平衡效率和计算负担:权重平均值,多重归算和反向概率权重方法,用于可靠的尺度中对项目的不响应进行权衡
Andrew Guide1, Shawn Garbett1, Xiaoke Feng1
1Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203-2158, United States.
Journal of the American Medical Informatics Association : JAMIA
|August 14, 2024
概括
对于具有高可靠性的调查尺度,加权平均值 (WMean) 是处理项目不响应的计算效率高的方法,避免了多重归算 (MI) 所需的大量资源. 这种方法适用于当缺少数据最小时的"我们所有人"研究计划.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
- 调查方法 调查方法
背景情况:
- 项不响应是多项量表中常见的挑战,影响数据准确性.
- 像加权平均值 (WMean) 这样的传统方法可能无法完全捕捉缺失的数据复杂性,而多重归算 (MI) 会增加计算需求.
- 我们所有人的研究计划利用了大量的调查数据,需要有效的方法来处理缺失的响应.
研究的目的:
- 评估WMean,MI和反向概率权重 (IPW) 的有效性和计算成本之间的权衡,以解决调查尺度中的项目非响应问题.
- 确定MI的计算负担是否合理,以处理我们所有人的研究计划中缺少的数据.
主要方法:
- 在我们所有人的数据集中使用了5项体力活动邻里环境量表 (PANES).
- 合成缺失在不同的缺失数据机制下以不同比例 (10%-50%) 引入.
- 基于偏差,可变性,覆盖概率和计算时间,比较了WMean,MI和IPW方法.
主要成果:
- 所有方法都显示了具有良好的内部一致性尺度的最小偏差 (<5.5%).
- 反向概率加权 (IPW) 显示出更高的变化,缺失数据的百分比更高.
- 多重归算 (MI) 需要更多的计算资源,比WMean慢8000倍,比IPW慢100倍.
结论:
- 在可靠的尺度上,MI对项目不响应的边际好处不能证明其在像我们所有人这样的大规模研究中的高计算成本.
- 对于低项目非响应的调查规模,WMean提供了一个实用且计算效率高的替代方案.
- 研究人员应该考虑WMean,以减少在调查研究中处理最小缺失数据时的计算负担.
相关概念视频
Surveys
14.7K
Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
14.7K
Reliability and Validity
12.7K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.7K
Systematic Error: Methodological and Sampling Errors
1.4K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.4K
Testing a Claim about Standard Deviation
2.4K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.4K
Uncertainty in Measurement: Accuracy and Precision
73.6K
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.
73.6K
Response Surface Methodology
102
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
The process of RSM involves several key steps:
102


