Title: A Nomogram for predicting cause-specific mortality among patients with cecal
carcinoma: A study based on SEER database
Version: 0 Date: 13 Feb 2023
Reviewer:  Ling Tong
Reviewer's report:
This study estimated the probabilities of cause-specific mortality in patients with cecal
carcinoma (CC) and to build a competing risk nomogram to quantify the effects of multiple
variables. Independent predictor variables were pathologic classification of cancer, tumor grade,
AJCC stage, T stage, etc. The nomogram can help clinicians choose appropriate options for
rehabilitation and follow-up strategies conducive to the survival of CC patients. The study is
well conducted and the level of details is sufficient. The manuscript is well written and easy
to understand.
My questions are mostly focused on the explanation of model and results:
1. In discussion, can you explain the reason why the Kaplan-Meier curve and Cox proportional
hazard models tend to produce inaccurate estimates? If you could compare the prediction
with the Kaplan-Meier curve or Cox proportional hazard models, that would provide more
information about how the model would overestimate or underestimate the mortality.
2. I believe the tumor grade, tumor size, and AJCC stage are highly correlated. How do you
address the collinearity? Do you think the high correlation of these variables may bias the CC
mortality prediction?
3. In conclusion, you mentioned that the nomogram constructed in the present study can help
clinicians choose appropriate options for rehabilitation and follow-up strategies conducive to
the survival of CC patients. What demographic or clinical factors do you suggest focusing on
in the process of making a diagnosis?
4. How would you interpret the AUC of 0.841, 0.862 and 0.852 for 1-, 3-, and 5-year survival
prediction? Are they sufficiently accurate to be applied in clinical practice to provide reliable
clinical decision support for clinicians?
Title: A Nomogram for predicting cause-specific mortality among patients with cecal
carcinoma: A study based on SEER database
Version: 0 Date: 13 Feb 2023
Reviewer:  Ling Tong
Reviewer's report:
This study estimated the probabilities of cause-specific mortality in patients with cecal
carcinoma (CC) and to build a competing risk nomogram to quantify the effects of multiple
variables. Independent predictor variables were pathologic classification of cancer, tumor grade,
AJCC stage, T stage, etc. The nomogram can help clinicians choose appropriate options for
rehabilitation and follow-up strategies conducive to the survival of CC patients. The study is
well conducted and the level of details is sufficient. The manuscript is well written and easy
to understand.
My questions are mostly focused on the explanation of model and results:
1. In discussion, can you explain the reason why the Kaplan-Meier curve and Cox proportional
hazard models tend to produce inaccurate estimates? If you could compare the prediction
with the Kaplan-Meier curve or Cox proportional hazard models, that would provide more
information about how the model would overestimate or underestimate the mortality.
2. I believe the tumor grade, tumor size, and AJCC stage are highly correlated. How do you
address the collinearity? Do you think the high correlation of these variables may bias the CC
mortality prediction?
3. In conclusion, you mentioned that the nomogram constructed in the present study can help
clinicians choose appropriate options for rehabilitation and follow-up strategies conducive to
the survival of CC patients. What demographic or clinical factors do you suggest focusing on
in the process of making a diagnosis?
4. How would you interpret the AUC of 0.841, 0.862 and 0.852 for 1-, 3-, and 5-year survival
prediction? Are they sufficiently accurate to be applied in clinical practice to provide reliable
clinical decision support for clinicians?
Please review this paper.
user
Title: A Nomogram for predicting cause-specific mortality among patients with cecal
carcinoma: A study based on SEER database
Version: 0 Date: 13 Feb 2023
Reviewer:  Ling Tong
Reviewer's report:
This study estimated the probabilities of cause-specific mortality in patients with cecal
carcinoma (CC) and to build a competing risk nomogram to quantify the effects of multiple
variables. Independent predictor variables were pathologic classification of cancer, tumor grade,
AJCC stage, T stage, etc. The nomogram can help clinicians choose appropriate options for
rehabilitation and follow-up strategies conducive to the survival of CC patients. The study is
well conducted and the level of details is sufficient. The manuscript is well written and easy
to understand.
My questions are mostly focused on the explanation of model and results:
1. In discussion, can you explain the reason why the Kaplan-Meier curve and Cox proportional
hazard models tend to produce inaccurate estimates? If you could compare the prediction
with the Kaplan-Meier curve or Cox proportional hazard models, that would provide more
information about how the model would overestimate or underestimate the mortality.
2. I believe the tumor grade, tumor size, and AJCC stage are highly correlated. How do you
address the collinearity? Do you think the high correlation of these variables may bias the CC
mortality prediction?
3. In conclusion, you mentioned that the nomogram constructed in the present study can help
clinicians choose appropriate options for rehabilitation and follow-up strategies conducive to
the survival of CC patients. What demographic or clinical factors do you suggest focusing on
in the process of making a diagnosis?
4. How would you interpret the AUC of 0.841, 0.862 and 0.852 for 1-, 3-, and 5-year survival
prediction? Are they sufficiently accurate to be applied in clinical practice to provide reliable
clinical decision support for clinicians?
assistant