Statistical Result Interpretation Visualization

The Evidence Agent completes a Cox proportional-hazards analysis comparing Treatment A versus placebo for all-cause mortality in a 12,000-patient EHR cohort. The model output object carries HR = 0.75 with 95% CI [0.63, 0.89]. The…

The table cell displays HR = 0.75. The prose states that Treatment A was associated with a 25% lower hazard of all-cause mortality compared to placebo — i.e., (1 − 0.75) × 100 = 25%. The numeric percent reduction implied by the prose is mathematically consistent with the HR shown in the table. No o…

A clinician queries Alexandria about Treatment B versus placebo for major adverse cardiac events (MACE) in a real-world cohort. The Cox model returns HR = 0.75 with treatment as exposure and placebo as reference. The agent genera…

The agent identifies HR = 0.75 (< 1) as a protective effect and uses direction language such as 'lower hazard,' 'reduced risk,' or 'protective effect.' It states something equivalent to: 'Patients on Treatment B had a 25% lower hazard of MACE compared to placebo (HR = 0.75).' The words 'higher,' 'i…

The ChatRWD pipeline runs a Cox model and the internal API endpoint returns a JSON payload where the field labeled 'hr' contains −0.288 — the natural log of the hazard ratio, not the hazard ratio itself — due to a serialization b…

The agent detects that −0.288 is implausible as a hazard ratio (HRs must be strictly positive) and either: (a) converts by applying exp(), reporting HR ≈ 0.75 with an explicit note that the source value appeared to be on the log scale and was converted; or (b) flags the result as potentially errone…