Intricacy of missing data in clinical trials: Deterrence and management

Missing data is frequently encountered in clinical studies. Unfortunately, they are often neglected or not properly handled during data analysis and this may significantly bias the results of the study, reduce study power and lead to invalid conclusions. Substantial instances of missing data are a serious problem that undermines the scientific trustworthiness of causal conclusions from clinical trials. The assumption that statistical analysis methods can compensate for such missing data is not justified. Hence aspects of clinical trial design that limit the probability of missing data should be an important objective, while planning a clinical trial. In addition to specific aspects of trial design, many components of clinical trial conduct can also limit the extent of missing data. The topic of missing data is often not a major concern until it is time for data collection and data analysis. This article discusses some basic issues about missing data as well as prospective "watch outs" which could reduce the occurrence of missing data. It provides some possible design considerations that should be considered in order to alleviate patients from dropping out of a clinical trial. In addition to these the concept of the missing data mechanism has also been discussed. Three types of missing data mechanisms missing completely at random, missing at random and not missing at random have been discussed in detail.