Table 2.

Common approaches used to account for mortality in the analysis of intensive care unit (ICU) length of stay (LOS).

Approach	Conceptual and empirical issues
Contrast a pooled LOS distribution of survivors and decedents together without acknowledging death.	LOS treatment effects for survivors and decedents may differ both in magnitude and direction. Patients saved by a treatment may experience longer LOS.
Contrast the LOS distribution among survivors only.	Survival may be affected by the intervention. Thus, it is a post-randomization variable. Conditioning on survival reduces statistical power and can erode randomization inference.
Contrast time-to-discharge in a time-to-event model and treat mortality as a form of non-administrative censoring.	• Risk set subsequent to the first death comprises a new subset of patients who have not previously died or been censored. Thus, the balance of confounders assumed by randomization is potentially eroded. • Statistical model assumes LOS at the time of death is not related to the intervention.
Contrast a composite endpoint that includes both a value for death and LOS (e.g., ICU-free day metric where those who died are assumed to have zero ICU-free days or changing LOS to the longest value).	• Valuing death inserts subjectivity into the statistical analysis and changes the causal question. • Composite outcome (e.g., ICU-free day) may summarize the net effect of an intervention but does not have a real-world translation.