用临床试验进行预测驱动的推理
Pierre-Emmanuel Poulet1, Maylis Tran1, Sophie Tezenas du Montcel1
1Inria Aramis project-team, Paris Brain Institute, Inserm U 1127, CNRS UMR 7225, Assistance Publique - Hopitaux de Paris, Sorbonne University F-75013, Paris, France.
medRxiv : the preprint server for health sciences
|January 27, 2025
概括
预测推断 (PPI) 通过为患者创建数字双胞胎,减少样本大小和对照组需求来增强临床试验. 该方法利用机器学习进行统计学上有效的治疗效果估计.
科学领域:
- 生物统计学 生物统计学
- 临床试验 临床试验
- 机器学习 机器学习
背景情况:
- 临床试验中的传统统计估计方法可能是低效的.
- 利用机器学习进行预测推断 (PPI) 提供了一种新的方法.
- 现有的PPI方法可以用于临床试验应用.
研究的目的:
- 在临床试验的背景下引入和评估预测驱动的推理加加 (PPI++).
- 展示PPI++如何提供统计学上有效的治疗效果估计.
- 探索PPI++对优化临床试验设计和资源配置的影响.
主要方法:
- 利用疾病进展模型,根据基线共变量,为参与者生成预后得分.
- 应用PPI范式来创建治疗患者的"数字双胞胎",与未经治疗的对照进行比较.
- 对估计器的属性进行理论分析,包括非对称的公正性和方差推导.
- 执行模拟以验证理论发现.
主要成果:
- 拟议的PPI++估计器对于平均治疗效应是不对称的.
- 已经得出了PPI++估计器方差的明确公式.
- 模拟证实了该方法的理论特性和实际实用性.
- 在阿尔茨海默病临床试验中的应用表明了显著的样本大小减少潜力.
结论:
- PPI++提供了一个统计学上有效的方法,将机器学习预测纳入临床试验分析中.
- 这种方法可以导致更有效的临床试验,需要更小的样本大小和更少的对照.
- PPI++ 便于将大规模疾病预测模型直接应用于临床试验环境.
- 该方法有望提高临床研究的效率和可行性,特别是在阿尔茨海默氏症等复杂疾病中.
更多相关视频
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
7.0K
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.2K
相关概念视频
01:16Clinical Trials
6.6K
Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
There are four phases in a clinical trial. A phase one...
6.6K
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
115
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,...
115
01:12Hazard Ratio
79
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...
79
01:24Testing a Claim about Population Proportion
3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K
01:03Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K
01:13Sensitivity, Specificity, and Predicted Value
174
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
174