  This chapter addresses important steps during the quality assurance and
control of RWD, with particular emphasis on the identification and handling of
missing values. A gentle introduction is provided on common statistical and
machine learning methods for imputation. We discuss the main strengths and
weaknesses of each method, and compare their performance in a literature
review. We motivate why the imputation of RWD may require additional efforts to
avoid bias, and highlight recent advances that account for informative
missingness and repeated observations. Finally, we introduce alternative
methods to address incomplete data without the need for imputation.
