Pharmaceutical Statistics最新文献

Central statistical monitoring aims at finding centers whose data distribution differs significantly from the other centers in multicentric clinical trials. Such differences may point to data quality issues due to negligence, misconduct, or fraud. Data distributions can be compared across centers in many different ways, depending on the type of data (e.g., numerical or categorical), whether a univariate or a multivariate comparison is performed, and so on. In that framework, we present two methods aimed at detecting centers with outlying bivariate Pearson correlation coefficients. One of the methods directly compares the correlations across centers. The other method conditions the test on one of the marginal standard deviations, which makes the test on correlation independent of the centers' standard deviations. Both methods are shown to perform equally well on simulated data. They are also applied on real world data, where they identify centers with outlying correlations. The findings of the two tests are compared, showing that they concord for centers with average standard deviations, but differ for centers with extreme standard deviations. While the focus here is on central statistical monitoring, the methods are general and can be used in other settings.

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.

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.

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.

Borrowing controls from external sources has become popular for augmenting the control arm in small randomized controlled trials (RCTs). Due to the difference between the external and RCT populations, bias can be introduced that may lead to invalid statistical inference based on combined data. To mitigate this risk, dynamic borrowing which adaptively determines the amount of borrowing, can be used together with pre-adjustment for prognostic factors in the external data. To take into account the variability due to the estimation of the amount of borrowing and the pre-adjustment, we propose a Bayesian bootstrap (BB)-based integrated Bayesian approach together with covariate balancing (CB) for pre-adjustment. We show that the proposed BB based approach is a valid approximate Bayesian approach with CB using different distances, particularly Euclidean or entropy distance. This justification is not trivial because CB has a different nature from the probability-based approach. We also propose a BB-algorithm for generating an approximate posterior sample, which is easy to implement and computationally efficient. Statistical inference for estimand of interest using combined external and internal data can be based on the bootstrapped posterior sample or on an approximate normal distribution with parameters estimated by BB. To examine the properties of the proposed approach, we conduct an extensive simulation study. The approach is illustrated by borrowing controls for an acute myeloid leukemia trial from another 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.

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.

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.

This article proposes a Workflow for Assessing Treatment effeCt Heterogeneity (WATCH) in clinical drug development targeted at clinical trial sponsors. WATCH is designed to address the challenges of investigating treatment effect heterogeneity (TEH) in randomized clinical trials, where sample size and multiplicity limit the reliability of findings. The proposed workflow includes four steps: analysis planning, initial data analysis and analysis dataset creation, TEH exploration, and multidisciplinary assessment. The workflow offers a general overview of how treatment effects vary by baseline covariates in the observed data and guides the interpretation of the observed findings based on external evidence and the best scientific understanding. The workflow is exploratory and not inferential/confirmatory in nature but should be preplanned before database lock and analysis start. It is focused on providing a general overview rather than a single specific finding or subgroup with a differential effect.