衍生结果的间接建模:小的预测差异是否令人担忧?
1Pharmacometrics and Systems Pharmacology, Pfizer, Groton, Connecticut, USA.
CPT: pharmacometrics & systems pharmacology
|August 14, 2024
概括
预测模型对衍生结果的检查,比如从基线变化,不应该过度审查. 模拟显示直接和衍生数据检查往往无关,建议在评估仅基于衍生措施的模型时谨慎行事.
科学领域:
- 药理计量和统计建模.
- 模型验证和预测检查
- 在临床试验中的数据分析.
背景情况:
- 模型开发通常旨在预测数据的衍生结果,例如基线变化 (CFB).
- 衍生结果预测通过视觉或数值预测检查 (V/NPC) 作为模型错误规范的有价值测试.
- 导出结果的V/NPC可能面临过度审查,可能导致拒绝适当的模型.
研究的目的:
- 研究直接建模数据的预测检查与衍生结果之间的关系.
- 通过使用模拟来确定V/NPC对衍生结果的预期行为.
- 为模型开发中的V/NPC解释提供指导,特别是对于衍生措施.
主要方法:
- 来自简单的前后研究和药理动力学/药理动力学剂量范围研究的模拟数据.
- 评估了牛皮暴露-反应模型案例研究.
- 在原始数据,CFB数据和安慰剂校正的CFB (dCFB) 数据上生成的V/NPC.
- 对模型支持 (强/弱) 的观测数据的分级内置总结统计.
主要成果:
- 在所有模拟和案例研究中,直接数据V/NPC的支持强度与衍生结果V/NPC的支持强度的关系很小.
- 模拟和分析了V/NPC中对于衍生结果的明显问题.
- 模型支持的分级表明直接和衍生数据检查之间的不同行为.
结论:
- 在抛弃基于过于严格的衍生测量预测检查的模型时,建议谨慎.
- 建模衍生指标的基础数据提供了显著的好处.
- 这些发现表明,直接和衍生数据V/NPC评估模型性能的不同方面,应该相应地解释.
相关概念视频
Mechanistic Models: Compartment Models in Individual and Population Analysis
33
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
33
Modeling and Similitude
257
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
257
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Variation
6.8K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
6.8K
Hindsight Biases
3.4K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
3.4K
Observational Learning
155
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
155


