Copenhagen, Denmark
Onsite/Online

ESTRO 2022

Session Item

Lower GI
6012
Poster (digital)
Clinical
CoDMI algorithm and survival analysis: first clinical application in rectal cancer
Francesca De Felice, Italy
PO-1312

Abstract

CoDMI algorithm and survival analysis: first clinical application in rectal cancer
Authors:

Francesca De Felice1, Luca Mazzoni2, Daniela Musio3, Vincenzo Tombolini1, Franco Moriconi4

1Sapienza University of Rome - Policlinico Umberto I, Radiotherapy, Rome, Italy; 2University of Florence, Department of Statistics, Computer Science, Applications, Florence, Italy; 3Sapienza University of Rome - Policlinico Umberto I , Radiotherapy, Rome, Italy; 4University of Perugia, Department of Economics, Perugia, Italy

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Purpose or Objective

To illustrate a clinical application of Covid-Death Mean-Imputation (CoDMI) algorithm in survival analysis. CoDMI algorithm is a new statistical tool that allows to adjust, through mean imputation based on the Kaplan-Meier model, Covid-19 death events in oncologic clinical trials, providing a complete sample of observations to which any statistical method in survival analysis can be applied. 

Material and Methods

We analyzed a group of patients who received trimodal treatment – neoadjuvant chemoradiotherapy, followed by surgery and adjuvant chemotherapy – for primary locally advanced rectal cancer. 

Overall survival was calculated in months from the date of diagnosis to the first event, including date of the last follow-up or 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 (but this would represent a loss of data), or ii) counted as censored (Cen) (but actually, due to its informative nature, Covid-19 death in a cancer patient cannot be censored as death from other causes), or iii) considered as died of disease (DoD) (but this provides an inappropriate exit cause). 

CoDMI algorithm offers an additional,  more satisfactory option: iv) DoC events are mean-imputed as no-DoC cases at later follow-up times. With 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-Meyer model. 


Results

A total of 94 patient records were collected. At the time of the analysis, 16 patients died of disease (DoD), 1 patient died of Covid-19 (DoC) and 77 cases were censored (Cen). The DoC patient died due to Covid-19 52 months after diagnosis. CoDMI algorithm computed the expected future lifetime (beyond the DoC time of occurrence) 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 (red triangle in Figure 1), CoDMI algorithm (applied in its standard form) estimated that this patient would be died after 79.5 months of follow-up. The blue line in Figure 1 represents the newly estimated survival curve, where the additional DoD event is denoted by a circle.



Conclusion

CoDMI algorithm leads to the “unbiased” (appropriately adjusted) probability of overall survival in locally advanced rectal cancer patients with Covid-19 infection, compared with that provided by a naïve application of  the Kaplan-Meier approach. This allows 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.