Question about statistical analysis for ordinal outcomes, baseline differences, and the proportional odds assumption.
Lancet published a much awaited study on use of Remdesivir for Covid19. This was a randomized (2:1 allocation ratio), double-blind, placebo-controlled trial (n=237):
The primary outcome was “time to clinical improvement within 28 days after randomization.” Clinical improvement was defined using a 6-point ordinal scale:
- 6 = death
- 5 = hospital admission for ECMO or mechanical ventilation
- 4 = hospital admission for non-invasive ventilation or high-flow oxygen
- 3 = hospital admission for regular oxygen therapy
- 2 = hospital admission but not requiring oxygen
- 1 = discharged or having reached discharge criteria
The statistical analysis, briefly, was as follows:
- “The primary efficacy analysis was done on an intention-to-treat (ITT) basis with all randomly assigned patients. Time to clinical improvement was assessed after all patients had reached day 28; no clinical improvement at day 28 or death before day 28 were considered as right censored at day 28. Time to clinical improvement was portrayed by Kaplan-Meier plot and compared with a log-rank test. The HR and 95% CI for clinical improvement and HR with 95% CI for clinical deterioration were calculated by Cox proportional hazards model.”
- Time to clinical improvement:
- Proportion distribution at Day 1, 7, 14, & 28:
I am trying to understand why the outcomes appear to shift for the worse at Day 7, and then for the better at Day 14.
- Is this just normal “noise” that we see in data early on in a trial?
- Is it possible chance handed Remdesivir a slightly sicker group of patients at baseline?
- Does Cox proportional hazards model take into account baseline differences?
- Does this violate the proportional odds assumption?
- Does this effect the optimal choice for a statistical model?
- Is it possible the choice of outcome scale flawed?
My initial assumption is that this is just expected noise/variation in data at the early point of the study. But I thought I would ask others so they could offer expertise on how to best interpret & learn form this example.