机器学习方法用于高维数据中的倾向性和疾病风险得分估计:等离子模拟和现实数据队列分析
Yuchen Guo1, Victoria Y Strauss2, Martí Català1
1Pharmaco- and Device Epidemiology Group, Centre of Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, United Kingdom.
Frontiers in pharmacology
|November 12, 2024
概括
机器学习 (ML) 方法对倾向性得分 (PS) 估计具有前景,极端梯度增强优于其他方法. 使用ML的疾病风险评分 (DRS) 方法的效果不如PS方法.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 倾向性得分 (PS) 和疾病风险得分 (DRS) 的估计对于观察性研究中的因果推断至关重要.
- 机器学习 (ML) 为PS估计提供了可扩展的替代方案,但其在DRS估计中的性能尚未得到充分理解.
研究的目的:
- 将ML方法的性能与PS和DRS估计的传统后勤回归进行比较.
- 用现实数据和等离子模拟来评估ML方法.
主要方法:
- 一项对632,201名英国初级保健患者的队列研究,比较抗高血压药物的使用者和非使用者.
- 用合成数据进行等离子模拟,以评估偏差和共变量平衡.
- 四种方法的比较:逻辑回归 (参考),LASSO,多层感知器 (MLP) 和极端梯度增强 (XgBoost).
主要成果:
- 在共变量平衡和偏差方面,ML方法,特别是XgBoost,通常优于PS估计的参考逻辑回归.
- 在所有情景中,疾病风险评分 (DRS) 估计方法的表现比PS估计方法要差.
- 在评估的PS估计ML方法中,XgBoost表现最高.
结论:
- 在观察性研究中,ML方法是倾向性得分 (PS) 估计的可靠替代方案.
- 基于ML的DRS方法的效果低于PS方法,可能是由于结果罕见.
- 这些发现支持使用先进的ML技术来改善因果推断.
相关概念视频
Mechanistic Models: Compartment Models in Individual and Population Analysis
29
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...
29
Statistical Methods for Analyzing Epidemiological Data
308
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
308
Kaplan-Meier Approach
100
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
100
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
60
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
60
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
121
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,...
121
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
42
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
42


