Avoidable statistical pitfalls in analyzing length of stay in intensive care units or hospitals

Excerpt

In a study of ventilated patients in a recent issue of Critical Care, Shahin and colleagues [1] compared the length of stay in ICU between patients who acquired a suspected ventilator-associated respiratory infection (sVARI) and patients who remained free of this infection. There are two basic facts that require special attention in the statistical analysis. First, ventilation is a time-dependent study entry since some patients receive ventilation a few days after the time of admission to the ICU. In keeping with the study design, these patients cannot be discharged between admission and first ventilation; ignoring this fact leads to length bias [2]. Second, sVARI is a time-dependent exposure since sVARI might occur a few days after the start of ventilation; treating sVARI incorrectly as time-fixed (that is, assumed to be known at the time of admission) leads to time-dependent bias [2]. The statistical models used by Shahin and colleagues incorrectly assumed that ventilation and sVARI occurrence already happened at the day of admission. Both types of bias can be substantial in such settings: in a study of patients colonized with methicillin-resistant Staphylococcus aureus (MRSA), the biased extra length of hospital stay of MRSA-infected patients was 24.5 days whereas the correct estimate was only 6 days [2]. Length and time-dependent bias are avoidable types of bias since the chronological order (admission, time of first ventilation, time of sVARI, and time of discharge) can be acknowledged in a multi-state model [2]. Researchers are encouraged to use adequate models to receive valid results for hospital-acquired infections [3]. …