随机癌症临床试验中基线共变异不平衡的显著性测试的患病率和影响:表1错误
Alexander D Sherry1, Pavlos Msaouel2, Zachary R McCaw3
1Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
概括
"表1谬论",使用随机试验中基线变量比较的显著性测试,在25%的瘤学研究中仍然存在. 这种做法导致对群组平衡的错误结论,强调需要更好的方法来评估随机化质量.
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
- 瘤学研究研究
背景情况:
- "表1谬误"描述了对显著性测试的滥用,以比较随机组之间的基线变量,从而导致对治疗组平衡的错误结论.
- 这种做法在临床研究中很普遍,可能会破坏试验结果的有效性.
研究的目的:
- 为了研究表1错误的发生和预测因子在III期瘤随机对照试验中.
- 评估统计调整对错误检测的影响.
主要方法:
- 一项横截面研究分析了来自ClinicalTrials.gov.gov的765个癌症III期随机对照试验.
- 用多变量逻辑回归来确定与表1谬误相关的因素.
主要成果:
- 表1错误在25%的分析试验中被发现,大约3%的基线比较显示出统计学意义 (未经调整).
- 在应用多重性校正后,显著发现率降至0.3%.
- 与错误可能性较低相关的因素包括行业赞助,更大的试验规模和在欧洲期刊上发表.
结论:
- 表1谬论在当代瘤学试验中仍然是一个问题,四分之一的试验表现出这个问题.
- 显著性测试是评估随机化质量的不充分方法,可能导致误导性解释.
- 实施期刊层面的政策可能有助于减少表1错误的发生率.
相关概念视频
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
133
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,...
133
Significance Testing: Overview
3.4K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
3.4K
Bias in Epidemiological Studies
316
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
316
Comparing the Survival Analysis of Two or More Groups
202
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...
202
Hazard Ratio
138
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
For example, in a clinical trial...
138
Randomized Experiments
7.0K
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...
7.0K


