Your browser doesn't support javascript.
loading
[Missing Data Replacement Methods in Different Scenarios].
Qiu, Jian-Qing; Zhou, Yu-Qiu; Yue, Ting-Yan; Pei, Jiao; Shui, Chun-Yan; Li, Xiao-Song; Zhang, Tao.
Afiliação
  • Qiu JQ; Department of Epidemiology and Biostatistics,West China School of Public Health,Sichuan University,Chengdu 610041,China.
  • Zhou YQ; Sichuan Cancer Hospital & Institute,Sichuan Cancer Center,School of Medicine,University of Electronic Science and Technology of China, Chengdu 610041,China.
  • Yue TY; Department of Epidemiology and Biostatistics,West China School of Public Health,Sichuan University,Chengdu 610041,China.
  • Pei J; Sichuan Cancer Hospital & Institute,Sichuan Cancer Center,School of Medicine,University of Electronic Science and Technology of China, Chengdu 610041,China.
  • Shui CY; Sichuan Cancer Hospital & Institute,Sichuan Cancer Center,School of Medicine,University of Electronic Science and Technology of China, Chengdu 610041,China.
  • Li XS; Department of Epidemiology and Biostatistics,West China School of Public Health,Sichuan University,Chengdu 610041,China.
  • Zhang T; Department of Epidemiology and Biostatistics,West China School of Public Health,Sichuan University,Chengdu 610041,China.
Sichuan Da Xue Xue Bao Yi Xue Ban ; 49(3): 430-435, 2018 May.
Article em Zh | MEDLINE | ID: mdl-30014648
OBJECTIVE: To compare the effect of different approaches of missing data replacement on the regression coefficient estimates r of "length of stay" on "hospital expenditure". METHODS: Data were extracted from the medical records of patients with head and neck neoplasms who were admitted to Sichuan Cancer Hospital. R 3.4.1 was used for generating and processing simulated datasets. Various scenarios were established by setting up different proportions of missing data and missing mechanisms using Monte Carlo method. Three strategies were tested for replacing missing data: Complete Case method,Expectation Maximization (EM),and Markov Chain Monte Carlo method (MCMC). The regression coefficient estimates r of standardized "length of stay" on standardized logarithmic "hospital expenditure" were calculated using these strategies and compared with that of the original complete dataset,in terms of their accuracy (magnitude of differences in r) and precision (differences in the standard error of r). RESULTS: The three replacement methods were all acceptable (within the limit rc±0.5 sc) when missing data were generated using MAR (2∶1) mechanism,or less than 30% data were simulated as missing using the MCAR and MAR (1∶2) mechanism. The EM method had the best estimation precision. CONCLUSION: Missing data replacement should consider the proportion of missing data and potential mechanisms involved.
Assuntos
Palavras-chave
Buscar no Google
Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Health_economic_evaluation Limite: Humans Idioma: Zh Ano de publicação: 2018 Tipo de documento: Article
Buscar no Google
Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Health_economic_evaluation Limite: Humans Idioma: Zh Ano de publicação: 2018 Tipo de documento: Article