Title: The effect of menopausal hormone therapy on gastrointestinal cancer risk and mortality
in South Korea: a population based cohort study
Version: 4 Date: 05 Jun 2021
Reviewer's report:
I appreciate the authors for their previous revisions. Now I only have a suggestion about the
propensity score matching, which was also indicated by Reviewer#2.
I agree with Reviewer#2 that the methods for propensity score matching should be described
in more detail. Although the authors argued that only age, income, and region were matched in
order to simplify the propensity score matching as much as possible, I do not think including
other variables to calculate the propensity score is a complex work that should be simplified. To
my knowledge, a propensity score is calculated by a logistic regression model to estimate the
probability of receiving MHT in this study. Suppose other variables in Table 1 may influence
the probability of receiving MHT or maybe possible confounders. In that case, I suggest also
put them into the logistic regression model to calculate the propensity score.
Further, the propensity score matching details were still not mentioned in the revised manuscript
(e.g., the algorithm for matching, using caliper for matching or not, with/without replacement).
I suggest providing the relevant details in the Method section.
Title: The effect of menopausal hormone therapy on gastrointestinal cancer risk and mortality
in South Korea: a population based cohort study
Version: 4 Date: 05 Jun 2021
Reviewer's report:
I appreciate the authors for their previous revisions. Now I only have a suggestion about the
propensity score matching, which was also indicated by Reviewer#2.
I agree with Reviewer#2 that the methods for propensity score matching should be described
in more detail. Although the authors argued that only age, income, and region were matched in
order to simplify the propensity score matching as much as possible, I do not think including
other variables to calculate the propensity score is a complex work that should be simplified. To
my knowledge, a propensity score is calculated by a logistic regression model to estimate the
probability of receiving MHT in this study. Suppose other variables in Table 1 may influence
the probability of receiving MHT or maybe possible confounders. In that case, I suggest also
put them into the logistic regression model to calculate the propensity score.
Further, the propensity score matching details were still not mentioned in the revised manuscript
(e.g., the algorithm for matching, using caliper for matching or not, with/without replacement).
I suggest providing the relevant details in the Method section.
Please review this paper.
user
Title: The effect of menopausal hormone therapy on gastrointestinal cancer risk and mortality
in South Korea: a population based cohort study
Version: 4 Date: 05 Jun 2021
Reviewer's report:
I appreciate the authors for their previous revisions. Now I only have a suggestion about the
propensity score matching, which was also indicated by Reviewer#2.
I agree with Reviewer#2 that the methods for propensity score matching should be described
in more detail. Although the authors argued that only age, income, and region were matched in
order to simplify the propensity score matching as much as possible, I do not think including
other variables to calculate the propensity score is a complex work that should be simplified. To
my knowledge, a propensity score is calculated by a logistic regression model to estimate the
probability of receiving MHT in this study. Suppose other variables in Table 1 may influence
the probability of receiving MHT or maybe possible confounders. In that case, I suggest also
put them into the logistic regression model to calculate the propensity score.
Further, the propensity score matching details were still not mentioned in the revised manuscript
(e.g., the algorithm for matching, using caliper for matching or not, with/without replacement).
I suggest providing the relevant details in the Method section.
assistant