四位数项在估计长度随机对照试验中平均治疗效果的作用,这些试验中缺少数据
Manshu Yang1, Lijuan Wang2, Scott E Maxwell2
1Department of Psychology, University of Rhode Island.
Psychological methods
|December 12, 2024
概括
将直线增长模型与二次变化的纵向RCT数据相匹配,可以偏差平均治疗效果 (ATE) 估计. 二次增长模型,特别是QG-ASR,在心理学研究中为ATE估计提供了更高的准确性.
科学领域:
- 心理学研究方法论心理学研究方法论
- 临床试验中的统计建模.
背景情况:
- 纵向随机对照试验 (RCT) 对于评估心理干预至关重要.
- 治疗结果可以呈现直线或曲线轨迹.
- 缺少的数据在纵向研究中很常见.
研究的目的:
- 在使用直线增长 (SLG) 模型对二次增长数据进行估计时,评估平均治疗效应 (ATE) 估计的偏差.
- 评估缺失数据 (MCAR/MAR) 对ATE估计的影响.
- 为了确定是否纳入二次数项可以改善ATE估计和推断.
主要方法:
- 一项模拟研究比较了四种模型:SLG,QG-AIF,QG-ASF和QG-ASR.
- 模型被评估了它们在纵向RCT数据中估计ATE的能力,具有二次增长模式.
- 数据与完全随机缺失 (MCAR) 和随机缺失 (MAR) 条件模拟.
主要成果:
- 当SLG模型应用于二次增长数据时,在ATE估计中产生了显著的偏差,即使使用了MCAR/MAR.
- 带有臂特异和随机二次效应 (QG-ASR) 的二次增长模型在四个或更多数据波中表现出卓越的性能.
- 对于三波数据,QG-ASR不适用,而QG-ASF模型表现良好.
结论:
- 标准SLG模型对于呈现二次增长的纵向RCT数据是不够的.
- 二次增长模型,特别是QG-ASR和QG-ASF,提供了更准确的ATE估计.
- 模型的选择取决于可用于分析的数据波的数量.
相关概念视频
Truncation in Survival Analysis
179
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
179
Assumptions of Survival Analysis
111
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.
111
Residuals and Least-Squares Property
7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
Actuarial Approach
68
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,...
68
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
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


