Multilevel competing risks in the evaluation of nosocomial infections: time to move on from proportional hazards and even from hazards altogether

Abstract

A competing risk is an event (for example, death in the ICU) that hinders the occurrence of an event of interest (for example, nosocomial infection in the ICU) and it is a common issue in many critical care studies. Not accounting for a competing event may affect how results related to a primary event of interest are interpreted. In the previous issue of Critical Care, Wolkewitz and colleagues extended traditional models for competing risks to include random effects as a means to quantify heterogeneity among ICUs. Reported results from their analyses based on cause-specific hazards and on sub-hazards of the cumulative incidence function were indicative of lack of proportionality of these hazards over time. Here, we argue that proportionality of hazards can be problematic in competing-risk problems and analyses must consider time by covariate interactions as a default. Moreover, since hazards in competing risks make it difficult to disentangle the effects of frequency and timing of the competing events, their interpretation can be murky. Use of mixtures of flexible and succinct parametric time-to-event models for competing risks permits disentanglement of the frequency and timing at the price of requiring stronger data and a higher number of parameters. We used data from a clinical trial on fluid management strategies for patients with acute respiratory distress syndrome to support our recommendations.

Figure 1

Parametric and non-parametric estimates of the cumulative percentages of ventilated patients who either achieved unassisted breathing or died after randomization in the fluid management trial. Estimates are stratified by study group (A) and ratios of conservative to liberal strategies of cause-specific hazards (B) and sub-hazards (C) of unassisted breathing. In all of these models, death before unassisted breathing is the competing risk. For panel (A) (Adapted with permission from Lippincott Williams and Wilkins/Wolters Kluwer Health: Epidemiology[17], copyright 2010), parametric estimates are represented with continuous lines and non-parametric estimates are represented with steps. The continuous lines give evidence to the goodness-of-fit of mixtures of generalized gamma distributions for the timing of the two events in both treatment groups.