Clinical Trials

❘❙❚■ Chapter 30 | Missing DataWhat are missing data?In clinical trials, after treatment group assignment, each participant is scheduledfor follow-up, during which time the primary outcome and other variables will bemeasured on at least one occasion. It is common for participants to miss some oftheir scheduled visits. It is also common for information to be misplaced or notentered, despite being collected successfully [1,2]. In both of these situations,we say we are faced with a missing data problem.Missing responses can be easily identified when the number of measurements perparticipant is fixed (balanced study). However, in studies with varying numbers ofmeasurement visits per participant (unbalanced studies), it might not be possibleto identify some of the nonresponses (missing data) unless all scheduledmeasurement occasions are strictly recorded.In some of the literature, unobserved latent variables and/or random effects arealso treated as missing data [3,4]. This is not the type of missing data consideredhere. We are concerned with missing outcome variables, ie, measurements thatcould potentially have been obtained. This is in contrast to latent or randomvariables that could never have been observed.What are the common types of missing data?There are numerous reasons why data may be missing at the analysis stage. Thesereasons can include the trial design (eg, if some participants miss some visits bydesign, such as trial closure), loss of successfully collected information, andparticipant refusal or withdrawal. While knowledge of these reasons is important,the analyst is most interested in their potential impact on the results of theanalysis. This impact depends on the relationship between the process giving riseto the missing data and other variables included in the analysis.Rubin gives a useful taxonomy and terminology, widely accepted, for the differentmechanisms that can generate missing data, based on their potential influence onthe results of an analysis [5]. He classifies three different types of missing data:• missing completely at random (MCAR)• missing at random (MAR)• missing not at random (MNAR), also termed non-ignorable missing data340