The New England Journal of Statistics in Data Science

The phase III BNT162b2 mRNA COVID-19 vaccine trial is based on a Bayesian design and analysis, and the main evidence of vaccine efficacy is presented in Bayesian statistics. Confusion and mistakes arise in the presentation of the Bayesian results. Some key statistics, such as Bayesian credible intervals, are mislabeled and stated as confidence intervals. Posterior probabilities of the vaccine efficacy are not reported as the main results. We illustrate the main differences in the reporting of Bayesian analysis results for a clinical trial and provide four recommendations. We argue that statistical evidence from a Bayesian trial, when presented properly, is easier to interpret and directly addresses the main clinical questions, thereby better supporting regulatory decision making. We also recommend using the abbreviation “BI” to represent Bayesian credible interval as a differentiation to “CI” which stands for confidence interval.

The biggest risk in new drug development is either unaware of, or under-estimate the potential risks in designing clinical trials. Among all challenges in drug development, the most critical one is about finding the appropriate dose(s) for the study drug in treating patients. Designing dose finding clinical trials involves in many potential risks. In practice, most of the expensive failures in drug development originated not from “We did not know”, rather, the mistake is “We thought we knew”. In other words, greatest risks came from lack of awareness of underlying assumptions. This manuscript attempts to discuss some of these risks and make recommendations to reduce risks in the design of dose finding clinical trials. This is not a complete list of risks, but it is only starting the discussion.

Dose proportionality is an essential aspect of pharmacokinetics (PK). We aim to enhance the efficiency of PK studies by incorporating interim analyses and utilizing data from past trials to increase precision and enable early termination of studies if applicable. In this paper, we extend the multisource exchangeability model (MEM) to the setting with correlated data with interim analyses. Simulation results indicate that the MEM estimators are efficient even with smaller sample sizes, although smaller sample sizes may have higher mean square error (MSE) and bias due to early stopping and more liberal data borrowing from non-exchangeable supplementary sources. Our recommendation is to use a constrained MEM approach when considering small sample sizes, with additional caution needed around the equivalence boundary to better control the inflated type I error rate, bias, and MSE. This research extends the application of MEMs from linear regression models to settings with correlated data using linear mixed effects regression models. It also innovatively applies MEMs to equivalence testing in the context of dose proportionality studies, thereby enhancing their efficiency.

MCP-Mod (Multiple Comparison Procedure-Modelling) is an efficient statistical method for the analysis of Phase II dose-finding trials, although it requires specialised expertise to pre-specify plausible candidate models along with model parameters. This can be problematic given limited knowledge of the agent/compound being studied, and misspecification of candidate models and model parameters can severely degrade its performance. To circumvent this challenge, in the work, we introduce LiMAP-curvature, a Bayesian model-free approach for the detection of the dose-response signal in Phase II dose-finding trials. LiMAP-curvature is built upon a Bayesian hierarchical framework incorporating information about the total curvature of the dose-response curve. Through extensive simulations, we show that LiMAP-curvature has comparable performance to MCP-Mod if the true underlying dose-response model is included in the candidate model set of MCP-Mod. Otherwise, LiMAP-curvature can achieve performance superior to that of MCP-Mod, especially when the true dose-response model drastically deviates from candidate models in MCP-Mod.

We study local change point detection in variance using generalized likelihood ratio tests. Building on [24], we utilize the multiplier bootstrap to approximate the unknown, non-asymptotic distribution of the test statistic and introduce a multiplicative bias correction that improves upon the existing additive version. This proposed correction offers a clearer interpretation of the bootstrap estimators while significantly reducing computational costs. Simulation results demonstrate that our method performs comparably to the original approach. We apply it to the growth rates of U.S. inflation, industrial production, and Bitcoin returns.

Historically, the primary objective of Phase I clinical trials has been to pick an optimal dose in terms of patient safety, referred to as the maximum tolerated dose (MTD). Most of these trials recommend a “one-size-fits-all” dose for the patient population being studied, while also solely focusing on short-term adverse events. Often patient heterogeneity exists so that group-specific dose selection is of interest. To address the issue of patient heterogeneity, several dose-finding methods have been proposed, including the shift model framework based on the Continual Reassessment Method (CRM). Additionally, for many cancer therapies, relevant toxicities may be defined by participants experiencing adverse events at any point over a long evaluation window, resulting in pending outcomes when new participants need to be assigned a dose. By leveraging the CRM, the time-to-event continual reassessment method (TITE-CRM) provides a feasible approach for addressing this issue. Motivated by a Phase I trial involving radiotherapy that included two patient groups conducted at the University of Virginia, we have developed a hybrid design that combines elements from the TITE-CRM and the shift model framework. This approach helps address patient heterogeneity and late-onset toxicity simultaneously. We illustrate how to perform a dose-finding trial using the proposed design, and compare its operating characteristics to other suggested methods in the field by conducting a simulation study. The shift model TITE-CRM is shown to be a practical design with good operating characteristics in regard to selecting the correct MTD in each group. An R package is also being developed, allowing investigators to provide group-specific MTD recommendations by applying the proposed design, in addition to providing operating characteristics for custom simulation settings.

The high cost of drug development and the relatively low success rates of phase III clinical trials highlight the need for improved and reasonably sized phase II trial designs, especially when responses observed in treatment and control could not lead to a clear-cut decision warranting further studies. To this end, we propose a three-outcome dual-criterion randomized (TDR) trial design, which implements inconclusive region sculpting using boundaries defined by both statistically significant differences between treatment and control as well as the clinical relevance of treatment responses. We provide statistical justifications for the TDR design in both one-stage and two-stage trial settings. Additionally, we evaluate its operating characteristics through a comparison with existing designs. The proposed design is shown able to achieve sample size saving and type II error reduction while controlling the type I error at a marginal cost of power reduction. Lastly, robustness under various deviations from the assumed control response rate is also demonstrated.

Conventional methods for analyzing composite endpoints in clinical trials often only focus on the time to the first occurrence of all events in the composite. Therefore, they have inherent limitations because the individual patients’ first event can be the outcome of lesser clinical importance. To overcome this limitation, the concept of the win ratio (WR), which accounts for the relative priorities of the components and gives appropriate priority to the more clinically important event, was examined. For example, because mortality has a higher priority than hospitalization, it is reasonable to give a higher priority when obtaining the WR. In this paper, we evaluate three innovative WR methods (stratified matched, stratified unmatched, and unstratified unmatched) for two and multiple components under binary and survival composite endpoints. We compare these methods to traditional ones, including the Cox regression, O’Brien’s rank-sum-type test, and the contingency table for controlling study Type I error rate. We also incorporate these approaches into two-stage enrichment designs with the possibility of sample size adaptations to gain efficiency for rare disease studies.