Effect size, sample size and power of forced swim test assays in mice: Guidelines for investigators to optimize reproducibility

Neil R. Smalheiser; Elena E. Graetz; Zhou Yu; Jing Wang

doi:10.1371/journal.pone.0243668

Peer Review History

Original SubmissionNovember 23, 2020
20 Jan 2021 Decision Letter - Patricia Souza Brocardo, Editor PONE-D-20-36904 Effect size, sample size and power of forced swim test assays in mice: Guidelines for investigators to optimize reproducibility PLOS ONE Dear Dr. Smalheiser, 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 Mar 06 2021 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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Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Patricia Souza Brocardo, Ph.D. 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 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: No 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: With a particular emphasis on the relationship between sample size, statistical power and effect size, the authors explored a sample of published research articles in order to shed light into these topics, as well as to discuss reproducibility and the behavior of scientists in planning their experiments. The sample (16 published articles) was selected from a population of mouse-based experimental studies aimed to investigate potential antidepressant molecules in the Forced Swim Test (FST), which has been used to test putative antidepressants. In brief, the authors observed extremely large mean effect sizes in the analyzed sample (1.5 – 2.5 in Cohen’s d units). The average experimental number per group (7-10 animals), although relatively small, was considered enough to achieve adequate power to consistently detect effects of this magnitude (1.5 – 2.5 in Cohen’s d units). In order to detect large effects (0.8 in Cohen’s d units) when the true effect of a test agent is unknown, the authors propose that proper prospective design would require approximately 21-26 animals per group, which is a number significantly higher than the current average (7-10 animals). The authors discuss their data emphasizing that they provide parameters and guidance for investigators seeking to carry out prospective power estimation for the FST. They also state that the investigators are not able to perceive intuitively whether or not a given sample size is adequate for a given experiment, which seems to be related, at least partially, with the extremely large mean effect sizes detected (1.5 – 2.5 in Cohen’s d units). The manuscript, which is properly written and presented, brings out an interesting view on the current scenario of FST, search for antidepressants, sample size and statistical power. However, there are points that deserve major consideration in order to improve the manuscript: 1 - In some parts of the manuscript (lines 46 and 327), the authors argue that underpowered studies have a tendency to show a very high false-positive rate. It is important to present information on the (also high) false-negative rate in underpowered studies, particularly in studies with small (but significant) effects. 2 - Does a 16-articles sample have enough power to properly represent the population of 737 articles? Based on statistical foundation, the authors need to discuss this topic in order to inform the reader about the reliability of their data, which derived from a relatively small sample (n = 16). 3 - In the Materials and Methods Section, line 107, it is informed “When sample size was not provided directly, it was inferred from t-test or ANOVA parameters and divided equally among treatment and groups, rounding up to the nearest whole number if necessary. If only a range for sample size was provided, the average of the range was assigned to all treatments, and rounded up if needed”. This also represents a limitation of the study because there are a huge number of articles with different experimental number in their different experimental groups. This should, at least, be informed in the limitations of the study (line 314). 4 - Line 128: Was the effect size of known antidepressants normally distributed in the sample (16 articles)? Please, add more on that. 5 - It would be informative to show data concerning the dispersion of immobility-time within and between studies, at least for controls. 6 - Line 169: “Interestingly, the assays in depressed models showed a smaller coefficient of variation (i.e., standard deviation divided by the mean) than in naïve mice”. This seems to be true only for "test agents", but not for "antidepressants". Please, revise the sentence. In addition, discuss it. 7 - Table 4: With respect to “antidepressants/naïve”, the mean effect size of 2.554 is considered high. However, several “known antidepressants" have no clinical effects in a significant number of patients (refractory). Even though 1.729 (“test agents/naïve”) is also considered a high mean effect size, is it possible to predict a worse clinic effect (in patients) compared to “antidepressants/naïve” (effect size 2.554)? In my opinion, discussions concerning the biological relevance of the data found in the evaluated articles would improve the present manuscript relevance. 