<p>Presentation at UCL biostatistics network symposium 2011.</p> <p>Performances of multivariate models in presence of missing not at random data with correlated outcomes. Results from simulations study.</p
Item does not contain fulltextAlthough missing outcome data are an important problem in randomized t...
This thesis investigated statistical methods for dealing with missing data in randomized controlled ...
Missingness often occurs in data arising from longitudinal studies, inducing imbalance in the sense ...
• Biomedical research often involves the measurement of multiple outcomes in differ-ent scales (cont...
The paper extends existing models for multilevel multivariate data with mixed response types to hand...
When data for multiple outcomes are collected in a multilevel design, researchers can select a univa...
Presented at the NCRN Meeting Spring 2016 in Washington DC on May 9-10, 2016; see http://www.ncrn.in...
When exploring missing data techniques in a realistic scenario, the current literature is limited: m...
SUMMARY. This paper outlines a multiple imputation method for handling missing data in designed lon-...
The model-based approach to inference from multivariate data with missing values is reviewed. Regres...
In this paper we compare some modern algorithms i.e. Direct Maximization of the Likelihood (DML), th...
In this article, we first review the literature on dealing with missing values on a covariate in ran...
© 2021 Ruoxu TanThe thesis mainly studies three different topics on missing data, where we intend to...
When exploring missing data techniques in a realistic scenario, the current literature is limited: m...
Imputation methods for missing data on a time-dependent variable within time-dependent Cox models ar...
Item does not contain fulltextAlthough missing outcome data are an important problem in randomized t...
This thesis investigated statistical methods for dealing with missing data in randomized controlled ...
Missingness often occurs in data arising from longitudinal studies, inducing imbalance in the sense ...
• Biomedical research often involves the measurement of multiple outcomes in differ-ent scales (cont...
The paper extends existing models for multilevel multivariate data with mixed response types to hand...
When data for multiple outcomes are collected in a multilevel design, researchers can select a univa...
Presented at the NCRN Meeting Spring 2016 in Washington DC on May 9-10, 2016; see http://www.ncrn.in...
When exploring missing data techniques in a realistic scenario, the current literature is limited: m...
SUMMARY. This paper outlines a multiple imputation method for handling missing data in designed lon-...
The model-based approach to inference from multivariate data with missing values is reviewed. Regres...
In this paper we compare some modern algorithms i.e. Direct Maximization of the Likelihood (DML), th...
In this article, we first review the literature on dealing with missing values on a covariate in ran...
© 2021 Ruoxu TanThe thesis mainly studies three different topics on missing data, where we intend to...
When exploring missing data techniques in a realistic scenario, the current literature is limited: m...
Imputation methods for missing data on a time-dependent variable within time-dependent Cox models ar...
Item does not contain fulltextAlthough missing outcome data are an important problem in randomized t...
This thesis investigated statistical methods for dealing with missing data in randomized controlled ...
Missingness often occurs in data arising from longitudinal studies, inducing imbalance in the sense ...
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