Missing value processing method based on multiple fill method and guided by Markov Chain Monte Carlo (MCMC) simulation under Meta analysis numerical value missing state

A technology of missing value and filling method, which is applied in the field of medical statistics to achieve the effect of ensuring integrity and improving statistical efficiency

Inactive Publication Date: 2014-10-08
刘鸿 +1
View PDF2 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The application of missing value processing in the data processing of Meta analysis can ensure the integrity of the extracted data to the greatest extent, and can solve the relatively common problems in missing data, especially when the data is in any missing mode, the MCMC model can be used to deal with it Complicated missing data problems can improve statistical efficiency; it can also effectively avoid the distorted variable distribution caused by the mean filling method, and make the distribution after substitution closer to the true value

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Missing value processing method based on multiple fill method and guided by Markov Chain Monte Carlo (MCMC) simulation under Meta analysis numerical value missing state

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0007] Concrete method of the present invention consists of the following examples and its accompanying figure 1 give.

[0008] attached figure 1 It is a flow chart of the method for MCMC simulation-oriented missing value processing based on multiple imputation methods under the condition of missing values ​​in Meta analysis proposed by the present invention. Combine below figure 1 The specific method proposed according to the present invention will be described in detail.

[0009] (1) The Markov Monte Carlo (MCMC)-oriented multiple imputation method mainly includes three key steps: estimating the target estimator (that is, an estimate of the research variable), embedding the value of the imputation, creating a complete data set (i.e., mainly completes the estimation of the offset value), and combines the estimation results of the target estimator. Among them, the estimation of the supplementary value of the missing data is the key to the estimation of the target estimator...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a missing value processing method based on a multiple fill method and guided by Markov Chain Monte Carlo (MCMC) simulation under a Meta analysis numerical value missing state. The missing value processing method is characterized in that the Bayesian probability theory serves as the base point of the missing value processing method, the method is guided by the MCMC random simulation method, based on the multiple fill method, and applied to the Meta analysis data missing state of any missingness pattern, so that integrated integration of missing value processing and missing data sets in conventional Meta analysis is achieved. By means of the method, the missing data can be fitted to the maximum degree, so that integrity of the extracted data and feasibility of subsequent standard statistic are guaranteed as much as possible, statistical test effectiveness of Meta analysis is remarkably promoted, robustness and possibility of a system evaluation result are improved, and scientificity and comprehensiveness of system overview evaluation are enhanced.

Description

technical field [0001] The invention relates to the field of medical statistics, in particular to an MCMC simulation-oriented method for processing missing values ​​based on multiple imputation methods in the state of missing values ​​in Meta analysis. Background technique [0002] In the field of biomedicine, Meta-analysis is the systematic analysis and quantitative synthesis of the results of multiple independent small-sample clinical trials and basic experimental research with the same research purpose, in order to improve the efficiency of statistical tests and increase the accuracy of effect value estimation. Resolve inconsistencies in findings across studies and seek new hypotheses. Meta-analysis is used in the evaluation and optimization of the accuracy of clinical diagnostic techniques, the evaluation and optimization of clinical treatment effects, the evaluation of causal associations in etiology, the evaluation of disease prevention interventions, the cost-benefit ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F19/00
Inventor 刘鸿
Owner 刘鸿
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap