PONE-D-20-03266

Long term freedom from invasive breast cancer diagnosis in 2,402,672 peri and postmenopausal women: a systematic review and meta-analysis

PLOS ONE

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Reviewers' comments:

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Comments to the Author

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

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Reviewer #1: Yes

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

Reviewer #1: Yes

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Reviewer #1: Yes

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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: This manuscript that provides an alternative to breast cancer risk using cohort studies across the globe makes an intriguing assertion. Specifically, the calculation used to assess breast cancer risk, and therefore recommendations for mammogram frequency though cancer registry data is flawed for menopausal women. Overall, this is a great paper but needs some edits/analyses to further strengthen this. It does provide a strong and important message.

Some specific comments are below

Line 81. I don’t understand how many of the participants enrolled at younger ages (age 39, 35, 40, etc) are considered perimenopause without there being clear indication in the manuscripts that women are experiencing hormonal changes. Given the average age is early 50s and symptoms associated with hormonal changes averages 6-8 years, many of the younger women are likely not in the perimenopausal phase. Unless this was determined, then the inclusion is just women with no IBC hx at enrollment would be more accurate. Recommend not using the perimenopausal to partially describe this population.

Line 91. Beginning with the statement “unfortunately…” This is unclear what the authors are saying. Please clarify.

The most valid statement is the point that cancer registry calculator using new incidence case not new person with IBC as numerator; I was not aware that was how the numerator was calculate. However if this was changed to be first IBC what would the risk be? To me adding this piece of information would significantly strengthen the paper.

Table 1 is a nice outline of the differences. My understanding was the cancer registry used the number of cases, not an estimate of number of cases. Do you mean the current year’s estimate? They always give an estimate for the current year’s since the case ascertainment has a long lag tail for inclusion. Point 4 is unclear to me. If the registry uses incident IBC, why is followup needed. In xx year, this is the number of women with IBC as numerator and denominator in as estimate of number of women who have to possibility of being dx with IBC.

Table 2 is a nice outline. However, I would delete the repetitive statement in bold, ‘1st incidence of IBC’ for each study. There was some inconsistencies in description; some used menopausal status, some used study type (screened or not screened), some used HRT, some used menopausal symptom medication. The purpose of this table is unclear since similar items were not included for each study---it lacks consistency. For example, many start with bolded study type (Cohort or RCT, but then others drift such as 11, record review (isn’t that a retrospective cohort study) or #15 was ?? or #20 and #21 did not specify study design. Some use menopausal status and other used age (with some clearly menopausal). No indication of any of these interventions that could protect against IBC. Please address/add info for consistency.

For the RCTs was only controls used in the analyses? It seems not, and therefore it makes me wonder if the intervention affected IBC. Any way to address this concern?

The other concern about these cohort studies in which screening was a key aspect AND the outcome for this study was IBC is that we all know screening can pick up in situ BC so this seems to influence the numerator. How was this bias handled?

Table 2 provides important information. Can you do the age adjusted rate here instead of just crude percent since age is one of the most important variables in cancer dx.

Fig 1, just include 90% to 100% on the y-axis so this can be read for easily. The title of y says percent of women without IBC dx and it is at 100% . It doesn’t make sense that all women or 50% or 70% of women would have IBC at baseline. It looks like it starts at baseline with 0% free IBC and goes to around 7-8% with IBC at 25 years. The description is better than the image and the image can be improved upon.

From what I could understand from Table 4, everything is significant at <0.05. That is not surprising given the sample size. Given this sample size, it seems reasonable to only consider significance at the <0.001. Overall this table is unreadable. Please use footnotes to help describe all our models. Please add number of participants in each model since it seems this varied. Years means ? PM=1 means; PM=0 means? Overall this is a very unclear table.

I’m struggling with the concept of free (probably not use freedom) from IBC dx, that does not take into consideration age of participant. Another piece of the data that makes me worried is the over-representativeness of the UK Million Women Study. Almost ½ of the participants come from this group and the fu was the shortest at 2.6 years. If the analyses were run without this study, would similar results be reported? I read further that you did an analysis where age and sample size was addressed (lines 217-235). I don’t fully understand the test. How was the independent variables constructed? Number of women enrolled entered into the model as ? Put years studied (means followup period?) and sample size … and found similar result, ‘holding number of women constant’. I just don’t follow the entire paragraph. As far as I can tell this does not address age + followup period as important in the analysis.

Probably would not use the word ‘bad health habits’ since this is a judgmental word. Line 250

The strongest argument for this paper is that the conventional use of a numerator for determining bc risk may over-inflate the risk since recurrence is well known. Without knowing what the rate would be by changing the numerator using cancer registry data is a significant limitation in the argument made in this paper. I also believe the ACS considers both IBC and in situ. About 12-15% of bc are insitu with strong mammogram uptake and this should be noted as part of their calculation. This also reflects a limitation in this analysis, women who were dx with in situ through use of mammograms, never developed IBC and were not ‘counted’ in the statistics. It seems a bit like comparing apples and oranges.

Please provide some reference to support the statement on line 268 that “census estimates are skewed towards undercounting.” Using one example in which an error was made and acknowledged as an error is not sufficient to make this global statement. More documentation of this is needed.

The limitation section needs to include a few of the comments made in this review. Many of the known risk factors may or may not have been included in the studies, such as family history of bc. Adding information about the bc risk factors (prevalences) by study would be a great addition and likely readily available. This could strengthen the analysis since it represent the population or does not. I would suspect it doesn’t represent lower educational attainment, or minorities (for US). If the study descriptions are not truly representative this should be included in the limitations and if it does, then that is a strength that should be noted.

Overall, I think this is a great analysis and just needs to be tighten up to provide a very strong message.

