Survival Analysis Using the Covid-death Mean-imputation (CoDMI) Algorithm: A First Clinical Application in Radiation Oncology.

Background/aim: To report long-term survival results after trimodal approach for locally advanced rectal cancer (LARC) in the Covid-19 era. We herein illustrate a clinical application of Covid-death mean-imputation (CoDMI) algorithm in LARC patients with Covid-19 infection.

Patients And Methods: We analyzed 94 patients treated for primary LARC. Overall survival was calculated in months from diagnosis to first event (last follow-up/death). Because Covid-19 death events potentially bias survival estimation, to eliminate skewed data due to Covid-19 death events, the observed lifetime of Covid-19 cases was replaced by its corresponding expected lifetime in absence of the Covid-19 event using the CoDMI algorithm. Patients who died of Covid-19 (DoC) are mean-imputed by the Kaplan-Meier estimator. Under this approach, the observed lifetime of each DoC patient is considered as an "incomplete data" and is extended by an additional expected lifetime computed using the classical Kaplan-Meier model.

Results: Sixteen patients were dead of disease (DoD), 1 patient was DoC and 77 cases were censored (Cen). The DoC patient died of Covid-19 52 months after diagnosis. The CoDMI algorithm computed the expected future lifetime provided by the Kaplan-Meier estimator applied to the no-DoC observations as well as to the DoC data itself. Given the DoC event at 52 months, the CoDMI algorithm estimated that this patient would have died after 79.5 months of follow-up.

Conclusion: The CoDMI algorithm leads to "unbiased" probability of overall survival in LARC patients with Covid-19 infection, compared to that provided by a naïve application of Kaplan-Meier approach. This allows for a proper interpretation/use of Covid-19 events in survival analysis. A user-friendly version of CoDMI is freely available at https://github.com/alef-innovation/codmi.