适应性选择最佳策略,以提高随机试验中的精度和功率
Laura B Balzer1, Erica Cai2, Lucas Godoy Garraza3
1Division of Biostatistics, University of California Berkeley, Berkeley, CA 94720, United States.
Biometrics
|March 6, 2024
概括
在随机试验中调整基线变量可以提高准确性. 新的方法自动选择最佳的调整策略,提高效率,减少样本大小要求,同时保持统计准确性.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 统计学学习 统计学学习
背景情况:
- 随机试验中对基线共变量进行调整是提高精度的长期做法.
- 像FDA和EMA这样的监管机构支持共变量调整.
- 以前的方法,如针对性最大概率估计 (TMLE) 中的自适应预规范,仅限于小型试验和简单模型.
研究的目的:
- 开发和评估一种改进的自适应预规范方法,用于在大型随机试验中选择共变量调整策略.
- 为了最大限度地提高统计准确度,同时确保I型错误控制.
- 将现代机器学习方法纳入灵活的协变量调整.
主要方法:
- 使用V倍交验证和估计的影响曲线平方作为模型选择的损失函数.
- 扩大了候选调整策略的集合,包括机器学习方法和多重共变量.
- 为大量随机单位的试验量身定制了自适应预规范方法.
主要成果:
- 拟议的方法在零假设下保持了I型错误控制.
- 在精度方面取得了显著的收益,相当于对同等统计功率的样本大小减少了20%-43%.
- 当应用到ACTG Study 175中的真实数据时,观察到有意义的效率改善,包括在子组内.
结论:
- 改进的自适应预规范方法为大型随机试验中的共变量调整提供了强大而高效的方法.
- 这种方法在统计准确度和样本大小缩小方面取得了显著的改进.
- 该方法通过模拟和现实世界数据应用得到验证,支持其实际实用性.
关键词:
TMLE TMLE TMLE TMLE TMLE TMLE TMLE TMLE TMLE同变量调整的调整.效率 效率 效率 效率 效率 效率 效率机器学习是机器学习.预规格预规格 预规格预规格随机化试验是一种随机化试验.更多相关视频
08:55Determining and Controlling External Power Output During Regular Handrim Wheelchair Propulsion
Published on: February 5, 2020
7.5K
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
14.5K
相关概念视频
Randomized Experiments
6.9K
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...
6.9K
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
Strategies for Assessing and Addressing Confounding
100
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
100
Accuracy and Errors in Hypothesis Testing
199
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%...
199
Group Design
8.9K
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...
8.9K
Uncertainty in Measurement: Accuracy and Precision
73.7K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
73.7K