8 - Line 220: “This might potentially be explained by high variability of baseline values across heterogeneous experiments and laboratories”. This is not shown. Please, see comment 5. 9 - Line 276: “In summary, for testing an unknown agent (e.g., chosen without prior experimental evidence or as part of a high-throughput screen), with minimum effect size = 0.8, power = 0.8 and false positive rate = 0.05, the results suggest that an investigator should use a two-tailed hypothesis and will need ~26 animals per group”. Taking into account that the present manuscript intents to provide guidance for investigators, it would be important to inform the readers that the development of preliminary (pilot) studies, with small experimental number, could represent an useful alternative in order to calculate the experimental number needed to achieve the desired power. Reviewer #2: General Comments to the Authors: This is a well thought-out and designed study that provides some clear guidelines for researchers and scientists on how to optimize behavioural studies (using the Forced Swim Test as an example). It highlights the importance of statistical considerations when designing a study and particularly deciding on the number of animals that should be included in the analysis to ensure reproducibility. I believe this to be a very important study in shifting some of the engrained beliefs around the adequate power to detect reliable effects. At this moment, I only have a few specific comments/suggestions for the authors to consider before the manuscript can be accepted for publication. Specific Comments to the Authors: 1. Introduction: It would be great if the Authors could briefly define these Terms the first time they are referred to in the manuscript: effect size, sample size, statistical power, and replicability. This will improve the paper's readability and will ensure that the reader understands the differences between effect size and sample size, for example. 2. Discussion, page 9, lines 210-216: According with this hypothesis that the FST may reflect a discontinuous YES/NO behavioural decision by mice, would it then be more appropriate to use non-parametric statistical tests when analyzing FST data? Can the Authors comment/discuss this in the Discussion of the Manuscript (by perhaps tying this in with the Discussion on “Parametric or Nonparametric Testing” on page 12)? 3. Discussion, page 9, lines 218-223: Other potential variables that may account for the variability in baseline include: strain of mice used and age of the animals at the time of the experiment (i.e., comparing baseline of young animals vs. old animals, for example). These additional variables should also be listed and discussed here. 4. Discussion: Can the Authors comment/provide practical suggestions on how the current body of literature (i.e., published studies using the FST to test the putative antidepressant effects of drugs in mice) should be interpreted in light of the findings reported in this study? ****** 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:** Marcelo Farina 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. https://doi.org/10.1371/journal.pone.0243668.r001
Revision 1
27 Jan 2021 Author Response Reviewer #1: With a particular emphasis on the relationship between sample size, statistical power and effect size, the authors explored a sample of published research articles in order to shed light into these topics, as well as to discuss reproducibility and the behavior of scientists in planning their experiments. The sample (16 published articles) was selected from a population of mouse-based experimental studies aimed to investigate potential antidepressant molecules in the Forced Swim Test (FST), which has been used to test putative antidepressants. In brief, the authors observed extremely large mean effect sizes in the analyzed sample (1.5 – 2.5 in Cohen’s d units). The average experimental number per group (7-10 animals), although relatively small, was considered enough to achieve adequate power to consistently detect effects of this magnitude (1.5 – 2.5 in Cohen’s d units). In order to detect large effects (0.8 in Cohen’s d units) when the true effect of a test agent is unknown, the authors propose that proper prospective design would require approximately 21-26 animals per group, which is a number significantly higher than the current average (7-10 animals). The authors discuss their data emphasizing that they provide parameters and guidance for investigators seeking to carry out prospective power estimation for the FST. They also state that the investigators are not able to perceive intuitively whether or not a given sample size is adequate for a given experiment, which seems to be related, at least partially, with the extremely large mean effect sizes detected (1.5 – 2.5 in Cohen’s d units). The manuscript, which is properly written and presented, brings out an interesting view on the current scenario of FST, search for antidepressants, sample size and statistical power. However, there are points that deserve major consideration in order to improve the manuscript: 1 - In some parts of the manuscript (lines 46 and 327), the authors argue that underpowered studies have a tendency to show a very high false-positive rate. It is important to present information on the (also high) false-negative rate in underpowered studies, particularly in studies with small (but significant) effects. Thank you for this comment. We have now mentioned the false-negative issue and added a reference, Fiedler K, Kutzner F, Krueger JI. The Long Way From α-Error Control to Validity Proper: Problems With a Short-Sighted False-Positive Debate. Perspect Psychol Sci. 2012 Nov;7(6):661-9. doi: 10.1177/1745691612462587. 