survival

EP16: Missing Data in Clinical Research

Multiple Imputation to Handle Missing Values in Clinical Research

Multiple Imputation of Missing Data in Simple and More Complex Settings

Pre-conference course on Multiple Imputation of Missing Data in Simple and More Complex Settings at the "Tagung der Fachgruppe Methoden & Evaluation der Deutschen Gesellschaft für Psychologie" in Kiel, Germany

Joint Models for Incomplete Longitudinal and Survival Data

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Working with Incomplete Data: When One-size-fits-all does not fit

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JointAI: Joint Analysis and Imputation of Incomplete Data in R

Missing data occur in many types of studies and typically complicate the analysis. Multiple imputation, either using joint modelling or the more flexible fully conditional specification approach, are popular and work well in standard settings. In …

Joint Analysis and Imputation of Incomplete Data in R

R package JointAI for analysis of incomplete data in the Bayesian framework.

R package JointAI

R package for Joint Analysis and Imputation of incomplete data in R using the Bayesian framework

Imputation of missing covariates: when standard methods may fail

Our work is motivated by examples from two large cohort studies, the Generation R Study and the Rotterdam Study, in which the analysis models of interest involved non-linear effects, interaction terms or had a longitudinal outcome. As is the case for …

Effects of time interval between primary melanoma excision and sentinel node biopsy on positivity rate and survival

**Background:** Sentinel node biopsy (SNB) is essential for adequate melanoma staging. Most melanoma guidelines advocate to perform wide local excision and SNB as soon as possible, causing time pressure. **Objective:** To investigate the role of time …