Title: Investigation of Subpopulations in Crohn Inflammatory bowel disease Patients
Version: 3 Date: 09 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the methodology mentions that the study includes treatment as a covariate, it would
benefit from more detail on the specific characteristics of the patient population. For example,
how were patients selected for this study? What were the baseline characteristics (age, sex,
disease duration, comorbidities) of the patients, and were these factors adjusted for in the
analysis? More information on the patient demographics and inclusion criteria would help
readers assess the generalizability of the findings.
-The concept of "mixture components" is central to the study, yet it could be explained
more clearly in the text. While it is stated that two subpopulations were identified, the
specific characteristics of these subpopulations (e.g., differences in disease severity, response
to treatment, etc.) are not fully explored. A deeper interpretation of what the two components
represent in terms of clinical outcomes would help make the results more meaningful. How do
these components differ in terms of patient behavior or clinical response?
-The finding that treatment was not significant in explaining the subpopulations is intriguing but
underexplained. Why was treatment not significant in either component? It would be helpful to
explore this result more thoroughly, particularly considering that treatment is often expected to
have a measurable effect in IBD patients. Are there other variables or treatment-related factors
that might better explain this lack of significance?
While the use of statistical models is appropriate, the presentation of results could be more
detailed. For example, when discussing the Poisson regression models (Equations 3* and 4*),
it would be helpful to include p-values or confidence intervals for the regression coefficients.
-This would allow readers to better assess the strength of the effects observed. Additionally,
the explanation of the deviance values and the significance of the change in deviance between
the single and two-component models is useful but could be expanded to better highlight the
statistical significance and practical implications of these results.
-There is no mention of model validation or checking for model assumptions, such as
overdispersion or residual analysis. While the finite mixture model is appropriate, it would be
useful to provide more evidence that the model fits well to the data (e.g., through residual plots
or diagnostic checks). Did the authors test the model’s goodness of fit, and if so, how did the
model perform?
Title: Investigation of Subpopulations in Crohn Inflammatory bowel disease Patients
Version: 3 Date: 09 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the methodology mentions that the study includes treatment as a covariate, it would
benefit from more detail on the specific characteristics of the patient population. For example,
how were patients selected for this study? What were the baseline characteristics (age, sex,
disease duration, comorbidities) of the patients, and were these factors adjusted for in the
analysis? More information on the patient demographics and inclusion criteria would help
readers assess the generalizability of the findings.
-The concept of "mixture components" is central to the study, yet it could be explained
more clearly in the text. While it is stated that two subpopulations were identified, the
specific characteristics of these subpopulations (e.g., differences in disease severity, response
to treatment, etc.) are not fully explored. A deeper interpretation of what the two components
represent in terms of clinical outcomes would help make the results more meaningful. How do
these components differ in terms of patient behavior or clinical response?
-The finding that treatment was not significant in explaining the subpopulations is intriguing but
underexplained. Why was treatment not significant in either component? It would be helpful to
explore this result more thoroughly, particularly considering that treatment is often expected to
have a measurable effect in IBD patients. Are there other variables or treatment-related factors
that might better explain this lack of significance?
While the use of statistical models is appropriate, the presentation of results could be more
detailed. For example, when discussing the Poisson regression models (Equations 3* and 4*),
it would be helpful to include p-values or confidence intervals for the regression coefficients.
-This would allow readers to better assess the strength of the effects observed. Additionally,
the explanation of the deviance values and the significance of the change in deviance between
the single and two-component models is useful but could be expanded to better highlight the
statistical significance and practical implications of these results.
-There is no mention of model validation or checking for model assumptions, such as
overdispersion or residual analysis. While the finite mixture model is appropriate, it would be
useful to provide more evidence that the model fits well to the data (e.g., through residual plots
or diagnostic checks). Did the authors test the model’s goodness of fit, and if so, how did the
model perform?
Please review this paper.
user
Title: Investigation of Subpopulations in Crohn Inflammatory bowel disease Patients
Version: 3 Date: 09 Dec 2024
Reviewer:  Anwar Ashraf Abouelnasr
Reviewer's report:
Dear Authors, please address the following points below
-While the methodology mentions that the study includes treatment as a covariate, it would
benefit from more detail on the specific characteristics of the patient population. For example,
how were patients selected for this study? What were the baseline characteristics (age, sex,
disease duration, comorbidities) of the patients, and were these factors adjusted for in the
analysis? More information on the patient demographics and inclusion criteria would help
readers assess the generalizability of the findings.
-The concept of "mixture components" is central to the study, yet it could be explained
more clearly in the text. While it is stated that two subpopulations were identified, the
specific characteristics of these subpopulations (e.g., differences in disease severity, response
to treatment, etc.) are not fully explored. A deeper interpretation of what the two components
represent in terms of clinical outcomes would help make the results more meaningful. How do
these components differ in terms of patient behavior or clinical response?
-The finding that treatment was not significant in explaining the subpopulations is intriguing but
underexplained. Why was treatment not significant in either component? It would be helpful to
explore this result more thoroughly, particularly considering that treatment is often expected to
have a measurable effect in IBD patients. Are there other variables or treatment-related factors
that might better explain this lack of significance?
While the use of statistical models is appropriate, the presentation of results could be more
detailed. For example, when discussing the Poisson regression models (Equations 3* and 4*),
it would be helpful to include p-values or confidence intervals for the regression coefficients.
-This would allow readers to better assess the strength of the effects observed. Additionally,
the explanation of the deviance values and the significance of the change in deviance between
the single and two-component models is useful but could be expanded to better highlight the
statistical significance and practical implications of these results.
-There is no mention of model validation or checking for model assumptions, such as
overdispersion or residual analysis. While the finite mixture model is appropriate, it would be
useful to provide more evidence that the model fits well to the data (e.g., through residual plots
or diagnostic checks). Did the authors test the model’s goodness of fit, and if so, how did the
model perform?
assistant