PONE-D-22-32989DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulationPLOS ONE

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Additional Editor Comments:

(1) There should be description section that presents step-by-step development and also the use of a flow chart is encouraged;

(2) Supplementary Material 2 is not available

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: N/A

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In the manuscript “DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation”, Ghadi et al. present a python based framework for DAG-based data simulations: DagSim which integrates complex data types and graphical modelling (that is, neither restricted by variable types nor functional forms). More so, the simulation model is defined in the YAML format promoting transparency. The authors demonstrated the framework on two use cases which are available on Github (examples, where basic metadata variables are controlling the shapes in an image and patterns in bio-sequences).

Major comments:

Table 1: An overview of the current frameworks that offer simulation functionalities. The authors provided detailed information showing how these tools/frameworks differ.

1. However, they are not clear on the selection criteria they used for the presented current frameworks (are these all the existing tools/packages?)

2. When simulating simple DAGs (discrete and continuous data), how does dagSim compare to the other simulation frameworks (for example, simMixedDAG and dagR)?

Minor comment:

Table 1: An overview of the current frameworks that offer simulation functionalities.

• dagR does not have any simulation utilities, however, Duan et al. 2021 (Reflection on modern methods: understanding bias and data analytical strategies through DAG-based data simulations, https://academic.oup.com/ije/article/50/6/2091/6272915) demonstrated that dagR can be useful in addressing selection and information bias analysis. Please, clarify if dagitty and dagR doesn’t have simulation utilities

Reviewer #2: The authors introduce a novel Direct Acyclic Graph (DAG) - based data simulation Python library called DagSim. They survey existing data simulation tools, discussing them and highlighting a common limitation i.e. that their inputs are limited to numeric variables and specified functional forms. DagSim has the unique advantage of having the capacity to use variables and functional relations of any form. Furthermore, it is modular and easily updatable. An implementation of DagSim has been made available on Github.

The survey is instructive and potentially a worthy contribution. I would suggest the following modifications:

-Table 1, as provided, is a very useful summary of existing tools, their capabilities, and limitations. I would recommend an additional column in which associated citations for the tools are provided.

-In addition to Figure 1, it would be useful to present a demonstration of an example of the limitation of the other tools’ input variable limitations, and DagSim’s advantage in processing more complex data types.

Minor edits:

-pages need to be numbered

-there is a citation in the Abstract. That needs to be removed.

-In the last section under “Use”, “Additionally, it is known that e.g. the age of a patient leaves a mark on the AIRR (19)…” needs to be corrected

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Reviewer #1: No

Reviewer #2: No

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DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation

PONE-D-22-32989R1

Dear Dr. Al Hajj,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Emmanuel S Adabor

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: N/A

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: George K. Acquaah-Mensah

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