Population-based cohort study, predominantly prospective based, on the same population as our previously study comparing short-term outcome after CME with conventional colon resections. The COMES database combines the prospectively registered colon cancer database in Hillerød (CME data), and data from the national database of the Danish Colorectal Cancer Group (DCCG) covering patients undergoing conventional resection (non-CME) in the other three centers. The medical records of all the patients in the non-CME group (control group) were reviewed by colorectal surgeons from Hillerød. Data audit for all CME patients was performed by various co-authors employed at the other centers. A similar audit of data for non-CME patients having postoperative complications or recurrence was performed by the co-author representing the department treating the specific patients. Follow-up data were registered prospectively in the medical records of the participating departments during patient follow-up.

Continuous data are presented as median and interquartile ranges, and categorical data as frequencies and proportions. Kruskal-Wallis test and Fisher's exact test were used as appropriate. Death is a competing risk to recurrence and time-to-event analyses were performed as competing risk analyses obtaining the cumulative incidences for recurrence or death using the "cmprsk" R-package.

Unbiased estimation of marginal or population-averaged treatment effects in observational and non-randomized studies can be obtained through different propensity score methods. Inverse Probability of Treatment Weighting (IPTW) uses the propensity score to weight each patient's data based on the inverse probability of receiving the treatment actually received. IPTW gives unbiased estimates of average treatment effects in time-to-event analyses if no differences in observed baseline covariates exist between the treatment groups. To account for baseline differences between patients in the two groups, stabilized weights truncated at the 0.99 interval were calculated using the "IPW" R-package. The following baseline covariates will be used: age, sex, ASA score, neoadjuvant chemotherapy, tumor morphology, perineural invasion, extramural venous invasion, tumor stage, and serosal invasion. All covariates used and UICC stage, two-way interactions, and squared terms of continuous covariates will be assessed for balance between the CME and the non-CME group after IPTW using the "cobalt" R-package. Absolute mean differences in mean (using standardized mean difference) and proportions (using raw mean difference) below 0.1, and variance ratios between 0.5 and 2 will be accepted. Graphical inspection of the distribution of covariates will be also performed.

The cause-specific hazards and overall survival will be analyzed using Cox regression. Binary outcomes were analyzed using logistic regression. Lymph node yield will be analyzed using linear regression after logarithmic transformation. Number of metastatic lymph nodes will be analyzed using negative binominal regression. All analyses of primary and secondary outcomes will be performed after IPTW. The 95% confidence intervals for the estimates from the original Cox regression IPTW analyses will be constructed after 1000 bootstraps with replacement, and a robust sandwich estimator will be used for the logistic regression analyses in order to ensure correct variance estimation.

All available data will be used. Model assumptions will be checked. A p-value below or equal to 0.05 will be considered significant. All analyses will be performed using R statistical software, version 3.5.1 (R Foundation for Statistical Computing, Vienna, Austria).