随机试验中的多重性II:子组和中间分析
Kenneth F Schulz1, David A Grimes
1Family Health International, PO Box 13950, Research Triangle Park, NC 27709, USA. KSchulz@fhi.org
Lancet (London, England)
|May 12, 2005
概括
小组分析和中间分析可以膨胀假阳性率. 使用统计互动测试或组序列方法,如奥布莱恩-弗莱明,以管理多重性问题,并保持研究完整性.
科学领域:
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
- 医学研究方法学 医学研究方法学
背景情况:
- 临床试验中的子组分析和中间分析存在重大多重性问题.
- 不受控制的子组分析可以导致虚假的发现,并通过选择性地报告显著结果来扭曲医学文献.
- 在没有适当的统计调整的情况下,重复的中间分析会增加假阳性错误的风险.
研究的目的:
- 解决临床试验中与子组分析和中间分析相关的多重性问题.
- 推适当的统计方法进行必要的子组分析和中间分析.
- 突出维护统计能力和控制假阳性错误率的重要性.
主要方法:
- 阻止常规的小组分析,主张在小组调查至关重要时进行互动的统计测试.
- 建议使用统计停止方法,如O'Brien-Fleming和Peto组顺序方法,进行中间分析.
- 强调需要考虑多重性,以防止不断升级的假阳性错误率.
主要成果:
- 如果进行子组分析,应使用相互作用测试,而不是单独分析每个子组.
- 组次序停止方法在中间分析期间有效地保持预期的α水平和统计功率.
- 由于使用这些方法明显的治疗优势,提前终止试验可能会导致夸大治疗效果估计.
结论:
- 由于多重性问题,应谨慎对待子组分析;相互作用测试是首选的.
- 统计停止方法对于管理中间分析中的多重性至关重要,确保试验完整性.
- 虽然早期停止治疗可能是有益的,但研究人员和读者必须意识到对治疗效果的潜在高估.
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...
Introduction to Test of Independence
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
One-Way ANOVA: Equal Sample Sizes
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
One-Way ANOVA: Unequal Sample Sizes
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
Two-Way ANOVA
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
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...


