Review for 10.1186/s12876-023-02706-6 (2023)

Title: A novel nomogram for identifying candidates for adjuvant chemotherapy in patients with
stage IB gastric adenocarcinoma
Version: 3 Date: 01 Feb 2023
Reviewer's report:
"STATISTICAL REVIEWER ASSESSMENT:
Is the study design appropriate for the research question (considering whether the analyzed
population accurately reflects the design and whether you see any problems with control/
comparison groups, e.g., likely confounders)?
No - there are minor issues
Are methodologies adequate and well implemented (considering whether assumptions are
addressed and whether analyses are robust)?
No - there are minor issues
Are the analyses adequately communicated (considering whether reporting details are adequate
and whether figures and tables are well labeled and described)?
No - there are minor issues
Does the interpretation accurately reflect the analyses without overstatement (considering
whether limitations/bias are acknowledged and whether accurate descriptors, e.g., 'significant',
are used)?
No - there are minor issues
Could an appropriately REVISED version of this work represent a statistically sound
contribution?
Probably - with minor revisions
STATISTICAL REVIEWER COMMENTS:
I was not a previous reviewer of the manuscript, but I have read the previous reviews and the
responses, as well as the manuscript itself. The authors have addressed some of the reviewer
concerns and incorporated changes into the paper. Many of the methods used in the paper are best
practice methods for developing a nomogram, such as presenting the operating characteristics
of the model, calibration plots, and decision curves. However, there are two related statistical
comments from prior reviewers that seem unanswered and could be better explained. Further,
I had some additional questions about the appropriateness of using the SEER dataset for an
analysis of chemotherapy outcomes. Some more comments follow.
REQUESTED REVISIONS:
Comment 1) The previous reviewers (reviewer 1 comment 6 and reviewer 2 comment 2) had
some concerns about the stratification of patients into those who would be high risk/low risk, or
benefit/no benefit patients. Reviewer 2 stated: “the author used logistic regression analysis and
multivariate Cox regression. In my opinion, this is repetitive work. In fact, I prefer the method of
Wang et al. To establish nomogram and distinguish high-risk and low-risk groups through Cox
regression analysis.” I am also confused about the need for the logistic regression of benefit (not
the regression used for the propensity score model). The authors could clarify what the outcome
for the “benefit” logistic regression model is. If the outcome is binary death in the dataset, then
it is unclear how this could be used as a “benefit” model, as everyone will eventually die (hence,
the probability of death is 100% over time). It would seem that the goal is to allow people to
live longer, because we cannot prevent eventual death in any person.
New Comment 2) The authors may have signed a data use agreement that states that the
dataset does not support a comparison of chemotherapy outcomes because the chemotherapy
data is unreliable. The authors should include in the manuscript whether their analyses are
consistent with NCI statements about the reliability of the chemotherapy data. The data
use agreement is here: https://seer.cancer.gov/data-software/documentation/seerstat/nov2021/
treatment-limitations-nov2021.html
New Comment 3) The authors could clarify which variables in the SEER dataset were used to
distinguish neoadjuvant versus adjuvant chemotherapy.
New Comment 4) The propensity score approach is nice, but the nomogram is only relevant to
people who are similar to those included in the propensity score analysis. This is a limitation of
the approach, and could be mentioned as a limitation.
New Comment 5) The figure captions could be more detailed. For example, the terms “all” and
“none” are never defined for the decision curves.
New Comment 6) “Multivariate” might better be termed “multivariable” throughout the paper,
as statistical multivariate models often refer to techniques such as principal components analysis,
which are different.