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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 is also analyzed by logistic regression to obtain odds-ratio (OR). We have the following three empirical observations: (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   ) &lt; 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 results may help future analyses of data from COVID-19 or other similar diseases, in particular if the deceased patients are lacking, whereas surviving patients are abundant.

Approximate reciprocal relationship between two cause-specific hazard ratios in COVID-19 data with mutually exclusive events