一个自适应的三臂比较临床终点生物等价性研究设计,采用未蒙蔽的样本大小重新估计和优化分配比率
1FDA/CDER/OTS/OB/DBVIII, Silver Spring, Maryland, USA.
Pharmaceutical statistics
|October 8, 2024
概括
这项研究引入了一种新的自适应性临床终点生物等价性 (BE) 设计. 它优化了样本规模,降低了成本,并提高了仿制药开发的成功率.
科学领域:
- 药理动力学和药物开发
- 临床试验设计 临床试验设计
- 生物统计学 生物统计学
背景情况:
- 传统的三臂生物等价性 (BE) 研究将仿制 (T) 与参考 (R) 药物相比较于安慰剂 (P) 可能由于不确定的参数而不足或过度.
- 固定的研究设计可能导致失败或增加成本,影响仿制药的可获得性.
研究的目的:
- 提出一种新的两阶段适应性临床终点BE研究设计.
- 提高效率,降低成本,提高仿制药生物等价性研究的成功率.
主要方法:
- 拟议的设计包括无盲目样本大小重新估计和优化分配比率.
- 它包括在中间分析时可选的效果大小和治疗平均值 (R和P) 的重新估计.
- 该方法在分析上保证了对1型错误率的控制.
主要成果:
- 适应性设计减少了超级研究的平均样本大小,并增加了超级研究的样本大小.
- 与固定或组序列设计相比,它提高了统计能力.
- 该方法通过可选的重估关键参数提供了灵活性.
结论:
- 拟议的适应性BE研究设计为固定设计提供了更有效和更具成本效益的替代方案.
- 这种方法可以提高生物等价性研究的成功率,促进更快的仿制药批准.
- 最终,这有助于使通用药物变得更加负担得起,并使公众更容易获得.
更多相关视频
07:25In Vitro Methods for Comparing Target Binding and CDC Induction Between Therapeutic Antibodies: Applications in Biosimilarity Analysis
Published on: May 4, 2017
17.7K
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
14.4K
相关概念视频
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
123
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
123
Hazard Ratio
91
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
For example, in a clinical trial...
91
Comparing the Survival Analysis of Two or More Groups
156
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
156
Assumptions of Survival Analysis
101
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
101
