Health-related quality-of-life (HRQoL) data are central to capturing the quality of patients' life, while endpoints like overall survival (OS) focus on the quantity of life. When analyzing HRQoL data gathered from patients in a randomized trial, a key consideration is the completion rate - indicating the proportion of patients remaining in the trial and with completed questionnaires. When completion rates are disproportionately low in one treatment arm, one likely explanation is that patients who did not complete questionnaires suffered more from toxicities, negatively impacting their HRQoL. This is likely the case when low completion rates occur in the more toxic arm within a randomized trial. If the HRQoL analysis is run as a complete-case analysis - only considering patients without missing data - a decrement in HRQoL can be missed. Conversely, when completion rates are high, the HRQoL data are thought to be more reliable, and informative censoring is less likely. We describe why this reasoning can be inadequate. In trials where high and imbalanced rates of early censoring affect progression-free survival or OS endpoints, the completion rates only apply to the fraction of patients remaining in the trial. In those, HRQoL results should be considered with caution, and reasons for censoring in the primary time-to-event analyses should be explored before making definite conclusions about HRQoL. This is even more relevant in trials with non-inferiority design, where a benefit in HRQoL could be used as a justification to modify practice.