Bayesian and non-bayesian analysis for stress-strength model based on progressively first failure censoring with applications

Salem A. Alyami; Amal S. Hassan; Ibrahim Elbatal; Olayan Albalawi; Mohammed Elgarhy; Ahmed R. El-Saeed

doi:10.1371/journal.pone.0312937

Peer Review History

Original SubmissionJune 4, 2024
23 Jul 2024 Decision Letter - Mudassar Rashid, Editor PONE-D-24-22736Bayesian and Non-Bayesian Analysis for Stress-Strength Model Based on Progressively First Failure Censoring with ApplicationsPLOS ONE Dear Dr. Elgarhy, 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 Sep 06 2024 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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If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] 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 ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 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 ****** 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 ****** 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: 1. The breadth of topics covered, from censoring techniques to the application of SS models in various fields, demonstrates a thorough understanding of the subject matter. This provides a solid background for readers new to the topic while offering depth for those familiar with the field. The combination of classical and Bayesian estimation approaches, along with the use of MCMC, promises to offer significant advancements in the field. 2. The method leverages the well-established theory of MLE to derive parameter estimates. The use of the observed Fisher information matrix provides a robust framework for assessing the variance and constructing confidence intervals. 3. however, the iterative nature of the Newton-Raphson method can be computationally intensive, especially for large datasets. The normal approximation for constructing CIs may not be accurate for small sample sizes or for parameters that are near the boundary of the parameter space. 4. Bayesian estimation allows for the incorporation of prior information, which can be particularly useful when dealing with limited data. The use of the Metropolis-Hasting algorithm facilitates the sampling from complex posterior distributions. 5. However, The choice of priors can significantly influence the results, and inappropriate priors can lead to biased estimates. The computational complexity of the Metropolis-Hasting algorithm can be high, particularly for large datasets or complex models. 6. The paper lacks a detailed explanation of the choice of specific parameter values (,δ1,δ2). Providing context or rationale behind these choices would strengthen the study’s relevance. 7. Assuming equal sample sizes (n1, n2) and stages (m1, m2) simplifies the simulation but may not reflect real-world scenarios. Investigating unequal sample sizes and stages could offer a more comprehensive understanding. 8. While MSE and Avr. are standard performance metrics, additional metrics like bias or variance could provide a deeper analysis. 9. The study mentions that INP performs better overall but does not delve into the reasons behind this trend. A deeper exploration of why INP outperforms N-INP would enhance the findings’ interpretability. Reviewer #2: The manuscript is a very interesting and written in the standard format of a theoretical article. Congratulations to authors for making a valuale contribution and the manuscript is in a perfect shape. The manuscript may be accpeted in its current form. ****** 6. 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: Yes: Dr. Shahid Akbar Reviewer #2: Yes: Asad Ul Islam Khan ******** [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. https://doi.org/10.1371/journal.pone.0312937.r001
Revision 1
16 Aug 2024 Author Response Response to the reviewer’s comments Submission ID PONE-D-24-22736 Bayesian and Non-Bayesian Analysis for Stress-Strength Model Based on Progressively First Failure Censoring with Applications PLOS ONE Dear Editors and Reviewers: First, the authors would like to thank the Editor in Chief, Associate editor, Lead editor, Academic editor and Anonymous referees for spending their time on the manuscript carefully. The comments of the editors and reviewers are valuable. We have taken all the suggestions/comments positively and did our best to incorporate all these suggestions in the revised version. Our point wise responses to the reviewer’s comments/suggestions are given below. Response to Editor Dear Sir/Mam, we appreciate your time in handling our paper and providing suggestions for improvement. We believe the quality of the revised version has considerably improved and hope that you find the revised manuscript satisfactory this time. Reply to the Reviewer #1 The breadth of topics covered, from censoring techniques to the application of SS models in various fields, demonstrates a thorough understanding of the subject matter. This provides a solid background for readers new to the topic while offering depth for those familiar with the field. The combination of classical and Bayesian estimation approaches, along with the use of MCMC, promises to offer significant advancements in the field. Answer: we appreciate your feedback, many thanks for your positive comment. ========================== The method leverages the well-established theory of MLE to derive parameter estimates. The use of the observed Fisher information matrix provides a robust framework for assessing the variance and constructing confidence intervals. Answer: we appreciate your feedback, many thanks for your positive comment. ========================== however, the iterative nature of the Newton-Raphson method can be computationally intensive, especially for large datasets. The normal approximation for constructing CIs may not be accurate for small sample sizes or for parameters that are near the boundary of the parameter space. Answer: we appreciate your feedback, we remove the sample size n=20 and m=15 from the article and replace them with another two large sets as seen in Table 1. Indeed, sample sizes and patterns of POFIF-CS were selected to ensure that the number of stages (m1 and m2) for each type of stress is sufficient to form CIs. The results of simulation study for old and new samples are provided in Tables 2 to 9. =============================== Bayesian