One of the consequences of the perceived need for a “primary outcome” is that people try to create a single outcome variable that will include all or most of the important effects, and will increase the incidence of the outcome, or in some other way allow the sample size calculation to give you a smaller target. There has for some time been a movement to use “ventilator-free days” in critical care trials, but a recent trend is for trials of treatments for cardiac arrest to use “hospital-free survival” or “ICU-free survival,” defined as the number of days that a trial participant was alive and not in hospital or ICU, up to 30 days post randomisation.
A recent example is Nicholl et al (2015), who compared continuous versus interrupted chest compressions during CPR. It was a massive trial, randomising over 23,000 participants, and found 9% survival with continuous compressions and 9.7% with interrupted. Inevitably this was described as “continuous chest compressions during CPR performed by EMS providers did not result in significantly higher rates of survival.” But it did result in a “significant” difference in hospital-free survival, which was a massive 0.2 days shorter in the continuous compression group (95% CI -0.01, -0.03, p=0.004).
A few comments. First, with a trial of this size and a continuous outcome, it’s almost impossible not to get statistical significance, even if the effect is tiny. As you can see. I very much doubt that anyone would consider an improvement in hospital-free survival of 0.2 days (that’s about 4 hours 48 minutes) of any consequence, but it’s there in the abstract as a finding of the trial.
Second, it’s a composite outcome, and like almost all composite outcomes, it includes things that are very different in their importance; in this case, whether the patient is alive or dead, and whether they are in hospital. It’s pretty hard to interpret this. What does a difference of about 5 hours in the time alive and out of hospital mean? Would a patient think that was a good reason to use the intervention? I doubt it. They would surely be more interested in the chances of surviving, and maybe secondarily whether the amount of time they might spend in hospital would be different.
Third, and this is especially true for cardiac arrest trials, the mean is a terrible way to summarise these data. The survival rate in this trial was about 9%. The vast majority of deaths would have occurred either before reaching hospital or in hospital, so all of those patients would have hospital-free survival of zero. The 9% or so of patients that survived to hospital discharge would have a number of hospital-free days between 0 and 30. So the means for each group will be pulled strongly towards zero by the huge number of participants with zero hospital-free days. The means for each group are presented in Table 3 of the paper, as 1.3 ± 5.0, and 1.5 ± 5.3, without comment, even though that seems to imply negative hospital-free survival. Definitely a case here for plotting the data to see what is going on; the tabulated summary is inadequate. The difference is almost certainly driven by the 0.7% higher survival in the interrupted compression group, which was possibly an important finding. However, because it was non-significant it is pretty much ignored and assumed to be zero.
Nichol G et al. Trial of Continuous or Interrupted Chest Compressions during CPR. NEJM 2015; 373: 2203-2214.
Original post 30 May 2017 http://blogs.warwick.ac.uk/simongates/entry/hospital-free_survival/