This section begins with a chapter illustrating how missing data can cloud inferences to be drawn from clinical trials – in other words, why missing data matter. Chapter 2 focuses on the mechanisms that give rise to missing data. Understanding these mechanisms is the essential background needed to understand the possible consequences of missing data. Chapter 3 discusses estimands – what is to b...

Guideline on Missing Data in Confirmatory Clinical Trials Discussion in the Efficacy Working Party June 1999/ November 2000 Transmission to CPMP January 2001 Released for consultation January 2001 Deadline for Comments April 2001 Discussion in the Efficacy Working Party October 2001 Transmission to CPMP November 2001 Adoption by CPMP November 2001 Draft Rev. 1 Agreed by Efficacy Working Party A...

Data completeness is an essential aspect of data quality as in many scenarios it is crucial to guarantee the completeness of query answers. Data might be incomplete in two ways: records may be missing as a whole, or attribute values of a record may be absent, indicated by a null. We extend previous work by two of the authors [10] that dealt only with the first aspect, to cover both missing reco...