联合数据的线性混合建模,当只有平均值,共差值和样本大小可用时
Marie Analiz April Limpoco1, Christel Faes1, Niel Hens1,2
1Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute (DSI), Hasselt University, Hasselt, Belgium.
Statistics in medicine
|December 11, 2024
概括
这项研究引入了一种用于分析患者数据的新方法,而不影响机密性. 该方法有效地估计了仅使用总结统计数据的统计模型,确保数据隐私和准确的结果.
科学领域:
- 医学统计 医学统计
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 患者数据的保密性在医学研究中至关重要.
- 使用分散数据的统计建模带来隐私挑战.
- 联合学习提供了一个解决方案,但需要代沟通.
研究的目的:
- 为合适线性混合模型提出联合学习的替代框架.
- 为了实现准确的统计分析,同时保持患者的保密性.
- 开发一种通讯效率高的方法来进行分散的数据分析.
主要方法:
- 开发了一个新的框架,利用来自数据提供商的总结统计数据 (平均值,共差,样本大小).
- 在概率框架内利用统计充分性原则.
- 应用该方法以使用真实患者数据建模COVID-19 PCR测试周期值.
主要成果:
- 获得的估计与来自个人级数据的估计相同.
- 在70家诊所的15068名患者记录上展示了这种方法.
- 该方法只需要每位数据提供者一次提交总结统计数据.
结论:
- 拟议的方法为线性混合模型提供了比联合学习更简单,更有效的沟通替代方案.
- 它通过仅共享汇总总要点统计数据来确保患者数据的机密性.
- 该框架是可概括的,可以在各种统计软件中实现.
相关概念视频
One-Way ANOVA: Equal Sample Sizes
3.2K
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...
3.2K
Friedman Two-way Analysis of Variance by Ranks
146
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...
146
One-Way ANOVA: Unequal Sample Sizes
5.7K
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:
5.7K
Estimating Population Mean with Unknown Standard Deviation
7.6K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
7.6K
Estimating Population Mean with Known Standard Deviation
8.3K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis
27
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
27


