[HTML][HTML] A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear …
Background Missing data is a common problem in epidemiological studies, and is
particularly prominent in longitudinal data, which involve multiple waves of data collection …
particularly prominent in longitudinal data, which involve multiple waves of data collection …
[引用][C] A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear …
APD Silva, M Moreno-Betancur… - BMC Medical …, 2017 - jglobal.jst.go.jp
A comparison of multiple imputation methods for handling missing values in longitudinal
data in the presence of a time-varying covariate with a non-linear association with time: a …
data in the presence of a time-varying covariate with a non-linear association with time: a …
[HTML][HTML] A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear …
AP De Silva… - BMC Medical …, 2017 - bmcmedresmethodol.biomedcentral …
Missing data is a common problem in epidemiological studies, and is particularly prominent
in longitudinal data, which involve multiple waves of data collection. Traditional multiple …
in longitudinal data, which involve multiple waves of data collection. Traditional multiple …
A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association …
AP De Silva, M Moreno-Betancur… - BMC Medical …, 2017 - search.proquest.com
Background Missing data is a common problem in epidemiological studies, and is
particularly prominent in longitudinal data, which involve multiple waves of data collection …
particularly prominent in longitudinal data, which involve multiple waves of data collection …
[HTML][HTML] A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear …
AP De Silva, M Moreno-Betancur… - BMC Medical …, 2017 - ncbi.nlm.nih.gov
Background Missing data is a common problem in epidemiological studies, and is
particularly prominent in longitudinal data, which involve multiple waves of data collection …
particularly prominent in longitudinal data, which involve multiple waves of data collection …
A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association …
AP De Silva, M Moreno-Betancur… - BMC medical …, 2017 - pubmed.ncbi.nlm.nih.gov
Background Missing data is a common problem in epidemiological studies, and is
particularly prominent in longitudinal data, which involve multiple waves of data collection …
particularly prominent in longitudinal data, which involve multiple waves of data collection …
A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association …
A De Livera - BMC Medical Research Methodology, 2017 - researchrepository.rmit.edu.au
Fully conditional specification Longitudinal data Missing data Multiple imputation
Multivariate normal imputation Non-linear trajectory Time-dependent covariate Statistics not …
Multivariate normal imputation Non-linear trajectory Time-dependent covariate Statistics not …
A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association …
AP De Silva, M Moreno-Betancur… - BMC Medical …, 2017 - europepmc.org
Background Missing data is a common problem in epidemiological studies, and is
particularly prominent in longitudinal data, which involve multiple waves of data collection …
particularly prominent in longitudinal data, which involve multiple waves of data collection …
[PDF][PDF] A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear …
AP De Silva, M Moreno-Betancur, AM De Livera… - 2017 - minerva-access.unimelb.edu.au
Background: Missing data is a common problem in epidemiological studies, and is
particularly prominent in longitudinal data, which involve multiple waves of data collection …
particularly prominent in longitudinal data, which involve multiple waves of data collection …
A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association …
AP De Silva, M Moreno-Betancur… - BMC Medical …, 2017 - go.gale.com
Background Missing data is a common problem in epidemiological studies, and is
particularly prominent in longitudinal data, which involve multiple waves of data collection …
particularly prominent in longitudinal data, which involve multiple waves of data collection …