提高试验信息性:对如何确保试验有用的全球研究进行快速审查
Sarah R Prowse1, Shaun Treweek1, Miriam Brazzelli1
1Aberdeen Centre for Evaluation, University of Aberdeen, Aberdeen, UK.
Journal of evaluation in clinical practice
|June 11, 2025
概括
提高随机对照试验 (RCT) 的信息性可以显著减少研究浪费. 本审查确定了关键的设计流程和工具,以提高RCT的质量和影响.
科学领域:
- 临床研究 临床研究
- 基于证据的医学基于证据的医学.
- 卫生科学 卫生科学 卫生科学
背景情况:
- 随机对照试验 (RCT) 对于临床决策至关重要,但往往存在缺陷.
- 目前的审查流程不足以及早识别和纠正这些缺陷.
- 试验设计,进行和报告问题导致大量的研究浪费.
研究的目的:
- 提供全球研究的见解,以提高RCT的信息性.
- 识别提高试验设计,可行性,完整性和报告的流程和工具.
- 调整结果与扎林及其同事概述的信息性试验的五个关键条件.
主要方法:
- 采用了快速审查方法.
- 专注于研究试验设计流程,以提高信息性.
- 使用NVivo 12进行了主题分析,引用Zarin的五个条件:重要性,设计,可行性,完整性和报告.
主要成果:
- 该审查分析了42个文本,确定了27个建议的流程,以提高试验的信息性.
- 大多数建议 (44%) 涉及试验设计.
- 确定了23种工具,以提高试验信息性,包括重要性 (17%),设计 (74%) 和报告 (9%).
结论:
- 对设计流程的更好理解可以通过提高试验信息性来减少研究浪费.
- 需要进一步的研究来将这些流程纳入预资金对等评审中.
- 实施这些策略增加了产生信息丰富和有影响力的临床试验的可能性.
更多相关视频
08:36Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
Published on: April 19, 2024
456
06:05The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
Published on: February 19, 2021
1.3K
相关概念视频
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
124
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
124
Blinding
2.4K
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
2.4K
Accuracy and Errors in Hypothesis Testing
183
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
183
Statistical Significance
20.1K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
20.1K
Regression 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
Testing a Claim about Population Proportion
3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K
