Clinical research associates experience with missing patient reported outcomes data in cancer randomized controlled trials

Background Missing patient reported outcomes data threaten the validity of PRO‐specific findings and conclusions from randomized controlled trials by introducing bias due to data missing not at random. Clinical Research Associates are a largely unexplored source for informing understanding of potential causes of missing PRO data. The purpose of this qualitative research was to describe factors that influence missing PRO data, as revealed through the lived experience of CRAs. Methods Maximum variation sampling was used to select CRAs having a range of experiences with missing PRO data from academic or nonacademic centers in different geographic locations of Canada. Semistructured interviews were audio‐recorded, transcribed verbatim, and analyzed according to descriptive phenomenology. Results Eleven CRAs were interviewed. Analysis revealed several factors that influence missing PRO data that were organized within themes. PROs for routine clinical care compete with PROs for RCTs. Both the paper and electronic formats have benefits and drawbacks. Missing PRO data are influenced by characteristics of the instruments and of the patients. Assessment of PROs at progression of disease is particularly difficult. Deficiencies in center research infrastructure can contribute. CRAs develop relationships with patients that may help reduce missing PRO data. It is not always possible to provide sufficient time to complete the instrument. There is a need for field guidance and a motivation among CRAs to contribute their knowledge to address issues. Conclusion These results enhance understanding of factors influencing missing PRO data and have important implications for designing operational solutions to improve data quality on cancer RCTs. This qualitative research provides essential inductive evidence for influences of missing PRO data. The results have important implications for designing operational solutions to improve data quality on cancer RCTs.