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An important problem within both epidemiology and many social sciences is to break down the effect of a given treatment into different causal pathways and to quantify the importance of each pathway. Formal mediation analysis based on counterfactuals is a key tool when addressing this problem. During the last decade, the theoretical framework for mediation analysis has been greatly extended to enable the use of arbitrary statistical models for outcome and mediator. However, the researcher attempting to use these techniques in practice will often find implementation a daunting task, as it tends to require special statistical programming. In this paper, the authors introduce a simple procedure based on marginal structural models that directly parameterize the natural direct and indirect effects of interest. It tends to produce more parsimonious results than current techniques, greatly simplifies testing for the presence of a direct or an indirect effect, and has the advantage that it can be conducted in standard software. However, its simplicity comes at the price of relying on correct specification of models for the distribution of mediator (and exposure) and accepting some loss of precision compared with more complex methods. Web Appendixes 1 and 2, which are posted on the Journal's Web site (http://aje.oupjournals.org/), contain implementation examples in SAS software (SAS Institute, Inc., Cary, North Carolina) and R language (R Foundation for Statistical Computing, Vienna, Austria).

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In recent years, mediation analysis has emerged as a powerful tool to disentangle causal pathways from an exposure/treatment to clinically relevant outcomes. Mediation analysis has been applied in scientific fields as diverse as labour market relations and randomized clinical trials of heart disease treatments. In parallel to these applications, the underlying mathematical theory and computer tools have been refined. This combined review and tutorial will introduce the reader to modern mediation analysis including: the mathematical framework; required assumptions; and software implementation in the R package medflex. All results are illustrated using a recent study on the causal pathways stemming from the early invasive treatment of acute coronary syndrome, for which the rich Danish population registers allow us to follow patients’ medication use and more after being discharged from hospital.

Background: Acutely ill adults with hypoxaemic respiratory failure are at risk of lifethreatening hypoxia, and thus oxygen is often administered liberally. Excessive oxygen use may, however, increase the number of serious adverse events, including death. Establishing the optimal oxygenation level is important as existing evidence is of low quality. We hypothesise that targeting an arterial partial pressure of oxygen (PaO 2 ) of 8 kPa is superior to targeting a PaO 2 of 12 kPa in adult intensive care unit (ICU) patients with acute hypoxaemic respiratory failure. Methods:The Handling Oxygenation Targets in the ICU (HOT-ICU) trial is an outcome assessment blinded, multicentre, randomised, parallel-group trial targeting PaO 2 in acutely ill adults with hypoxaemic respiratory failure within 12 hours after ICU admission. Patients are randomised 1:1 to one of the two PaO 2 targets throughout ICU stay until a maximum of 90 days. The primary outcome is 90-day mortality.Secondary outcomes are serious adverse events in the ICU, days alive without organ support and days alive out of hospital in the 90-day period; mortality, health-related | 957 SCHJØRRING et al.