对二进制结果的平均治疗效应与随机共变量
Christoph Kiefer1, Marcella L Woud2, Simon E Blackwell2
1Department of Psychological Methods and Evaluation, Bielefeld University, Bielefeld, Germany.
概括
在RCT中分析具有二元结果的心理治疗时,计算随机共变量可以提高标准错误的准确性. 标准方法可能会低估错误,特别是治疗效果异质性.
科学领域:
- 心理学研究方法论心理学研究方法论
- 生物统计学 生物统计学
- 临床心理学 临床心理学
背景情况:
- 随机对照试验 (RCT) 经常使用逻辑回归来实现二分法结果.
- 平均边际效应 (AME) 提供了比对共变量调整的RCT的赔率比率更清晰的解释.
- 标准AME计算可能会低估标准误差,因为共变量被视为固定的.
研究的目的:
- 在二进制结果模型中比较标准 (固定共变量) 和随机共变量方法来计算AME.
- 通过模拟在有限样本中评估这些方法的统计推理质量.
- 在心理学RCT中提供关于选择合适的共变量处理方法的指导.
主要方法:
- 进行了一项模拟研究,以比较固定共变量和随机共变量方法.
- 该研究的重点是统计推断,特别是对外汇交易员的标准误差估计.
- 用临床心理学的一个说明性例子来展示这些方法.
主要成果:
- 固定的共变量方法只有当治疗效果均时 (没有治疗-共变量相互作用) 才可靠.
- 当个体间治疗效果异质时,随机共变的方法是可取的.
- 在某些条件下,在固定共变量方法下,AME标准错误的低估发生.
结论:
- 随机-共变量方法在心理试验中提供了更准确的标准误差,用于 AME,具有二进制结果的心理试验,特别是治疗效应异质性.
- 仔细考虑共变量采样不确定性对于在RCT中可靠的统计推断至关重要.
- 这些发现对心理干预和临床试验设计的分析有影响.
相关概念视频
01:52Regression Toward the Mean
6.3K
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...
6.3K
01:20Comparing the Survival Analysis of Two or More Groups
170
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...
170
01:13Randomized Experiments
6.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
6.8K
01:09Truncation in Survival Analysis
187
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...
187
01:15Assumptions of Survival Analysis
119
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.
119
01:20Biostatistics: Overview
230
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Discrete variables are...
230