Use of High-Dimensional Propensity Score with Grid Computing based Immune Algorithm to Improve Confounding Control
碩士 === 國立虎尾科技大學 === 資訊管理研究所 === 102 === Through Taiwan’s Health Claim Database, this study investigated the confounding variable combination with the best potential in the big data based on comparative studies on therapy and effectiveness, thereby modifying bias arising form the confounding covariat...
Main Authors: | Yi-Che Lee, 李宜澤 |
---|---|
Other Authors: | Ta-Cheng Chen |
Format: | Others |
Language: | zh-TW |
Published: |
2014
|
Online Access: | http://ndltd.ncl.edu.tw/handle/rxcu4y |
Similar Items
-
Using High-Dimensional Propensity Score with Immune Algorithm to Automate Optimal Confounding Control in Big Health Care Claim Data
by: Hong-Min Chen, et al.
Published: (2015) -
Bacteremic sepsis leads to higher mortality when adjusting for confounders with propensity score matching
by: Lisa Mellhammar, et al.
Published: (2021-03-01) -
Head to head comparison of the propensity score and the high-dimensional propensity score matching methods
by: Jason R. Guertin, et al.
Published: (2016-02-01) -
Propensity score matching for multilevel spatial data: accounting for geographic confounding in health disparity studies
by: Melanie L. Davis, et al.
Published: (2021-02-01) -
Using propensity score to adjust for unmeasured confounders in small area studies of environmental exposures and health
by: Wang, Yingbo
Published: (2015)