一种潜在变量方法,用于联合建模纵向和累积事件数据,使用加权双阶段方法
Madeline R Abbott1, Inbal Nahum-Shani2, Cho Y Lam3
1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Statistics in medicine
|July 20, 2024
概括
负面情绪状态增加了吸烟,这是一个新的生态瞬间评估 (EMA) 数据的联合模型所表明的. 该方法分析实时心理状态和吸烟行为,以了解成动态.
科学领域:
- 心理学 心理学 心理学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 生态瞬间评估 (EMA) 在mHealth研究中收集心理,行为和上下文状态的实时数据.
- 欧洲药物管理局的数据有助于理解时间动态和状态和不良健康事件之间的关系.
- 分析纵向EMA数据对于理解像吸烟这样的复杂健康行为至关重要.
研究的目的:
- 提出一个共同的统计模型来分析长度EMA数据.
- 确定隐性心理状态与重复吸烟之间的关联.
- 解决吸烟戒断研究中部分不可观察的预测因素和结果.
主要方法:
- 一个动态因子模型用于纵向子模型来跟踪时间变化的潜状态.
- 累积风险子模型的Poisson回归模型,将潜在状态与事件计数连接起来.
- 一种两阶段的估计方法,使用基于重要性抽样的权重来减轻偏差.
主要成果:
- 拟议的联合模型有效地分析了带有不可观察的组件的纵向EMA数据.
- 重要抽样权重成功降低了累积风险子模型参数中的偏差.
- 超过平均水平的负面情绪强度与增加的吸烟有显著的关联.
结论:
- 开发的联合模型为分析健康研究中复杂的EMA数据提供了可靠的方法.
- 心理状态,特别是负面情绪,在重复吸烟中起着重要作用.
- 这项研究提供了对成机制的见解,并为戒烟干预提供了信息.
更多相关视频
06:52Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
Published on: September 17, 2019
6.3K
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
Parametric Survival Analysis: Weibull and Exponential Methods
402
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
402
Longitudinal Research
11.9K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
11.9K
Actuarial Approach
72
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
72
Assumptions of Survival Analysis
120
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.
120
Survival Tree
78
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...
78
Censoring Survival Data
75
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
75
