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.

When multiple historical controls are available, it is necessary to consider the conflicts between current and historical controls and the relationships among historical controls. One of the assumptions concerning the relationships between the parameters of interest of current and historical controls is known as the "Potential biases." Within the "Potential biases" assumption, the differences between the parameters of interest of the current control and of each historical control are defined as "potential bias parameters." We define a class of models called "potential biases model" that encompass several existing methods, including the commensurate prior. The potential bias model incorporates homogeneous historical controls by shrinking the potential bias parameters to zero. In scenarios where multiple historical controls are available, a method that uses a horseshoe prior was proposed. However, various other shrinkage priors are also available. In this study, we propose methods that apply spike-and-slab, Dirichlet-Laplace, and spike-and-slab lasso priors to the potential bias model. We conduct a simulation study and analyze clinical trial examples to compare the performances of the proposed and existing methods. The horseshoe prior and the three other priors make the strongest use of historical controls in the absence of heterogeneous historical controls and reduce the influence of heterogeneous historical controls in the presence of a few historical controls. Among these four priors, the spike-and-slab prior performed the best for heterogeneous historical controls.

The primary purpose of an oncology dose-finding trial for novel anticancer agents has been shifting from determining the maximum tolerated dose to identifying an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. In 2022, the FDA Oncology Center of Excellence initiated Project Optimus to reform the paradigm of dose optimization and dose selection in oncology drug development and issued a draft guidance. The guidance suggests that dose-finding trials include randomized dose-response cohorts of multiple doses and incorporate information on pharmacokinetics (PK) in addition to safety and efficacy data to select the OD. Furthermore, PK information could be a quick alternative to efficacy data to predict the minimum efficacious dose and decide the dose assignment. This article proposes a model-based trial design for dose optimization with a randomization scheme based on PK outcomes in oncology. A simulation study shows that the proposed design has advantages compared to the other designs in the percentage of correct OD selection and the average number of patients assigned to OD in various realistic settings.

With the development of targeted therapy, immunotherapy, and antibody-drug conjugates (ADCs), there is growing concern over the "more is better" paradigm developed decades ago for chemotherapy, prompting the US Food and Drug Administration (FDA) to initiate Project Optimus to reform dose optimization and selection in oncology drug development. For early-phase oncology trials, given the high variability from sparse data and the rigidity of parametric model specifications, we use Bayesian dynamic models to borrow information across doses with only vague order constraints. Our proposed adaptive design simultaneously incorporates toxicity and efficacy outcomes to select the optimal dose (OD) in Phase I/II clinical trials, utilizing Bayesian model averaging to address the uncertainty of dose-response relationships and enhance the robustness of the design. Additionally, we extend the proposed design to handle delayed toxicity and efficacy outcomes. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed method under various practical scenarios. The results demonstrate that the proposed designs have desirable operating characteristics. A trial example is presented to demonstrate the practical implementation of the proposed designs.

An informed estimate of subject-level variance is a key determinate for accurate estimation of the required sample size for clinical trials. Evaluating completed adult Type 2 diabetes studies submitted to the FDA for accuracy of the variance estimate at the planning stage provides insights to inform the sample size requirements for future studies. From the U.S. Food and Drug Administration (FDA) database of new drug applications containing 14,106 subjects from 26 phase 3 randomized studies submitted to the FDA in support of drug approvals in adult type 2 diabetes studies reviewed between 2013 and 2017, we obtained estimates of subject-level variance for the primary endpoint-change in glycated hemoglobin (HbA1c) from baseline to 6 months. In addition, we used nine additional studies to examine the impact of clinically meaningful covariates on residual standard deviation and sample size re-estimation. Our analyses show that reduced sample sizes can be used without interfering with the validity of efficacy results for adult type 2 diabetes drug trials. This finding has implications for future research involving the adult type 2 diabetes population, including the potential to reduce recruitment period length and improve the timeliness of results. Furthermore, our findings could be utilized in the design of future endocrinology clinical trials.

In pre-clinical and medical quality control, it is of interest to assess the stability of the process under monitoring or to validate a current observation using historical control data. Classically, this is done by the application of historical control limits (HCL) graphically displayed in control charts. In many applications, HCL are applied to count data, for example, the number of revertant colonies (Ames assay) or the number of relapses per multiple sclerosis patient. Count data may be overdispersed, can be heavily right-skewed and clusters may differ in cluster size or other baseline quantities (e.g., number of petri dishes per control group or different length of monitoring times per patient). Based on the quasi-Poisson assumption or the negative-binomial distribution, we propose prediction intervals for overdispersed count data to be used as HCL. Variable baseline quantities are accounted for by offsets. Furthermore, we provide a bootstrap calibration algorithm that accounts for the skewed distribution and achieves equal tail probabilities. Comprehensive Monte-Carlo simulations assessing the coverage probabilities of eight different methods for HCL calculation reveal, that the bootstrap calibrated prediction intervals control the type-1-error best. Heuristics traditionally used in control charts (e.g., the limits in Shewhart c- or u-charts or the mean ± 2 SD) fail to control a pre-specified coverage probability. The application of HCL is demonstrated based on data from the Ames assay and for numbers of relapses of multiple sclerosis patients. The proposed prediction intervals and the algorithm for bootstrap calibration are publicly available via the R package predint.

'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.

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.

Immunotherapies and targeted therapies have gained popularity due to their promising therapeutic effects across multiple treatment areas. The focus of early phase dose-finding clinical trials has shifted from finding the maximum tolerated dose (MTD) to identifying the optimal biological dose (OBD), which aims to balance the toxicity and efficacy outcomes, thus optimizing the risk-benefit trade-off. These trials often collect multiple pharmacokinetics (PK) outcomes to assess drug exposure, which has shown correlations with toxicity and efficacy outcomes but has not been utilized in the current dose-finding designs for OBD selection. Moreover, PK outcomes are usually available within days after initial treatment, much faster than toxicity and efficacy outcomes. To bridge this gap, we introduce the innovative model-assisted PKBOIN-12 design, which enhances BOIN12 by integrating PK information into both the dose-finding algorithm and the final OBD determination process. We further extend PKBOIN-12 to TITE-PKBOIN-12 to address the challenges of late-onset toxicity and efficacy outcomes. Simulation results demonstrate that PKBOIN-12 more effectively identifies the OBD and allocates a greater number of patients to it than BOIN12. Additionally, PKBOIN-12 decreases the probability of selecting inefficacious doses as the OBD by excluding those with low drug exposure. Comprehensive simulation studies and sensitivity analysis confirm the robustness of both PKBOIN-12 and TITE-PKBOIN-12 in various scenarios.