Survival analysis using the Covid-Death Mean-Imputation (CoDMI) algorithm: a first clinical application in radiation oncology

Purpose: . We illustrate a clinical application of Covid-Death Mean-Imputation (CoDMI) algorithm in survival analysis. Material: and methods . We analyzed 94 patients treated for primary locally advanced rectal cancer (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 CoDMI algorithm. In a traditional Kaplan-Meier approach, patient died of Covid-19 (DoC) can be: i) excluded to the cohort, or ii) counted as censored (Cen), or iii) considered as died of disease (DoD). CoDMI algorithm offers an additional, more satisfactory option: iv) DoC events are mean-imputed by the Kaplan-Meier estimator. With this approach, 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: . 16 patients were DoD, 1 patient was DoC and 77 cases were Cen. The DoC patient died of Covid-19 52 months after diagnosis. 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, CoDMI algorithm estimated that this patient would be died after 79.5 months of follow-up. Conclusion: . CoDMI algorithm leads to “unbiased” probability of overall survival in LARC patients with Covid-19 infection, compared with 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.