因子试验的分析和报告:一个系统的审查
Finlay A McAlister1, Sharon E Straus, David L Sackett
1Division of General Internal Medicine, University of Alberta, Edmonton, Canada. Finlay.McAlister@ualberta.ca
JAMA
|May 22, 2003
概括
因数试验对于测试多种干预措施是有效的. 对44项试验的分析发现很少有显著的相互作用,这表明当治疗不太可能相互作用时,该设计的适当使用.
科学领域:
- 临床试验方法论 临床试验方法论
- 生物统计学 生物统计学
- 基于证据的医学基于证据的医学.
背景情况:
- 实际试验设计在医学研究中越来越常见.
- 缺乏对因子试验的分析和报告的既定标准.
- 在因子试验中,未被识别的治疗相互作用的可能性令人担忧.
研究的目的:
- 审查使用随机因数试验背后的逻辑.
- 检查在因数试验设计和执行中使用的方法.
- 分析用于因数试验数据的统计方法.
主要方法:
- 在多个数据库 (MEDLINE,EMBASE,Cochrane) 进行了全面的文献搜索.
- 利用特定的关键词 (因数,相互作用,2x2) 和手动搜索技术来识别符合条件的试验.
- 包括在2000年1月至2002年7月期间发表的44个具有二元结果的因数试验.
主要成果:
- 大多数试验 (82%) 使用因数设计来提高效率.
- 在报告的相互作用试验中,只有6%的试验具有统计学意义.
- 对每个治疗细胞的数据的透明报告不一致 (66%的试验).
结论:
- 对因数试验的准确解释需要清楚报告所有治疗组数据.
- 显著相互作用的低率表明了对因子设计的适当应用.
- 研究人员在选择干预措施时似乎很谨慎,用于因子试验,而实质性相互作用不太可能发生.
相关概念视频
Factorial Design
13.1K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.1K
Chi-square Analysis
28.9K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
28.9K
Multiple Comparison Tests
3.4K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.4K
Types of Reports I: Hand-off Report
1.5K
A hand-off report, also known as a change-of-shift report, is a crucial nursing process that ensures the smooth transition of patient care responsibilities between nursing staff.
Following are the key components and categories of hand-off reports:
Purpose and Process:
Following are the key components and categories of hand-off reports:
Purpose and Process:
1.5K
System of Forces and Couples
779
In the analysis of structural systems, it is common to encounter members subjected to various forces and couple moments. Simplifying these systems can make the analysis more manageable and easier to understand. One approach to achieve this simplification is by moving a force to a point O that does not lie on its line of action and adding a couple with a moment equal to the moment of the force about point O.
The principle of transmissibility plays a crucial role in this process. According to...
The principle of transmissibility plays a crucial role in this process. According to...
779
Friedman Two-way Analysis of Variance by Ranks
595
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
595


