and biologic development, which could also be applied to drug products for normal conditions. In addition, some innovative strategies and approaches which reflect regulatory flexibility and current thinking are proposed. Statistical considerations regarding the implementation of regulatory flexibility and/or current thinking in support of the demonstration of the safety and efficacy in drug development are discussed.

In the context of clinical trial practices, the study power and sample size are typically determined based on the expected treatment effects on the primary endpoint collected over time. The utilization of longitudinal modeling for the primary endpoint offers a flexible approach that has the potential to reduce the sample size and duration of the trial, thereby improving operational efficiency and costs. Joint modeling of multiple endpoints presents a unique opportunity to understand how the primary endpoint evolves over time with other clinically important endpoints, and has the potential to increase precision of estimates and therefore increase study power when designing a study at planning stage and enhance understanding and interpretation of the data at a multi-dimensional level at the analysis stage. This approach enables a comprehensive evaluation of clinical evidence from various perspectives, rather than relying solely on isolated pieces of information. Joint modeling of multiple longitudinal endpoints would also help trial monitoring process as the trial accumulates clinical evidence of efficacy data, and there is a high demand in developing tools for statistical learning the treatment benefits on the go especially when the endpoint(s) is not well-established yet in some therapeutic indications. In this article, we will illustrate the use of joint modeling of longitudinal endpoints and its applications to study design, analysis, and trial monitoring practices. Simulation studies suggest that the potential efficiency gain would be achieved via leveraging information within endpoint over time and/or between endpoints. We developed an R shiny application to aid in and support identifying promising efficacy signals from endpoints under investigation during the trial monitoring. The implementation of the joint models and the added values will be discussed through case studies and/or simulation studies.

Observational cohort studies are increasingly being used for comparative effectiveness research to assess the safety of therapeutics. Recently, various doubly robust methods have been proposed for average treatment effect estimation by combining the treatment model and the outcome model via different vehicles, such as matching, weighting, and regression. The key advantage of doubly robust estimators is that they require either the treatment model or the outcome model to be correctly specified to obtain a consistent estimator of average treatment effects, and therefore lead to a more accurate and often more precise inference. However, little work has been done to understand how doubly robust estimators differ due to their unique strategies of using the treatment and outcome models and how machine learning techniques can be combined to boost their performance, which we call double machine learning estimators. Here, we examine multiple popular doubly robust methods and compare their performance using different treatment and outcome modeling via extensive simulations and a real-world application. We found that incorporating machine learning with doubly robust estimators such as the targeted maximum likelihood estimator gives the best overall performance. Practical guidance on how to apply doubly robust estimators is provided.

We propose an approach for constructing and evaluating the performance of inverse probability weighted robust mixture priors (IPW-RMP) which are applied to the parameters in treatment group-specific marginal models. Our framework allows practitioners to systematically study the robustness of Bayesian dynamic borrowing using the IPW-RMP to enhance the efficiency of inferences on marginal treatment effects (e.g. marginal risk difference) in a target study being planned. A key assumption motivating our work is that the data generation processes for the target study and external data source (e.g. historical study) will not be the same, likely having different distributions for key prognostic factors and possibly different outcome distributions even for individuals who have identical prognostic factors (e.g. different outcome model parameters). We demonstrate the approach using simulation studies based on both binary and time-to-event outcomes, and via a case study based on actual clinical trial data for a solid tumor cancer program. Our simulation results show that when the distribution of risk factors does in fact differ, the IPW-RMP provides improved performance compared to a standard RMP (e.g. increased power and reduced bias of the posterior mean point estimator) with essentially no loss of performance when the risk factor distributions do not differ. Thus, the IPW-RMP can safely be used in any situation where a standard RMP is appropriate.