statistics

Welcome the 11th Edition of the AMA Manual of Style!

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We are pleased to announce the 11th edition of the AMA Manual of Style, now live at https://www.amamanualofstyle.com/ and shipping in hardcover in a few days.

The manual has been thoroughly updated, including comprehensive guidance on reference citations (including how to cite journal articles, books, reports, websites, databases, social media, and more), an expanded chapter on data display (for the first time in full color), a completely up-to-date chapter on ethical and legal issues (covering everything from authorship and open access to corrections and intellectual property), and updated guidance on usage (from patient-first language and terms to avoid to preferred spelling and standards for sociodemographic descriptors).

The section on nomenclature has undergone thorough review and updating, covering many topics from genetics and organisms to drugs and radiology.

The statistics and study design chapter has been extensively expanded, with more examples of usage and terms that link to a related glossary.

Chapters on grammar, punctuation, abbreviations, capitalization, manuscript preparation, and editing feature refreshed examples and new entries (such as allowance of the “singular they”).

The nearly 1200-page book is enriched by a variety of online features. For example, regular updates to address changes in style or policies will be featured in the Updates section. Any corrections will be made online so that you are always looking at the latest guidelines as you use the manual.

New quizzes will be posted to help new or continuing users learn to master the finer points of AMA style, and the units of measure calculator offers easy conversions between the SI system and conventional units, as well as the metric system.

We welcome questions and comments on the manual: write to stylemanual@jamanetwork.org or find us on Twitter (@AMAManual). We look forward to engaging with you. –Stacy Christiansen, for the AMA Manual of Style Committee

Breaking It Down

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When you pick up a book you haven’t yet read, do you immediately turn to page 1 and begin reading? Or, do you check out the front cover design to see if the book looks interesting? What about the back cover?

Most stories are broken down in many ways to hook the audience. It’s fairly standard that a book has a promotional blurb on the front cover, a tagline, a 1-sentence summary on the back, a slightly longer summary on the back, and, yes, more promotional blurbs on the back and inside the front cover. Perhaps you learned of the book through a social media post or through a review. What grabs your attention may not grab someone else’s, so breaking a story down in various ways makes it appeal to a larger audience.

The same can be done with scientific research articles. The main text typically follows the IMRAD format (introduction, methods, results, and discussion) to clearly and fully tell the story. The authors detail why they performed the research, how they did it and among whom, what they found, and what it means.

That story is condensed into an abstract, a brief summary that allows readers to determine whether they may find the full article interesting or useful. Should an abstract be too long or technical to pull a reader in, an article can have an even briefer key points section. This could be a bulleted list of important findings or, as in the JAMA Network journals, a list of the question, findings, and meaning of the research.

For social media, perhaps a single summary sentence is needed to fit the constraints of a character limit. For readers who prefer a more visual summary, especially through social media, a visual abstract can be useful. These are eye-catching depictions of the research, often using icons and very brief wording.

Twitter, JAMA (JAMA_current), January 17, 2020.

Medical editors may be tasked with reviewing, editing, or even writing some of these pieces. In doing so, a few tips might be helpful.

First, make sure the shorter piece is consistent with the main article. Numbers should match and any data in the shorter piece should be included in the main article.

Second, make sure the trimmed text doesn’t overstate the study’s results. For example, “This study suggests that X is associated with Y” is different from “X affects Y.”

Third, make sure the most important information is emphasized: “This [study type] examines [primary outcome] in [population].”

Fourth, remember the audience. The short items should be able to draw someone in to read the more technical information in the main article.

A journal article may be the culmination of an investigator’s life’s work or the end of a trial that has cost millions of dollars, which may make an article’s 1-sentence blurb seem measly. However, a patient may search social media to find information on a rare disease, and a post could bring the patient to the full article. A physician may scan key points to see if an article looks interesting enough to read fully. A student could review abstracts to find articles that are helpful for a research project. An investigator might use the full article to replicate the study or as a springboard for further research.

Each breakdown serves a purpose, promoting the right information to the right audience.–Shannon Sparenga

ME Without the MD

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One of the occupational hazards of being a medical editor is the inevitability of occasionally working on a highly technical, highly detailed manuscript on a topic about which you know nothing. You don’t need to have a medical degree to be a medical editor, but how do you edit a paper when you’re not sure whether the item under discussion should treated as a plural, or even as a noun?

