You have helped me before and I come again !
This is an interesting trial, for many reasons. It compares two antibotics, both very broad spectrum, for a set of resistant bugs (ESBL E.coli + Klebsiella). Meropenem - one of the antibiotics, was found to be significantly more efficacious than Piperacillin-Tazobactam.
23/187 died in Piperacillin arm (all cause)
7/191 died in Meropenem arm (all cause)
This is a quite significant result and has altered therapy around the world. There are a couple of caveats - firstly, no-one was adjudged to have actually died from infection (!). This is a problem in infection trials in the critically ill - to get that sick to get a nasty bug you have to be pretty sick and your underlying disease kills you. So the addendum makes for interesting reading - many had incurable cancer and perhaps the antibotics were hardly relevant.
I am intrigued about one thing - and I know this is a hated topic - but post randomisation adjustment for known variables that are associated with the outcome.
The key here is UTI. Those with UTI (and in fact, this is mentioned in the protocol- and is pre-specified) do much better with bloodstream infection, and therefore some (maybe not on this forum!) would adjust for this.
As chance would have it, the number of patients with a UTI source differed greatly between the two groups:
103/188 in the Piperacillin arm (56%)
128/193 in the Meropenem arm (67%)
So far, so good. They performed a planned multivariable regression including UTI (and Charlson’s comorbidity index) and the treatment arm and calculated adjusted and unadjusted odds ratios:
Adjustment for a urinary tract source of infection and the Charlson Comorbidity Index score resulted in little change in the findings (unadjusted odds ratio, 3.69 [1-sided 97.5% CI, 0 to 8.82]; adjusted odds ratio, 3.41 [1-sided 97.5% CI, 0 to 8.38) (eTable 5 in Supplement 2).
What I don’t understand, is how that odds ratio doesn’t change very much?
Given the small number of events, there must have been quite significant correlation between the variables. Here is the UTI data:
I don’t understand how the OR for UTI can be 0.34 - suggesting they are much less likely to die, but then when adjusting for treatment arm, it doesn’t affect the treatment arm OR practically at all?
Or am I really stupid and I have completely forgotten how regression and correlation between variables work? and these two results are completely plausible?