PONE-D-24-33134missForestPredict – Missing data imputation for prediction settingsPLOS ONE

Dear Dr. Albu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Please submit your revised manuscript by Jan 11 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

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Santiago Callegari, MD

Academic Editor

PLOS ONE

 Journal requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match.  When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section. 3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information. 

4. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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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

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

Reviewer #1: Yes

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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

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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

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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 this manuscript, the authors present a new package that offers the convenience of random forest powered imputation coupled with prediction. The material presented is robust and complete. The R package is actually already on CRAN, so I think this should complement nicely. I don't think people would wish for more.

I have very few concerns, and I feel this manuscript should be made public without delay.

- Although I realize it's something of an extension of missForest, but it seems like there's a distinct lack of any consideration of the results of the imputation. It's so focused on predictions, although it's only half the process. The manuscript is so complete except for any visualization or analysis of what is imputed. I wouldn't do more than a figure or two. Then readers would have everything in one place.

- In figures 2,3,4,6, and 8 the y axis is labeled "value", perhaps that could be renamed "Error" or "NMSE"?

- Seems like linear and mean imputation is often times the biggest competitor, could consider moving missForestPredict next to them in the plots, because that's what I was always trying to compare... looking across the rows of dots.

- Line 169, should that p_obs be a subscript "obs"?

- I wonder if a lighter application of noise wouldn't have been better... had a huge effect.

- Nice code!

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Reviewer #1: No

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PONE-D-24-33134R1missForestPredict – Missing data imputation for prediction settingsPLOS ONE

Dear Dr. Albu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jun 19 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Leona Cilar Budler

Academic Editor

PLOS ONE

Additional Editor Comments:

Dear authors,

please read all comments and suggestions to improve your paper. Read carefully all comments and revise the paper. Also, check all journal guidelines.

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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: (No Response)

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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: Partly

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

Reviewer #1: Yes

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: Thanks for fully addressing the concerns. It’s a nice paper, good work. I’m sure it will be useful.

Reviewer #2: Manuscript ID: PONE-D-24-33134R1

Title: missForestPredict – Missing data imputation for prediction settings

Dear Authors,

Thank you for your revised submission. After a careful and detailed review of your manuscript, I would like to share several concerns and points that require further attention and clarification. While your manuscript addresses an important topic, there are a number of methodological and conceptual issues that, in my opinion, weaken the current version and limit its clarity and practical utility.

1. Limited Justification for Convergence Criterion

Your proposed use of NMSE as a unified convergence criterion is not sufficiently justified. Specifically, applying the same NMSE thresholding logic to both continuous and categorical variables (via Brier Skill Score transformations) lacks nuance. The metric’s sensitivity to data imbalance—especially for categorical variables—is not discussed, and no rationale is provided for why the default convergence behavior (e.g., stopping when NMSE increases) is ideal across scenarios. This calls into question the robustness of the convergence mechanism.

Recommendation: Provide a more detailed rationale for choosing NMSE across variable types, include sensitivity analyses, and justify the default convergence thresholds through empirical or theoretical reasoning.

2. Scalability and Memory Use Concerns

The method stores imputation models for each variable and iteration, but the implications for scalability in high-dimensional datasets are not discussed. It is unclear how missForestPredict performs when applied to datasets with hundreds or thousands of variables (e.g., genomic or sensor data).

Recommendation: Include runtime and memory usage benchmarks on at least one high-dimensional dataset, or explicitly clarify the limitations and trade-offs of the current implementation in such settings.

3. Lack of Error Analysis

Although your results section reports predictive and imputation performance across scenarios, it fails to offer any qualitative or case-specific analysis of failures, misclassifications, or patterns of poor performance (e.g., in MNAR or MAR scenarios). This weakens interpretability and hinders practical guidance for users.

Recommendation: Include an error analysis section with case studies or diagnostic plots highlighting when and why the method fails or overfits (i.e., "over-imputes").

4. Insufficient Comparison to Contemporary Methods

While the study compares to several common imputation tools (e.g., mice, miceRanger, IterativeImputer), there is no mention or evaluation of modern deep learning or transformer-based imputation frameworks, or techniques that model missingness explicitly as informative.

