康索特声明:修订了改善平行组随机试验报告质量的建议
D Moher1, K F Schulz, D G Altman
1University of Ottawa, Thomas C Chalmers Centre for Systematic Reviews, Ottawa, Ontario, Canada.
Lancet (London, England)
|April 27, 2001
概括
改善随机对照试验 (RCT) 报告对于理解研究有效性至关重要. 修订后的CONSORT声明提供了一个检查清单和流程图,以提高RCT结果的透明度和解释.
科学领域:
- 临床试验 临床试验
- 医学研究方法学 医学研究方法学
- 基于证据的医学基于证据的医学.
背景情况:
- 准确解释随机对照试验 (RCT) 需要了解其设计,进行,分析和结果.
- 尽管有教育努力,但RCT的报告质量仍然不够优.
- 最初的CONSORT (报告试验综合标准) 声明是为了改善RCT报告而开发的.
研究的目的:
- 提出修订后的CONSORT声明,包括新的证据,并解决对原始声明的批评.
- 提高RCT报告的透明度和清晰度.
- 为了使读者能够更好地评估试验结果的有效性和相关性.
主要方法:
- 开发一个修订后的CONSORT检查清单,包含22个项目.
- 包括对判断治疗效果偏差和发现可靠性至关重要的项目.
- 修订CONSORT流程图,以说明参与者通过四个试验阶段 (注册,分配,跟进,分析) 的进展.
主要成果:
- 修订后的检查清单包括透明RCT报告的基本项目.
- 更新的流程图明确显示了初级数据分析中的参与者数量.
- 修订后的CONSORT声明旨在提高对RCT行为和结果评估的理解.
结论:
- 修订后的CONSORT声明为改善RCT报告提供了一个全面的框架.
- 通过CONSORT检查清单和流程图提高透明度,有助于对试验结果进行批判性评估.
- 遵守CONSORT指南对于确保临床试验证据的可靠性和可解释性至关重要.
相关概念视频
Group Design
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
Random and Systematic Errors
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Testing a Claim about Standard Deviation
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
Randomized Experiments
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...
Random and Systematic Errors
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...