estimation allows for the incorporation of prior information, which can be particularly useful when dealing with limited data. The use of the Metropolis-Hasting algorithm facilitates the sampling from complex posterior distributions. Answer: we appreciate your feedback, many thanks for your positive comment. ==================================== However, the choice of priors can significantly influence the results, and inappropriate priors can lead to biased estimates. The computational complexity of the Metropolis-Hasting algorithm can be high, particularly for large datasets or complex models. Answer: we appreciate your feedback, we agree with this fact, so in this study a convergence diagnostic of the Metropolis-Hastings algorithm has been assessed to ensure reliable results as provided in Figure 1. These diagnostics included trace plots, and histogram. The results, presented in Figure 1, indicate satisfactory convergence of the Markov chains." ==================================== The paper lacks a detailed explanation of the choice of specific parameter values (v,δ1,δ2). Providing context or rationale behind these choices would strengthen the study’s relevance. Answer: we appreciate your feedback, the selection of the assumed parameters allows for estimating the stress strength with values ranging from zero to one. Indeed, for the first and second case the true value of stress strength parameter ϑ=0.874, which is close to one, while in the third and fourth case the true value of stress strength parameter ϑ=0.3405, which is small value of reliability. Also, this issue is explained in simulation study. ==================================== Assuming equal sample sizes (n1, n2) and stages (m1, m2) simplifies the simulation but may not reflect real-world scenarios. Investigating unequal sample sizes and stages could offer a more comprehensive understanding. Answer: we appreciate your feedback, new patterns of POFIF-CS were selected and defined, including differences in sample sizes while keeping the number of stages equal. This resulted in a total of 12 patterns as showed in Table (1). Additionally, two cases for the number of groups h were specified. It should be noted that this required revising the practical part in the attached table for the new sample sizes. ==================================== While MSE and Avg. are standard performance metrics, additional metrics like bias or variance could provide a deeper analysis. Answer: we appreciate your feedback, we agree with you Dear Prof. on this point, but the research may expand to include a number of tables equal to twice the current results. Moreover, selecting a measure for the mean (Avg.) can be considered equivalent to addressing bias, and choosing a measure for the MSE can be considered equivalent to addressing variance. We have tried to balance between the mean and the variance of the obtained results Also, the MSE is a crucial metric for evaluating the performance of a model or estimator. It quantifies the average squared difference between the predicted (or estimated) values and the actual observed values. It is defined by MSE = variance + (bias)2. In a future study, we will take this point into consideration. ==================================== The study mentions that INP performs better overall but does not delve into the reasons behind this trend. A deeper exploration of why INP outperforms N-INP would enhance the findings’ interpretability. Answer: we appreciate your feedback, but dear Prof. as shown in the tables of results and the convergence plots for the MCMC study, in both informative and non-informative cases, it became clear that the results in the informative case were better. Therefore, we recommend using the informative case for estimation. This does not preclude the possibility of better informative cases, but determining which cases are better requires a sensitivity analysis. However, we were able to obtain a more efficient estimate that serves the current purpose. The main reasons for this are primarily twofold: the prior distribution assumed in this study was a Gamma distribution, and the second reason is the method of determining the hyper-parameter values (elicitation), which were derived from a previous study. We note and emphasize once again that there is still the potential for better informative cases, but this requires effort beyond the scope of this research, which is not the aim of this study. ==================================== Reply to the Reviewer #2 Reviewer #2: The manuscript is a very interesting and written in the standard format of a theoretical article. Congratulations to authors for making a value contribution and the manuscript is in a perfect shape. The manuscript may be accepted in its current form. Answer: Answer: we appreciate your feedback, many thanks for your positive report. Attachments Attachment Submitted filename: Response to the reviewers comments PLOS ONE (26-7-2024).docx https://doi.org/10.1371/journal.pone.0312937.r002
16 Oct 2024 Decision Letter - Inés P. Mariño, Editor Bayesian and Non-Bayesian Analysis for Stress-Strength Model Based on Progressively First Failure Censoring with Applications PONE-D-24-22736R1 Dear Dr. Elgarhy, 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. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. 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, Inés P. Mariño, Ph.D. 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 #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 #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? 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 #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 #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 #2: There are no further comments, so the manuscript may be accepted as it is. The authors have incorporated all the comments from the First round review. ****** 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 #2: Yes: ASAD UL ISLAM KHAN ******** https://doi.org/10.1371/journal.pone.0312937.r003
Formally Accepted
21 Nov 2024 Acceptance Letter - Inés P. Mariño, Editor PONE-D-24-22736R1 PLOS ONE Dear Dr. Elgarhy, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Inés P. Mariño Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0312937.r004

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