Multiple imputation in data that grow over time: A comparison of three strategies

Multiple imputation is a highly recommended technique to deal with missing data, but the application to longitudinal datasets can be done in multiple ways. When a new wave of longitudinal data arrives, we can treat the combined data of multiple waves as a new missing data problem and overwrite existing imputations with new values (re-imputation)...

Multiple imputation in data that grow over time: a comparison of three strategies

Multiple imputation is a recommended technique to deal with missing data. We study the problem where the investigator has already created imputations before the arrival of the next wave of data. The newly arriving data contain missing values that need to be imputed. The standard method (RE-IMPUTE) is to combine the new and old data before...

Multiple imputation for multilevel data with continuous and binary variables

We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. Simulations are reproducible. The...

Pooling multiple imputations when the sample happens to be the population

Current pooling rules for multiply imputed data assume infinite populations. In some situations this assumption is not feasible as every unit in the population has been observed, potentially leading to over-covered population estimates. We simplify the existing pooling rules for situations where the sampling variance is not of interest. We...

Modeling individual and collective opinion in online social networks: drivers of choice behavior and effects of marketing interventions

We investigate factors influencing choice behavior in online social networks. We use twitter data from a Dutch television talent show. In study one, we implement a nested conditional logit model with latent classes. We find heterogeneous effects. For two latent classes, cognitive factors most strongly influence choice behavior. For two other...

Standardisation of environmental enrichment for laboratory mice and rats: Utilisation, practicality and variation in experimental results

Rats and mice are the most commonly used species as laboratory animal models of diseases in biomedical research. Environmental factors such as cage size, number of cage mates and cage structure such as environmental enrichment can affect the physiology and behavioural development of laboratory animals and their well-being throughout their lives....

The Leiden developmental care project: effects of developmental care on behavior and quality of life of very preterm infants and parental and staff experiences

This thesis explores the effects of a basic form of developmental care (the use of incubator covers and nests) and the more extended Newborn Individualized Developmental Care and Assessment Program (NIDCAP) on health related quality of life and behavior of very preterm infants. The effects on parents' stress and their experiences with...