Prone position and VAP incidence in the PROSEVA trial: attention to the causal question when interpreting competing risk analysis
Dear Editor, We read with great interest the study by Ayzac et al. [1] assessing the impact of prone positioning on the incidence of ventilator associated pneumonia (VAP) as a secondary analysis of the PROSEVA trial. Importantly, given that patients who die early are precluded from developing VAP, the authors appropriately applied competing risk analysis [2] Indeed, it is important to emphasize that the most severe patient also presents the highest risk of developing VAP (e.g. severity is a risk factor for mortality and VAP) The authors hypothesized a lower risk of VAP in the prone position group, but surprisingly they observed higher 90 day cumulative incidence (CIF) [46.5 (27 66) compared with supine patients [33.5 (23 44) Although the authors did not report the subdistribution hazard ratio (subdistribution HR) of the Fine and Gray model, prone position increased the risk of VAP, with a point estimate difference (risk difference) of 13 Ultimately, this result did not reach statistical significance by the Gray's test (p 0.11) Fine and Gray models estimate associations in the presence of competing events. Therefore, the CIF in Fig. 1 of the main paper depicts what we would expect [1] because patients in the prone position survived longer and were exposed to the risk of VAP for a longer period [2 4] In their analysis, the CIF was the final composition of two cause specific hazard ratios (cause specific HR) one for survival and the other accounting for VAP. Therefore, when presenting the results of a competing risk analysis aiming to answer causal questions, it is imperative to report also cause specific HR, because cause specific HR represents the "actual" direct effect of the exposure on the outcome [2 4] In order to emphasize the importance of these measures of effect (subdistribution HR and cause specific HR) we simulated three different scenarios keeping the protective effect on survival constant (Table 1) We performed our analyses through STATA 13.1, using the survsim command to generate the simulated data (codes are available in the electronic supplementary material) We observed that when the "real" direct effect of prone position on VAP incidence was defined as "small" or "null" interpretation of the study results only reporting subdistribution HR would have been challenging. Indeed, when prone position had a null "real" direct effect on VAP incidence, the cause specific HR was 0.959 (95 CI, 0.879 1.046, p 0.344) whereas, the subdistribution HR was 1.113 (95 CI, 1.020 1.214, p 0.016) contrasting the cause specific HR. Conversely, when prone position had a small protective "real" direct effect on VAP incidence, the cause specific HR was 0.905 (95 CI, 0.829 0.989, p 0.027) whereas, the subdistribution HR was 1.052 (95 CI, 0.964 1.149, p 0.257) These were expected results due to the large effect of prone position on survival. Similar issues have been noted in other contexts [5] Comprehensive graphical reports of these scenarios can be found elsewhere [2 4] In conclusion, Fine and Gray models are not well suited to address etiological/causal questions; thus, it would *Correspondence: otavioranzani@yahoo.com.br 2 Respiratory Intensive Care Unit, Pulmonary Division, Heart Institute, Hospital das Clinicas, University of Sao Paulo, Av. Dr. Arnaldo, 455, Laboratorio de Pneumologia, 2o andar, sala 2144, Sao Paulo, Sao Paulo 01246903, Brazil Full author information is available at the end of the article
Intensive Care Medicine