Pharmaceutical Statistics最新文献

The results of randomized clinical trials (RCTs) are frequently assessed with the fragility index (FI). Although the information provided by FI may supplement the p value, this indicator presents intrinsic weaknesses and shortcomings. In this article, we establish an analysis of fragility within a broader framework so that it can reliably complement the information provided by the p value. This perspective is named the analysis of strength. We first propose a new strength index (SI), which can be adopted in normal distribution settings. This measure can be obtained for both significance and nonsignificance and is straightforward to calculate, thus presenting compelling advantages over FI, starting from the presence of a threshold. The case of time-to-event outcomes is also addressed. Then, beyond the p value, we develop the analysis of strength using likelihood ratios from Royall's statistical evidence viewpoint. A new R package is provided for performing strength calculations, and a simulation study is conducted to explore the behavior of SI and the likelihood-based indicator empirically across different settings. The newly proposed analysis of strength is applied in the assessment of the results of three recent trials involving the treatment of COVID-19.

Precision medicine is the future of drug development, and subgroup identification plays a critical role in achieving the goal. In this paper, we propose a powerful end-to-end solution squant (available on CRAN) that explores a sequence of quantitative objectives. The method converts the original study to an artificial 1:1 randomized trial, and features a flexible objective function, a stable signature with good interpretability, and an embedded false discovery rate (FDR) control. We demonstrate its performance through simulation and provide a real data example.

Conventional Phase I trial designs assign a single dose to each patient, necessitating a minimum number of patients per dose to reliably identify the maximum tolerated dose (MTD). However, in many clinical trials, such as those involving pediatric patients or patients with rare cancers, recruiting an adequate number of patients can pose challenges, limiting the applicability of standard trial designs. To address this challenge, we propose a new Phase I dose-finding design, denoted as IP-CRM, that integrates intra-patient dose escalation with the continual reassessment method (CRM). In the IP-CRM design, intra-patient dose escalation is allowed, guided by both individual patients' toxicity outcomes and accumulated data across patients, and the starting dose for each cohort of patients is adaptively updated. We further extend the IP-CRM design to address carryover effects and/or intra-patient correlations. Due to the potential for each patient to contribute multiple data points at varying doses owing to intra-patient dose escalation, the IP-CRM design offers the advantage of determining the MTD with a considerably reduced sample size compared to standard Phase I dose-finding designs. Simulation studies show that our IP-CRM design can efficiently reduce sample size while concurrently enhancing the probability of identifying the MTD when compared with standard CRM designs and the 3 + 3 design.

In early phase drug development of combination therapy, the primary objective is to preliminarily assess whether there is additive activity from a novel agent when combined with an established monotherapy. Due to potential feasibility issues for conducting a large randomized study, uncontrolled single-arm trials have been the mainstream approach in cancer clinical trials. However, such trials often present significant challenges in deciding whether to proceed to the next phase of development due to the lack of randomization in traditional two-arm trials. A hybrid design, leveraging data from a completed historical clinical study of the monotherapy, offers a valuable option to enhance study efficiency and improve informed decision-making. Compared to traditional single-arm designs, the hybrid design may significantly enhance power by borrowing external information, enabling a more robust assessment of activity. The primary challenge of hybrid design lies in handling information borrowing. We introduce a Bayesian dynamic power prior (DPP) framework with three components of controlling amount of dynamic borrowing. The framework offers flexible study design options with explicit interpretation of borrowing, allowing customization according to specific needs. Furthermore, the posterior distribution in the proposed framework has a closed form, offering significant advantages in computational efficiency. The proposed framework's utility is demonstrated through simulations and a case study.

The risk difference (RD) between the secondary infection, given the primary infection, and the primary infection can be a useful measure of the change in the infection rates of the primary infection and the secondary infection. It plays an important role in pharmacology and epidemiology. The method of variance estimate recovery (MOVER) is used to construct confidence intervals (CIs) for the RD. Seven types of CIs for binomial proportion are introduced to obtain MOVER-based CIs for the RD. The simulation studies show that the Agresti-Coull CI, score method incorporating continuity correction CI, Clopper Pearson CI, and Bayesian credibility CI are conservative. The Jeffreys CI, Wilson score CI, and Arcsin CI draw a satisfactory performance; they are suitable for various practical application scenarios as they can provide accurate and reliable results. To illustrate that the recommended CIs are competitive or even better than other methods, three real datasets were used.

Project FrontRunner encourages development of cancer drugs for advanced or metastatic disease in an earlier clinical setting by promoting regulatory approaches such as the accelerated approval pathway. The FDA draft guideline proposes a one-trial approach to combine accelerated approval and regular approval in a single trial to maintain efficiency. This article describes our idea of controlling Type I error for accelerated and regular approvals in the one-trial approach. We introduce success and futility boundaries on p-values for accelerated approval to create three outcomes: success, RA, and futility. If success, accelerated approval can be claimed for; for RA, only regular approval (RA) is considered; if futility, we stop the trial early for futility. For both success and RA, the endpoint for regular approval can be tested with no penalty on its significance level. The proposed approach is robust to all possible values of correlation between test statistics of the endpoints for accelerated and regular approvals. This framework is flexible to allow clinical trial teams to tailor success and futility boundaries to meet clinical and regulatory needs, while maintaining the overall Type I error control in the strong sense.

Study designs incorporate interim analyses to allow for modifications to the trial design. These analyses may aid decisions regarding sample size, futility, and safety. Furthermore, they may provide evidence about potential differences between treatment arms. Bayesian response adaptive randomization (RAR) skews allocation proportions such that fewer participants are assigned to the inferior treatments. However, these allocation changes may introduce covariate imbalances. We discuss two versions of Bayesian RAR (with and without covariate adjustment for a binary covariate) for continuous outcomes analyzed using change scores and repeated measures, while considering either regression or mixed models for interim analysis modeling. Through simulation studies, we show that RAR (both versions) allocates more participants to better treatments compared to equal randomization, while reducing potential covariate imbalances. We also show that dynamic allocation using mixed models for repeated measures yields a smaller allocation proportion variance while having a similar covariate imbalance as regression models. Additionally, covariate imbalance was smallest for methods using covariate-adjusted RAR (CARA) in scenarios with small sample sizes and covariate prevalence less than 0.3. Covariate imbalance did not differ between RAR and CARA in simulations with larger sample sizes and higher covariate prevalence. We thus recommend a CARA approach for small pilot/exploratory studies for the identification of candidate treatments for further confirmatory studies.

'Treatment effect measures under nonproportional hazards' by Snapinn et al. (Pharmaceutical Statistics, 22, 181-193) recently proposed some novel estimates of treatment effect for time-to-event endpoints. In this note, we clarify three points related to the proposed estimators that help to elucidate their properties. We hope that their work, and this commentary, will motivate further discussion concerning treatment effect measures that do not require the proportional hazards assumption.

The topic of this article is pre-posterior distributions of success or failure. These distributions, determined before a study is run and based on all our assumptions, are what we should believe about the treatment effect if we are told only that the study has been successful, or unsuccessful. I show how the pre-posterior distributions of success and failure can be used during the planning phase of a study to investigate whether the study is able to discriminate between effective and ineffective treatments. I show how these distributions are linked to the probability of success (PoS), or failure, and how they can be determined from simulations if standard asymptotic normality assumptions are inappropriate. I show the link to the concept of the conditional $PoS$ introduced by Temple and Robertson in the context of the planning of multiple studies. Finally, I show that they can also be constructed regardless of whether the analysis of the study is frequentist or fully Bayesian.