2 - Does a 16-articles sample have enough power to properly represent the population of 737 articles? Based on statistical foundation, the authors need to discuss this topic in order to inform the reader about the reliability of their data, which derived from a relatively small sample (n = 16). Thank you for this comment. We did address this issue implicitly by presenting the 95% confidence interval for effect sizes – even extracting “only” 77 assays in 16 articles, the effect sizes were extremely large: “the mean FST effect sizes of both test agents and known clinically prescribed antidepressants regarded as positive controls had values in Cohen’s d units of -1.67 (95% Confidence Interval: -2.12 to -1.23) and -2.45 (95% CI: -3.34 to -1.55), respectively.” In the revised version, we add some language in the Discussion: “95% Confidence Intervals indicate that our sampling is adequate to support our conclusion, namely, that mean effect sizes are extremely large -- and not anywhere near to the Cohen’s d value of 0.8 which is generally thought of as a “large” effect size.” 3 - In the Materials and Methods Section, line 107, it is informed “When sample size was not provided directly, it was inferred from t-test or ANOVA parameters and divided equally among treatment and groups, rounding up to the nearest whole number if necessary. If only a range for sample size was provided, the average of the range was assigned to all treatments, and rounded up if needed”. This also represents a limitation of the study because there are a huge number of articles with different experimental number in their different experimental groups. This should, at least, be informed in the limitations of the study (line 314). Thanks, we have now added this as a limitation. 4 - Line 128: Was the effect size of known antidepressants normally distributed in the sample (16 articles)? Please, add more on that. In the revised version, we have now added a histogram and a test of normality for this distribution. 5 - It would be informative to show data concerning the dispersion of immobility-time within and between studies, at least for controls. The reports did not provide any data on the dispersion within studies, but in the revised version, we have now added a histogram on the immobility times across studies. 6 - Line 169: “Interestingly, the assays in depressed models showed a smaller coefficient of variation (i.e., standard deviation divided by the mean) than in naïve mice”. This seems to be true only for "test agents", but not for "antidepressants". Please, revise the sentence. In addition, discuss it. We have now revised the sentence and we now explain that: “The number of assays of known antidepressants in depressed mice (N = 3) was too small in our sample to compare coefficients of variation vs. naïve mice.” 7 - Table 4: With respect to “antidepressants/naïve”, the mean effect size of 2.554 is considered high. However, several “known antidepressants" have no clinical effects in a significant number of patients (refractory). Even though 1.729 (“test agents/naïve”) is also considered a high mean effect size, is it possible to predict a worse clinic effect (in patients) compared to “antidepressants/naïve” (effect size 2.554)? In my opinion, discussions concerning the biological relevance of the data found in the evaluated articles would improve the present manuscript relevance. The reviewer raises a set of profound questions about the relationship of animal assay responses to human depressive treatment response. Our data do not directly address these issues. However, in the revised ms., in the Limitations section, we have now added several references that point to this issue: “Finally, it must be acknowledged that none of the preclinical antidepressant assays carried out in animals fully reproduce all aspects of depression pathophysiology or treatment response in humans. Therefore, regardless of effect sizes or reproducibility of animal findings, one must make a conceptual leap when considering the clinical promise of any given antidepressant drug.” 8 - Line 220: “This might potentially be explained by high variability of baseline values across heterogeneous experiments and laboratories”. This is not shown. Please, see comment 5. Added a histogram as mentioned above. 