Recommendation: Acknowledge the limitations of not including such baselines, or ideally, include one or two such recent methods for a fairer benchmark.

5. Ambiguity in Parameter Recommendations

Several user-controllable parameters (e.g., number of trees, max depth, p_obs, p_miss) are introduced but not thoroughly explored or explained in terms of their practical implications or impact on performance.

Recommendation: Add guidance, either empirically or heuristically derived, on how to set these parameters in various scenarios. This could be supported by a sensitivity analysis.

6. Poorly Structured Presentation of Results

The results section is overly long, dense, and at times repetitive. The placement of figures, especially for simulated data, precedes the more practically relevant real-data analysis. Important findings are buried in lengthy narrative without adequate summary or interpretation.

Recommendation: Restructure the results to highlight practical findings more clearly, streamline the narrative, and consider summarizing key patterns in performance using tables or concise bullet-point summaries.

7. Reproducibility and Workflow Transparency

While code is provided, there is no clear description of the workflow pipeline for reproducing the experiments (especially test-time imputation), nor a discussion on how the models generalize across datasets with varying missing data mechanisms.

Recommendation: Include a clear, schematic overview of the full workflow (perhaps in an appendix or figure), and discuss limitations in generalizing trained imputation models to datasets with differing missingness patterns.

In summary, although the manuscript presents an extension to an existing method, it currently lacks the depth and rigor necessary to fully support its contributions and claims. A substantial revision addressing the above concerns is required before this work can be considered for publication.

Sincerely,

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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.

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Reviewer #1: Yes: David L Gibbs

Reviewer #2: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PONE-D-24-33134R2missForestPredict – Missing data imputation for prediction settingsPLOS ONE

Dear Dr. Van Calster,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Oct 17 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Leona Cilar Budler

Academic Editor

PLOS ONE

Journal Requirements:

1. the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. 

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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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: Yes

Reviewer #2: Yes

**********

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

**********

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

**********

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: Nice work, I the manuscript is great. I'm sure people will find it of use, especially in domains that more commonly have missing data.

Reviewer #2: Manuscript Title: missForestPredict – Missing data imputation for prediction settings

Manuscript Number: PONE-D-24-33134R2

Dear Authors,

I have carefully reviewed the revised manuscript and the corresponding “Response to Reviewers” document. The authors have made substantial and appropriate revisions to address the prior concerns raised by reviewers in earlier rounds. The paper now presents a clear, comprehensive, and well-motivated introduction of the missForestPredict algorithm, which meaningfully extends the widely used missForest imputation method to support prediction-time imputation — a feature of high practical value in numerous applied domains, particularly in clinical decision support, finance, and sensor-based analytics.

Remaining Suggestions (Minor, Stylistic/Grammatical):

- There are occasional typographical inconsistencies (e.g., spacing around mathematical operators, inconsistent use of “data are” vs. “data is”).

- Some sentences in the Methods section are long and could be split for improved readability, especially in the step-by-step algorithm description and convergence criterion explanation.

- Ensure uniform formatting of symbols and subscripts in equations (e.g., \(y^{(s)}_{obs}\) vs `y(s) obs`).

- Verify consistency in citation formatting according to journal style (e.g., spaces before references in square brackets).

- A quick language polishing pass may further enhance reader accessibility, particularly for non-specialist audiences.

Conclusion:

The manuscript presents a valuable, novel, and well-executed contribution to the literature on missing data imputation in prediction contexts. The work is methodologically sound, empirically well-validated, and will be of significant interest to applied researchers and practitioners.

Kind regards,

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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: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

missForestPredict – Missing data imputation for prediction settings

PONE-D-24-33134R3

Dear Dr. Van Calster,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Leona Cilar Budler

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

**********

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: Yes

Reviewer #2: Yes

**********

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

**********

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

**********

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: Dear Authors,

Thank you for carefully addressing the comments and suggestions raised during the review process. I have reviewed the revised manuscript, and I am pleased to note that all issues have been satisfactorily resolved. The final version is clear, well-prepared, and ready for publication.

Best regards,

**********

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: No

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