Of course, an excellent place to start is the AMA Manual of Style, which can provide a general overview sufficient to navigate the complexities of many topics.

For example, you don’t need to know much about respiratory physiology as long as you remember to check section 15.16 (Pulmonary, Respiratory, and Blood Gas Terminology). You’ll be sprinkling cryptoglyphs like V̇, Pb, and v̅ in no time!

You also don’t need to know how to conduct an F test to know that when you see an F score, it should also include the numerator and denominator of the degrees of freedom in subscript (section 20.9, Glossary of Statistical Terms).

In addition to the searchable AMA Manual of Style, the modern age has also bestowed the gift of internet search engines to help decrypt topics of which you may have no knowledge (and had never previously needed knowledge of, for that matter). If you find yourself looking at a topic you’ve never heard of and the Manual doesn’t cover it, a few minutes perusing sites such as Google, PubMed, and Wikipedia can give you tentative grounds on which to make your stand.

But sometimes even all the treasures of the internet and your trusty AMA Manual of Style combined can’t help and you’re adrift on a sea of statistics, biochemistry, gene expression, or whatever your topical Achilles heel may be.

In such cases, it can be useful to think of a sentence like a math equation—you don’t need to know what the subject means, but if you can look at a sentence and know where the subject is, where the object is, and what kind of verb tense you need, you’re more than halfway there.

And, as always, you can and should rely on the author to clarify and correct as needed. The ultimate goal of your work is to improve the author’s work, and that can only be truly accomplished through teamwork.–Rebecca Palmer

Forest Plots: The Basics

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When I was recently asked to give a presentation on forest plots at work, I was less than enthused. Figures are my least favorite part of a manuscript to edit because they usually require a lot of tedious work, and determining how to best visually present statistics makes my brain hurt. Forest plots in particular had become the subject of my nightmares leading up to the time of preparation of my presentation after a few experiences with editing unwieldy ones. However, thanks to being subjected to presenting on forest plots, I’ve gained some basic knowledge that I thought I would share.

There are a few types of forest plots, namely those presenting the results of meta-analyses and those presenting subgroup analyses. Here, I will focus on a forest plot for a meta-analysis. In a meta-analysis, a forest plot acts as a visual representation of the results of the individual studies and the overall result of the analysis. It also shows overall effect estimates and study heterogeneity (ie, variation in results in the individual studies). A forest plot for ratio data should include the following data:

  1. The sources included in the meta-analysis, with citations. If the source author or study name is listed more than once, query the author to ensure that the study samples are unique; overlapping samples would lead to inaccurate estimates. Also, remember to renumber the references if you have renumbered them in the body of the article.
  2. The number of events and total number of participants in each group of the study and in the combined studies.
  3. Risk ratio and 95% CI for each study and overall.
  4. Graphed relative risk and 95% CI, with top labels describing what data markers on either side of the null line mean. The squares represent the results of each study and are centered on the point estimate, with the horizontal line in the center representing the 95% CI. The diamond shows the overall meta-analysis estimate, with the center representing the pooled estimate and the horizontal tips indicating the confidence limits.
  5. Log scale for the x axis with a label indicating the measure.
  6. Percentage of weight given to the study. Weights are given when pooled results are presented. Studies with narrower confidence intervals are weighted more heavily.
  7. Heterogeneity and data on overall effect.

(Open image in a new tab to see more detail.)

The caption should indicate the test and model (fixed or random effects) used in the evaluation and may include an explanation of the meaning of the different marker sizes.

If you follow these basic rules, forest plots are a breeze. —Sara M. Billings

 

 

 

Putting P Values in Their Place

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Although I am not a statistician, I find something very appealing about mathematics and statistics and am pleased when I find a source to help me understand some of the concepts involved. One of these sources intersects with my obsession with politics: Nate Silver’s website fivethirtyeight.com. Yesterday, during a scan of fivethirtyeight’s recent posts, this one by Christie Ashwanden caught my eye: “Statisticians Found One Thing They Can Agree On: It’s Time to Stop Misusing P-Values.”

