Zobrazeno 1 - 10
of 242
pro vyhledávání: '"multiple imputations"'
Autor:
Mina Jahangiri, Anoshirvan Kazemnejad, Keith S. Goldfeld, Maryam S. Daneshpour, Shayan Mostafaei, Davood Khalili, Mohammad Reza Moghadas, Mahdi Akbarzadeh
Publikováno v:
BMC Medical Research Methodology, Vol 23, Iss 1, Pp 1-20 (2023)
Abstract Background Missing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to address this issue. In this study, for the first time, the function of
Externí odkaz:
https://doaj.org/article/3486fad2c16c411a95e9f947923fd4d0
Autor:
Naasegnibe Kuunibe
Publikováno v:
Cogent Public Health, Vol 10, Iss 1 (2023)
Abstract: There is increased availability of routine secondary data globally in many health facilities due to technological advancement and the growth in electronic records. Such data present the opportunity for their use when evaluating health syste
Externí odkaz:
https://doaj.org/article/a3bbfce9da6649bf9e82a90f0048506f
Publikováno v:
BMC Research Notes, Vol 16, Iss 1, Pp 1-5 (2023)
Abstract Purpose this study was conducted to assess the impact of AIs on body mass index and high sensitivity as prognostic predictors to be incorporated into point of care technology (POCT) testing in postmenopausal breast cancer women after a 24 mo
Externí odkaz:
https://doaj.org/article/d99d5ce2c8d442f2ab2f1fd8631f5fe3
Autor:
Albæk, Martin, author, Andersen, Torben Juul, author
Publikováno v:
Strategic Responses for a Sustainable Future: New Research in International Management
Autor:
Adusei Bofa, Temesgen Zewotir
Publikováno v:
Lithuanian Journal of Statistics, Vol 61 (2022)
Our study presents the methods adopted to produce accurate imputed values for Africa's food security and nutrition (FSN). We focused primarily on the following five imputation methods for handling missing data: Mean Imputation; Multiple Imputed value
Externí odkaz:
https://doaj.org/article/9af951d1deb64c5e8f778e442a2ceff1
Akademický článek
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Publikováno v:
Vietnam Journal of Computer Science, Vol 7, Iss 2, Pp 161-177 (2020)
One major characteristic of data is completeness. Missing data is a significant problem in medical datasets. It leads to incorrect classification of patients and is dangerous to the health management of patients. Many factors lead to the missingness
Externí odkaz:
https://doaj.org/article/eaa2dba0acad41d199de1460880d038c
Autor:
Ryung S. Kim, Viswanathan Shankar
Publikováno v:
BMC Medical Research Methodology, Vol 20, Iss 1, Pp 1-10 (2020)
Abstract Background Electronic Health Records (EHR) has been increasingly used as a tool to monitor population health. However, subject-level errors in the records can yield biased estimates of health indicators. There is an urgent need for methods t
Externí odkaz:
https://doaj.org/article/5a47df556a87421eb221b29f21e624e3
Autor:
N. V. Kovtun, A.-N. Ya. Fataliieva
Publikováno v:
Статистика України, Vol 87, Iss 4, Pp 4-13 (2019)
The main reasons for omissions are: 1. Exclusion of the subject from the study due to non-compliance with study requirements; 2. The occurrence of an adverse event; 3. Missing result; 4. Lack of registration; 5. Researchers’ act of omission and / o
Externí odkaz:
https://doaj.org/article/c6f130bdec354037b80b3b16c4341bed
Publikováno v:
Neural Regeneration Research, Vol 14, Iss 4, Pp 713-720 (2019)
Some studies have suggested that early surgical treatment can effectively improve the prognosis of cervical spinal cord injury without radiological abnormality, but no research has focused on the development of a prognostic model of cervical spinal c
Externí odkaz:
https://doaj.org/article/1809a14b7ff74e1f8e1ac5169c2c640d