对于多变量结果的回归树和集合
Evan L Reynolds1, Brian C Callaghan1, Michael Gaies2
1University of Michigan, Ann Arbor, USA.
概括
本研究引入了多变量回归树的新方法,以处理生物医学研究中的相关结果. 这种方法改善了对神经病变等复杂健康状况的数据分析.
科学领域:
- 生物统计学 生物统计学
- 医疗保健中的机器学习
- 数据挖掘 数据挖掘
背景情况:
- 基于树的方法对于复杂的数据分析具有强大作用.
- 生物医学研究经常涉及多变量结果 (例如,多个血压测量).
- 目前的方法不足以解决多变量结果中的相关性.
研究的目的:
- 为多变量回归树开发新的分割良性测量方法.
- 构建树木,有效地处理连续的多变量结果与固有的相关性.
- 通过整体方法提高预测准确度.
主要方法:
- 提出了两种方法:最小化节点内部的同质性和最大化节点之间的分离.
- 使用Mahalanobis距离,变异-共变矩阵的决定数,欧几里德距离和标准化的欧几里德距离进行分割测量.
- 将单个树扩展到多变量树集团,以改善预测.
主要成果:
- 开发和评估了用于多变量回归的新的分割优度指标.
- 模拟表明了拟议措施的特性.
- 这些方法成功地应用于神经病和儿科心脏手术的临床数据集.
结论:
- 这些新方法为分析生物医学研究中相关的多变量结果提供了一个强大的框架.
- 提出的技术增强了基于树的方法在复杂的健康数据分析中的实用性.
- 综合多变量回归树在改善临床研究中的预测准确性方面表现有前途.
相关概念视频
Survival Tree
362
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
362
Multiple Regression
3.7K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.7K
Regression Analysis
7.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.7K
Regression Toward the Mean
6.8K
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...
6.8K
Correlation and Regression
3.0K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.0K
Comparing the Survival Analysis of Two or More Groups
525
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...
525


