Approximate Reciprocal Relationship Between Two Cause-Specific Hazard Ratios in COVID-19 Data With Mutually Exclusive Events

COVID-19 survival data presents a special situation where not only the time-to-event period is short, but also the two events or outcome types, death and release from hospital, are mutually exclusive, leading to two cause-specific hazard ratios (csHR _d and csHR _r ). The eventual mortality/release outcome can also be analyzed by logistic regression to obtain odds-ratio (OR). We have the following three empirical observations concerning csHR _d , csHR _r and OR: (1) The magnitude of OR is an upper limit of the csHR _d : | log(OR) | ≥ | log(csHR _d )|. This relationship between OR and HR might be understood from the definition of the two quantities; (2) csHR _d and csHR _r point in opposite directions: log(csHR _d )· log(csHR _r ) < 0; This relation is a direct consequence of the nature of the two events; and (3) there is a tendency for a reciprocal relation between csHR _d and csHR _r : csHR _d ∼ 1/csHR _r . Though an approximate reciprocal trend between the two hazard ratios is in indication that the same factor causing faster death also lead to slow recovery by a similar mechanism, and vice versa, a quantitative relation between csHR _d and csHR _r in this context is not obvious. These resutls may help future analyses of COVID-19 data, in particular if the deceased samples are lacking.