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Reviewer #1: No

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PONE-D-20-03266R1

Long term freedom from invasive breast cancer diagnosis in 2,402,672 peri and postmenopausal women: a systematic review and meta-analysis

PLOS ONE

Dear Dr. Cutler,

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 August 10, 2020. 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:

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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.

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We look forward to receiving your revised manuscript.

Kind regards,

Magdalena Grce, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

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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: (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

**********

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

Reviewer #1: 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

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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

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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 the extensive responses.

I appreciate the response to the issue of using peri-menopausal. I am puzzled by the insistence on using this term, including in the title when it does not capture the characteristics of the women and muddles the picture. As mentioned by the reviewers “For the 3 studies that enrolled women who were at least 40:#15,18, and 19 we also looked closely at each before qualifying them as studying women in their peri/postmenopausal era, since the term “perimenopausal” is a loose one – but does convey our intent.” No idea what the authors mean by ‘the intent’. I don’t get it since these 3 studies start at women who are 40 years old. We can argue about this, but ultimately you can choose to use this ‘loose term’ extensively and in the title but be aware that if I read this, I would question your work. My thinking would be, ‘if they ‘misclassify’ women by assigning them a phase of menopausal status that they are not definitely experiencing, then what else were the authors less precise about. At minimum this should be defined in the first paragraph of the paper—perimenopausal is defined in this paper as any women aged 40 and older who has not been classified as menopausal.

In response to the request about calculating rate using only first case, the rationale to not do this is not compelling. Of course at any hospital level, your statement (“And we found that accurate information about prior cases from different hospitals the patient had used was not reported or often even available.”) is not a surprising finding. We are all aware of the fragmentation of health care in the US. Not sure about the other counties.

“Therefore, to your point “if this was changed to be first IBC what would the risk be?” we really cannot

estimate the risk using the retrospective method that the CDC uses because the raw data are not collected,

and they are unavailable.” This is a puzzling statement. What do you mean ‘raw data are not collected.’ It is a federal mandate to report cancer for any patient and in requesting data from the registry, first dx is a field that must be specified. I don’t understand your rationale for gold standard; using a registry that captures all cases is a much better method to estimate risk, if the numerator excludes those who have a recurrence. I thought this was one of your major points. If not, then I am truly confused. Prospective studies, unless very large and representative of the population AND have incredibly high retention rates (not happening in this era of research) are not the gold standard to determine risk. This paragraph puts a spanner in the paper, to me. Why would you not use cancer registry data but have it set up, in the future, to do a ‘better job’ of calculating risk? Or do you think it is impossible to use cancer registry data for estimating risk? What am I missing since this paragraph suggests that we should basically throw out cancer registry data for estimating risk. Is this really your conclusion?

In Table 1, #1: add the denominator is xxx. And for retrospective cancer registry (“estimated number of eligible women in the census for the ….” The key point here is the number of eligible women are estimated. Add ‘eligible’ women to prospective. To me that is the different in #1. One is exact number of eligible women and the other is an estimate of eligible for the denominator.

#2 implies a different way in which women’s bc dx was determined but some/many/most of the studies use the same system. I didn’t read each study, but I would be very surprised if the very large prospective studies did not use cancer registries as linkage to determine dx. Is this incorrect? I just randomly picked one study (Finnish) and they used the registry for followup bc dx.

I want to reiterate that this is a GREAT study and work. I just want to support the representation of the data described to be precise and without question so that it gets the attention it needs vs distracting comments that take away from the message. The only difference between the 2 systems is one uses a list and matches it to the registry and the other just uses the registry list.

#3 This is the key point of the study, to me.

#4 I’m not sure what you mean here. The registries enter date of dx. Longitudinal studies enter date of dx. Longitudinal studies somewhat arbitrarily enroll women on a certain date. Why is that meaningful for this analysis. By default, cancer registries ‘follow’ all women from year of eligibility to date of dx since it is a registry. Cumulative incidence is the number of new events or cases of disease divided by the total number of individuals in the population at risk for a specific time interval. This again gets back to the basic question as to whether the cancer registries can differentiate between first and subsequent cancer dx. I just don’t get this one as a difference between the 2 systems in a meaningful way.

#5 is kind of redundant. The ‘known follow-up period’ isn’t useful info for calculating incidence rate. This is using #1 and #3

Table 4. What is observation? Is this cancer dx? Besides this question the table is so much easier to understand. Thanks

I disagree with the final sentence since the finding of this great analysis was not about the patient’s anxiety and optimism but rather the risk of breast cancer diagnosis by the general population. One could easily argue that this finding can have the opposite effect on the general population of reducing compliance with breast cancer screening since it looks like the risk is much lower than publicly portrayed, from these analyses. If these findings are accepted as norm, we could reasonably go back to the 1980s rates of screening and subsequent more late stage cancer dx. I would not call that scenario a significant societal benefit. I’m not convinced that low risk women (such as no family hx) are anxious about being diagnosed with breast cancer. To me this last sentence distracts from the findings by injecting a personal perspective that could easily be countered.

I also note that this nuance, non-neutral word ‘freedom’ is also used in table footnote. I already indicated my perspective that using this word choice may do a disservice to this work due to inserting non-neutral language.

I would appreciate a definition with maybe an example to understand chronological age vs participant’s age, in the paper.

Appreciate seeing the formulas for the analyses since that was much easier to follow than the description as well as a more information in the limitation section.

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Reviewer #1: No

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Long term absence of invasive breast cancer diagnosis in 2,402,672 pre and postmenopausal women: a systematic review and meta-analysis

PONE-D-20-03266R2

Dear Dr. Cutler,

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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Magdalena Grce, PhD

Academic Editor

PLOS ONE

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