Conditional Survival and Nomogram for Elderly Non-Metastatic Colon Cancer Patients Following Colectomy

Reviewer #1 (Public Review):

Summary:

This study assessed conditional survival in elderly patients with non-metastatic colon cancer who underwent colectomy. The study found that 5-year conditional overall survival rates exhibited a slight increase initially, followed by a decrease over time. In contrast, 5-year conditional colon-specific survival rates consistently improved over the same period. Nomograms were developed to predict survival probabilities at baseline and for patients surviving 1, 3, and 5 years post-diagnosis, with good predictive performance. The study concludes that conditional survival offers valuable insights into medium- and long-term survival probabilities for these patients.

Strengths:

The strengths of this study include robust study design, methodology, statistical analysis, and interpretation of the findings. Utilizing a well-known database for the analysis is another strength. Differentiating overall survival and colon-specific survival rates could be another one. Focusing on elderly patients with this condition is another major point. Providing nomograms for an easier implication of the findings in real-world clinical practice is a major strength of the study.

Weaknesses:

Relying on only one database of patients and narrowing down the population to only elderly patients who underwent colectomy could be mentioned as a weakness. Less generalizability of the findings for other populations and not including more diverse databases is a major weakness of this study. The good predictive capabilities of the developed tools are another weakness that could be improved to be excellent.

Reviewer #2 (Public Review):

Summary:

The authors assessed the conditional survival of elderly patients with non-metastatic colon cancer who had survived a certain length of time after colectomy. They used data from the Surveillance, Epidemiology, and End Results (SEER) registry to conduct a conditional survival analysis providing estimates of conditional survival rates as well as an analysis of which variables were most important for survival at baseline, one year, three years, and five years.

Strengths:

- The authors used SEER data, providing them with long-term follow-up, and thoroughly considered a wide range of variables related to cancer mortality.
- The authors did a thorough job of assessing the predictive ability of their models.
- The authors used conditional survival, providing estimates of survival that are meaningful for patients/physicians, making them useful for clinical practice.

Weaknesses:

- The paper would have benefited from a more thorough explanation of why the methods were improvements on existing approaches.

- This study was primarily interested in cancer mortality, and compared it to the secondary outcome of death from any cause. The study would have benefited from modeling death from non-cancer causes (the competing risk) in addition to death from colon cancer, rather than comparing only to the composite endpoint of death from any cause.

- When considering a cause-specific hazard, as done with cancer survival in this paper, it would be better to consider the cumulative incidence function rather than Kaplan Meier, since it does not assume the independence of the events like Kaplan Meier does. For this reason, the paper would benefit from focusing on the results of the adjusted cause-specific hazard models (rather than the unadjusted conditional survival estimates done using Kaplan Meier estimates shown in Figure 1 and conducting a parallel analysis for death from other causes.

- The authors mention that they consider disparities using a log-rank test. For the same reason as above, is not the best approach when dealing with competing risks as it depends on Kaplan Meier curves. The log-rank test may be fine if there is no strong dependence between the two causes of death, but the paper would benefit from some discussion of that choice, or sensitivity analysis by comparison to other approaches.

- The variables for the adjusted models were chosen with univariate Cox regression analysis, with any variables having a p-value less than 0.05 being included in the adjusted. Another approach, which may have made the models more easily comparable, would be to choose the variables that are relevant based on prior literature and include them in the multivariate model regardless of significance. The paper would benefit from a discussion of what is gained by excluding some variables from some models.

Reviewer #3 (Public Review):

Summary:

This article uses a subset of data from the SEER cancer registry to develop nomograms, a patient-facing risk prediction tool, for predicting overall and cancer-specific survival in elderly patients who underwent colectomy for the treatment of non-metastatic colon cancer. A unique contribution is the intent to provide conditional predictions, i.e. given that you have survived for x years from your diagnosis, what is your probability of survival for an additional y years? Although the goal is a useful one, the approach is unfortunately hampered by some important weaknesses.

Strengths:

Predicting conditional overall survival is a useful, patient-oriented goal.

The data source is the high-quality SEER cancer registry.

Weaknesses:

Using Kaplan-Meier methodology to estimate the survival distribution for a time-to-event in the presence of another competing time-to-event (in this case: estimating colon-specific survival in the presence of death from other causes) will generally over-estimate the event rate. The reported colon-specific survival probabilities are probably biased downwards from their true values. See https://pubmed.ncbi.nlm.nih.gov/10204198/

A similar concern would apply to the use of the cause-specific Cox model, and thus also the nomogram, to predict absolute (conditional) survival.

The p-value-based methodology for determining which predictors should be included in the nomogram is rudimentary. More modern variable selection methods, e.g. the Lasso, would have been preferred.

Related to the above comment, some predictors are present for the conditional survival nomogram for time t, then absent for time t+1, then present again for time t+2. A cancer site is an example of such a predictor. From a face validity perspective, this doesn't really make sense. Ideally, predictors would not enter, then leave, and then re-enter a model.

Many observations were excluded due to missingness in predictors, e.g. >10000 were excluded to due unknown CEA (Supplementary Figure 1). Given the number of observations dropped due to missingness in the predictors, ideally an attempt would have been made to incorporate the partial information available in these data.

Details are lacking on how the AUCs and Brier scores were calculated in the presence of censoring / competing events, which limits the reader's understanding of the results.

It is not clear why a nomogram would be preferred to an online risk prediction calculator.