9 - Line 276: “In summary, for testing an unknown agent (e.g., chosen without prior experimental evidence or as part of a high-throughput screen), with minimum effect size = 0.8, power = 0.8 and false positive rate = 0.05, the results suggest that an investigator should use a two-tailed hypothesis and will need ~26 animals per group”. Taking into account that the present manuscript intents to provide guidance for investigators, it would be important to inform the readers that the development of preliminary (pilot) studies, with small experimental number, could represent an useful alternative in order to calculate the experimental number needed to achieve the desired power. Done. Reviewer #2: General Comments to the Authors: This is a well thought-out and designed study that provides some clear guidelines for researchers and scientists on how to optimize behavioural studies (using the Forced Swim Test as an example). It highlights the importance of statistical considerations when designing a study and particularly deciding on the number of animals that should be included in the analysis to ensure reproducibility. I believe this to be a very important study in shifting some of the engrained beliefs around the adequate power to detect reliable effects. At this moment, I only have a few specific comments/suggestions for the authors to consider before the manuscript can be accepted for publication. Specific Comments to the Authors: 1. Introduction: It would be great if the Authors could briefly define these Terms the first time they are referred to in the manuscript: effect size, sample size, statistical power, and replicability. This will improve the paper's readability and will ensure that the reader understands the differences between effect size and sample size, for example. Thank you for your comment. We have now defined these terms in the Introduction. 2. Discussion, page 9, lines 210-216: According with this hypothesis that the FST may reflect a discontinuous YES/NO behavioural decision by mice, would it then be more appropriate to use non-parametric statistical tests when analyzing FST data? Can the Authors comment/discuss this in the Discussion of the Manuscript (by perhaps tying this in with the Discussion on “Parametric or Nonparametric Testing” on page 12)? We agree entirely! We now added the following sentence to the Discussion: “If indeed immobility responses have a switch-like aspect (see above), one might expect that responses might be bimodal or skewed.” 3. Discussion, page 9, lines 218-223: Other potential variables that may account for the variability in baseline include: strain of mice used and age of the animals at the time of the experiment (i.e., comparing baseline of young animals vs. old animals, for example). These additional variables should also be listed and discussed here. In the revised ms., we have now added age, strain and gender to the list here as well as in the Limitations to our analysis. 4. Discussion: Can the Authors comment/provide practical suggestions on how the current body of literature (i.e., published studies using the FST to test the putative antidepressant effects of drugs in mice) should be interpreted in light of the findings reported in this study? The reviewer astutely notices that we have refrained from critiquing/discussing the way that the FST is used as a behavioral endpoint for animal neuroscience studies, nor the relevance of the FST as a predictor of clinical promise in humans. We also do not walk readers through the entire process of how to design FST experiments (which would involve how to choose informative agents to test in the first place, the best strains of mice, possible use of flexible stopping points, etc.). These subjects are important but are much too large for the present paper to consider. Rather, we deliberately restricted practical suggestions to a narrow scope: how experimenters could/should improve their existing designs in terms of sample size, statistics, etc. These considerations should be of somewhat general interest insofar as they generalize to other preclinical assays. In the revised ms., we have now added discussion and references (as mentioned above) that acknowledge the leap between preclinical antidepressant assays in animals and clinical antidepressant treatment in humans. Attachments Attachment Submitted filename: response to the reviewers 0121.docx https://doi.org/10.1371/journal.pone.0243668.r002
10 Feb 2021 Decision Letter - Patricia Souza Brocardo, Editor Effect size, sample size and power of forced swim test assays in mice: Guidelines for investigators to optimize reproducibility PONE-D-20-36904R1 Dear Dr. Smalheiser, 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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, 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, Patricia Souza Brocardo, Ph.D. Academic Editor PLOS ONE 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: Based on reading of the revised version and authors' response, it is evident that the manuscript was properly revised and significantly improved. Reviewer #2: All my previous comments have now been addressed and I do not have any further suggestions at this point. I am happy to now endorse this version of the manuscript for publication. ****** 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 https://doi.org/10.1371/journal.pone.0243668.r003
Formally Accepted
15 Feb 2021 Acceptance Letter - Patricia Souza Brocardo, Editor PONE-D-20-36904R1 Effect size, sample size and power of forced swim test assays in mice: Guidelines for investigators to optimize reproducibility Dear Dr. Smalheiser: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. 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 plosone@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. Patricia Souza Brocardo Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0243668.r004

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