P values and data in general are frequently on the minds of manuscript editors at the JAMA Network. Instead of just making sure that statistical significance is defined and P values provided, we always ask for odds ratios or 95% confidence intervals to go with them. P values are just not enough anymore, and Ashwanden’s article was really useful in helping me understand why these additional data are needed (as well as making me feel better about not fully understanding the definition of a P value—it turns out I’m not alone. According to another fivethirtyeight article, “Not Even Scientists Can Easily Explain P-Values”). One of the bad things about relying on P values alone is that they are used as a “litmus test” for publication. Findings with low P values but not contextual data are published, yet important studies with high P values are not—and this has real scientific and medical consequences. These articles explain why P values only can  be a cause for concern.

And then there was even more information about statistical significance to think about. A colleague shared a link to a story on vox.com by Julia Belluz: “An Unhealthy Obsession With P-Values Is Ruining Science.” This article a discussed a recent report in JAMA  by Chavalarias et al “that should make any nerd think twice about p-values.” The recent “epidemic” of statistical significance means that “as p-values have become more popular, they’ve also become more meaningless.” Belluz also provides a useful example of what a P value will and will not tell researchers in, say, a drug study, and wraps up with highlights of the American Statistical Association’s guide to using P values.—Karen Boyd

Ch-ch-ch-changes

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To pass the time between stylebook editions, the JAMA Network staff keep an in-house file of little tips, tricks, guidelines, and style changes that have occurred since the last time the manual was published. Here is a small peek inside that file—2 things from this past summer.

The terms multivariable and multivariate are not synonymous, as the entries in the Glossary of Statistical Terms suggest (Chapter 20.9, page 881 in the print). To be accurate, multivariable refers to multiple predictors (independent variables) for a single outcome (dependent variable). Multivariate refers to 1 or more independent variables for multiple outcomes. (This update was implemented June 1, 2014.)

Cross-section, as a verb or adjective should be capped in titles as Cross-section; cross section as a noun should be capped in titles as Cross Section. (This update was implemented August 4, 2014).—Brenda Gregoline, with help from John McFadden

Bucking the “Trend” and Approaching “Approaching Significance”

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I believe we are on an irreversible trend toward more freedom and democracy – but that could change.

—Dan Quayle

In general usage, the concept of trend implies movement. Not only is this implied in its definitions, but the word can be traced to its Middle High German root of trendel, which is a disk or spinning top.1

In scientific writing, when is a trend not a trend? When it is not referring to comparisons of findings across an ordered series of categories or across periods of time. However, this and related terms are often misused in manuscripts and articles.

Most studies are constructed as hypothesis testing. Because an individual study only provides a point estimate of the truth, the researchers must determine before conducting the study an acceptable cutoff for the probability that a finding of an association is due to chance (the α value, most commonly but not universally set at .05 in clinical studies). This creates a dichotomous situation in interpreting the result: the study either does or does not meet this criterion. If the criterion is met, the finding is described as “statistically significant”; if it is not met, the finding is described as “not statistically significant.”

There are many limitations to this approach. Where the α level is set is arbitrary; therefore, in general all findings should be expressed as the study’s point estimate and confidence interval, rather than just the study estimate and the P value. Despite the limitations, if a researcher designs a study on the basis of hypothesis testing, it is not appropriate to change the rules after the results are available, and the results should be interpreted accordingly. The entire study design (such as calculation of the sample size and study power – the ability of a study to detect an actual difference or effect, if one truly exists) is dependent on setting the rules in advance and adhering to them.

If a study does not meet the significance criterion (for example, if the α level was set as < .05, and the P value for the finding was .08), authors sometimes describe the findings as “trending toward significance,” “having a trend toward significance,” “approaching significance,” “borderline significant,” or “nearly significant.” None of these terms is correct. Results do not trend toward significant—they either are or are not statistically significant based on the prespecified study assumptions. Similarly, the results do not include any movement and so cannot “approach” significance; and because of the dichotomous definition, “nearly significant” is no more meaningful than “nearly pregnant.”

When a finding does not meet statistical significance, there are generally 2 possible explanations: (1) There is no real association. (2) There might be an association, but the study was underpowered to detect it, usually because there were not enough participants or outcome events. A finding that does not meet statistical significance may still be clinically important and warrant further consideration.

However, when authors use terms such as trend or approaching significance, they are hedging the interpretation. In effect, they are treating the findings as if the association were statistically significant, or as if it might have been if the study had just gone a little differently. This is not justified. (Lang and Secic2 make the fascinating observation that “Curiously, P values never seem to ‘trend’ away from significance.”)

