Missing data can plague researchers in many scenarios, arising from incomplete surveys, experimental objects broken or destroyed, or data collection/computational errors. This short course will ...

Purpose Missing data are a well-known and widely documented problem in cost-effectiveness analyses alongside clinical trials using individual patient-level data. Current methodological research ...

Multiple imputation (MI) can be used to address missing data at Level 2 in multilevel research. In this article, we compare joint modeling (JM) and the fully conditional specification (FCS) of MI as ...

Data is almost always incomplete. Patients drop out of clinical trials and survey respondents skip questions; schools fail to report scores, and governments ignore elements of their economies. When ...

In finance, data is often incomplete because the data is unavailable, inapplicable or unreported. Unfortunately, many classical data analysis techniques — for instance, linear regression — cannot ...