Statistics in Biopharmaceutical Research最新文献

Hybrid clinical trials, which borrow real-world data (RWD) from patient registries, claims databases, or electronic health records (EHRs) to augment randomized clinical trials, are of increasing interest. Hybrid clinical trials are especially relevant for rare diseases, where the recruitment of large sample sizes may be challenging. While these trials may better use available information, they assume that the RWD and randomized control arm are exchangeable. Violating this assumption can induce bias, inflate Type I error, or adversely affect statistical power. A two-step hybrid design first tests the exchangeability between randomized control arm and external data sources before incorporating RWD as a comparator for statistical inferences (Yuan et al. 2019). This approach reduces the chance of inappropriate borrowing but may simultaneously inflate the Type I error rate. We propose four different methods to control the Type I error rate under the exchangeability assumption. Approach 1 estimates the variance of the overall test statistic and rejects the null hypothesis based on a Z-test. Approach 2 uses a numerical method to determine the exact critical value for Type I error control. Approach 3 splits the Type I error rates according to the equivalence test outcome. Approach 4 adjusts the critical value only when equivalence is established. We illustrate these methods using a hypothetical scenario in the context of amyotrophic lateral sclerosis (ALS). We evaluate the Type I error and power under various clinical trial conditions in comparison with the Bayesian power prior approach (Ibrahim et al. 2015). We demonstrate that our proposed methods and Bayesian power prior control Type I error and increase power under the exchangeability assumption, whereas the method proposed by Yuan et al. (2019) results in an increased Type I error. In the scenario where the exchangeability assumption does not hold, all methods fail to control the Type I error. Our proposed methods, however, limit a maximum Type I error inflation ranging from 6% to 8%, which compares favorably to 10% for Yuan et al. (2019) and 16% for the Bayesian power prior. All methods increase statistical power under the exchangeability condition but may lead to a loss of statistical power when the exchangeability assumption is violated.

Neurodegenerative disorders such as Alzheimer's disease (AD) present a significant global health challenge, characterized by cognitive decline, functional impairment, and other debilitating effects. Current AD clinical trials often assess multiple longitudinal primary endpoints to comprehensively evaluate treatment efficacy. Traditional methods, however, may fail to capture global treatment effects, require larger sample sizes due to multiplicity adjustments, and may not fully utilize the available longitudinal data. To address these limitations, we introduce the Longitudinal Rank Sum Test (LRST), a novel nonparametric rank-based omnibus test statistic. The LRST enables a comprehensive assessment of treatment efficacy across multiple endpoints and time points without the need for multiplicity adjustments, effectively controlling Type I error while enhancing statistical power. It offers flexibility for various data distributions encountered in AD research and maximizes the utilization of longitudinal data. Simulations across realistic clinical trial scenarios, including those with conflicting treatment effects, and real-data applications demonstrate the LRST's performance, underscoring its potential as a valuable tool in AD clinical trials.

Biomarker enrichment clinical trial designs are versatile tools to assess the treatment effect and increase the efficiency of clinical trials. In this paper, we propose a two-stage enrichment clinical trial design with survival outcomes, and consider the situation where the biomarker assay and classification are possibly subject to errors. Specifically, the first stage is a randomized design, stratified by the biomarker appeared status. Depending on the result of the interim analysis and a pre-specified futility criterion, the second stage can be either enriched with only the biomarker appeared positive patients, or remain as the stratified design with both biomarker appeared positive and biomarker appeared negative patients. Compared to continuous and binary outcomes, test statistics to account for biomarker misclassification are much more complicated and require special care. We develop log-rank statistics for the interim and final analyses, with an adjustment for the sensitivity and specificity of the biomarker assay. Control of Type I error rate is achieved by considering correlations between adjusted log-rank statistics from the same and/or different stages. R code is developed to calculate critical values, global/marginal power, and sample size. Our method is illustrated with examples of a recently successful development of immunotherapy in non-small-cell lung cancer.

Multiregional clinical trials (MRCTs) have become increasingly common in recent years. Detecting underlying regional heterogeneity is a critical issue for these trials. Existing methods for assessing treatment effect heterogeneity across regions have ignored the incomparability of baseline extrinsic risk factors of the randomized patients from different regions. In this paper, a calibration weighting method is proposed to calibrate the distribution of these extrinsic risk factors between multiple regions. We establish the consistency and the asymptotic normality of the calibration weighting estimator. Simulation studies confirm the finite sample properties of the proposed estimator as well as its superior performance over naive methods and the inverse probability weighting method. The proposed method is illustrated using a randomized clinical trial of adjuvant chemotherapy for resected non-small-cell lung cancer.