Longitudinal studies of cognitive performance are sensitive to dropout, as participants experiencing cognitive deficits are less likely to attend study visits, which may bias estimated associations between exposures of interest and cognitive decline. Multiple imputation is a powerful tool for handling missing data, however its use for missing cognitive outcome measures in longitudinal analyses remains limited
Background: Missing data often cause problems in longitudinal cohort studies with repeated follow-up...
Missing observations are common in cluster randomised trials. The problem is exacerbated when modell...
It is now a standard practice to replace missing data in longitudinal surveys with imputed values, b...
Longitudinal studies of cognitive performance are sensitive to dropout, as participants experiencing...
International audienceABSTRACT: BACKGROUND: The weighted estimators generally used for analyzing cas...
The objective of this dissertation is to utilize statistical methods to obtain consistent estimates ...
To improve understanding of Alzheimer’s disease, large observational studies are needed to increase ...
Objectives: We provide guidelines for handling the most common missing data problems in repeated mea...
CITATION: Masconi, K. L., et al . 2016. Effects of different missing data imputation techniques on t...
BACKGROUND: When an outcome variable is missing not at random (MNAR: probability of missingness depe...
International audienceHeterogeneity of cohorts, in terms of inclusion criteria, design of follow-up ...
Funder: One Mind for ResearchFunder: bill and melinda gates foundation (us)Funder: University of Man...
INTRODUCTION: Loss to follow-up in dementia studies is common and related to cognition, which worsen...
INTRODUCTION: Loss to follow-up in dementia studies is common and related to cognition, which worsen...
*<p>Rate ratios based on a 10 point change in each of the cognitive measures. Higher scores on the c...
Background: Missing data often cause problems in longitudinal cohort studies with repeated follow-up...
Missing observations are common in cluster randomised trials. The problem is exacerbated when modell...
It is now a standard practice to replace missing data in longitudinal surveys with imputed values, b...
Longitudinal studies of cognitive performance are sensitive to dropout, as participants experiencing...
International audienceABSTRACT: BACKGROUND: The weighted estimators generally used for analyzing cas...
The objective of this dissertation is to utilize statistical methods to obtain consistent estimates ...
To improve understanding of Alzheimer’s disease, large observational studies are needed to increase ...
Objectives: We provide guidelines for handling the most common missing data problems in repeated mea...
CITATION: Masconi, K. L., et al . 2016. Effects of different missing data imputation techniques on t...
BACKGROUND: When an outcome variable is missing not at random (MNAR: probability of missingness depe...
International audienceHeterogeneity of cohorts, in terms of inclusion criteria, design of follow-up ...
Funder: One Mind for ResearchFunder: bill and melinda gates foundation (us)Funder: University of Man...
INTRODUCTION: Loss to follow-up in dementia studies is common and related to cognition, which worsen...
INTRODUCTION: Loss to follow-up in dementia studies is common and related to cognition, which worsen...
*<p>Rate ratios based on a 10 point change in each of the cognitive measures. Higher scores on the c...
Background: Missing data often cause problems in longitudinal cohort studies with repeated follow-up...
Missing observations are common in cluster randomised trials. The problem is exacerbated when modell...
It is now a standard practice to replace missing data in longitudinal surveys with imputed values, b...