神经学试验中普通结果分析的统计实践:一篇文献综述
Yongxi Long1, Sophie C de Ruiter2, Linda W G Luijten3
1Biomedical Data Sciences, Leiden University Medical Center, the Netherlands.
Neurology
|February 3, 2025
概括
神经学试验中的顺序结果的统计方法差异很大. 许多研究对数据进行二分化或使用单个时间点,未能充分利用排名顺序和重复测量来估计治疗效果.
科学领域:
- 神经学 神经学
- 生物统计学 生物统计学
- 临床试验 临床试验
背景情况:
- 顺序尺度是神经学随机对照试验 (RCT) 中常见的结果指标.
- 目前分析这些顺序结果的统计实践在急性神经疾病中缺乏标准化.
研究的目的:
- 评估用于测试和估计治疗效果的统计方法,从最近的神经学RCT中的顺序结果.
- 识别顺序数据分析中的变异,包括处理结果排序,假设,纵向测量和协变量调整.
主要方法:
- 在PubMed系统搜索中风,TBI,SAH,脑膜炎和GBS中的RCT,发表于2015-2023.
- 两个独立的审查员选了研究,并提取了对统计分析的统计数据.
- 评估的重点是如何解决结果的顺序性质,报告假设,使用纵向数据和调整共变量.
主要成果:
- 审查了70个RCT;66%保留了顺序信息,经常使用比例概率模型 (其中72%).
- 31%的试验二分化了顺序结果,只有7%的研究使用纵向数据使用纵向分析.
- 对于比例赔率模型的假设检查经常被遗漏 (62%),在69%的试验中报告了共变量调整.
结论:
- 在分析神经学试验中的顺序结果中存在显著的差异.
- 像二分化和单一时间点分析这样的常见实践不足以利用有价值的结果数据.
- 需要进一步的研究来平衡统计能力,假设和解释,以便有效地使用顺序结果数据.
相关概念视频
Ranks
223
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
223
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
114
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,...
114
Friedman Two-way Analysis of Variance by Ranks
137
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...
137
Odds Ratio
98
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
98
Comparing the Survival Analysis of Two or More Groups
131
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...
131
McNemar's Test
131
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
131
