Survival methods, including those using competing risk analysis, are not appropriate for intensive care unit outcome studies

In Evaluating Mortality in Intensive Care Units: Contribution of Competing Risks Analysis [1], the authors introduce the use of the Fine and Grey regression model [2], based on the cumulative incidence function (CIF), to analyze data from outcome studies in the intensive care unit (ICU). They show that this model can be used to provide a valid analysis of hospital or ICU mortality. The authors prefer this model to analyzing mortality as a binary variable (lived versus died) using binary data analysis techniques such as logistic regression. I argue that mortality should be analyzed as a binary variable because patients who die in the ICU do not benefit if the duration of their survival is prolonged. Because survival methods, including those based on the CIF, measure this increase in survival, these methods can lead to inferences where a treatment is preferred that doesn't confer patient benefit. I conclude that logistic regression should be the preferred method of analyzing ICU data. First I compare total mortality and hospital mortality as outcomes for ICU studies. I explain which survival theory methods are appropriate for these outcomes. Then I show why these methods may lead to misleading results.Most medical studies use total mortality as their primary outcome variable. To capture this outcome patients must be followed after they leave the hospital to make sure that they do not die elsewhere. Survival analysis methods allow us to incorporate non-informative censoring in which a patient is known to be alive at a certain time. The authors correctly point out that when a patient is known to leave the hospital alive, survival methods that consider the patient as censored are not appropriate [1]. The CIF and the Fine and Grey models are also not appropriate when total mortality is the outcome because deaths after the patient leaves the hospital are not included in the CIF. In an analysis of total mortality, censoring is the last time the patient was contacted. Method