A proper use of the term trend refers to the results of one of the specific statistical tests for trend, the purpose of which is to estimate the likelihood that differences across 3 or more groups move (increase or decrease) in a meaningful direction more than would be expected by chance. For example, if a population of persons is ranked by evenly divided quintiles based on serum cholesterol level (from lowest to highest), and the risk of subsequent myocardial infarction is measured in each group, the researcher may want to determine whether risk increases in a linear way across the groups. Statistical tests that might be used for analyzing trends include the χ2 test for trend and the Cochran-Armitage test.

Similarly, a researcher may want to test for a directional movement in the values of data over time, such as a month-to-month decrease in prescriptions of a medication following publication of an article describing major adverse effects. A number of analytic approaches can be used for this, including time series and other regression models.

Instead of using these terms, the options are:

1. Delete the reported finding if it is not clinically important or a primary outcome. OR

2. Report the finding with its P value. Describe the result as “not statistically significant,” or “a statistically nonsignificant reduction/increase,” and provide the confidence interval so that the reader can judge whether insufficient power is a likely reason for the lack of statistical significance.

If the finding is considered clinically important, authors should discuss why they believe the results did not achieve statistical significance and provide support for this argument (for example, explaining how the study was underpowered). However, this type of discussion is an interpretation of the finding and should take place in the “Discussion” (or “Comment”) section, not in the “Results” section.

Bottom line:

1. The term trend should only be used when reporting the results of statistical tests for trend.

2. Other uses of trend or approaching significance should be removed and replaced with a simple statement of the findings and the phrase not statistically significant (or the equivalent). Confidence intervals, along with point estimates, should be provided whenever possible.—Robert M. Golub, MD

1. Mish FC, ed in chief. Merriam-Webster’s Collegiate Dictionary. 11th ed. Springfield, MA: Merriam-Webster Inc; 2003.

2. Lang TA, Secic M. How to Report Statistics in Medicine: Annotated Guidelines for Authors, Editors, and Publishers. 2nd ed. Philadelphia, PA: American College of Physicans; 2006:56, 58.

 

Incidence

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In medical contexts, incidence is most often used in its epidemiologic sense, ie, the number of new cases of a disease occurring over a defined period among persons at risk for that disease. When thus used, incidence may be expressed as a percentage (new cases divided by number of persons at risk during the period) or as a rate (number of new cases divided by number of person-years at risk).

Reporting several incidence values in the same sentence can nearly always be accomplished using the singular form (eg, “the incidence of nonfatal myocardial infarction during follow-up was 10% at 6 months, 19% at 12 months, and 26% at 18 months” or “the incidence of clinical stroke decreased significantly, from 7.6 to 5.3 per 1000 person-years in men and from 6.2 to 5.1 per 1000 person-years in women). However, in rare instances, sentence construction may necessitate the use of the plural, which of course is… what, exactly? The understandable urge to simply add an “s” at the end of the word to form the plural results in incidences — a form not found in most dictionaries and a clunker of a word if ever there was one. Writers wishing for a more mellifluous plural sometimes use incidence rates, a valid term but one perhaps best reserved for reporting incidence values expressed as actual rates rather than simple percentages. Moreover, incidences is sometimes used when reporting values either as percentages or as rates, in the latter case missing a valuable opportunity to emphasize that rates rather than percentages are being reported.

Thus, it is perhaps best to use incidences, awkward as it may be, when reporting multiple incidence values as percentages and incidence rates when reporting such values as rates, eg, “at first follow-up, the incidences of falls resulting from frailty, neuromuscular disorders, or improper use of mobility devices were 15% (95% CI, 10%-20%), 12% (95% CI, 7%-17%), and 12% (5%-19%), respectively” or “the incidence rates for falls resulting from frailty, neuromuscular disorders, or improper use of mobility devices were 5.1, 6.3, and 4.6 per person-year, respectively.” Incidentally, these 2 examples report occurrences (falls) rather than diseases or conditions, and so represent 2 instances reporting the incidence of incidents.

To further muddy the waters, incidence is sometimes confused with prevalence, defined as the proportion of persons with a disease at any given time (ie, total number of cases divided by total population). Thus, whereas incidence describes how commonly cases are diagnosed, prevalence describes how widespread the disease already is; on a more personal level, incidence describes one’s risk of developing the disease, whereas prevalence describes the likelihood that one already has it. The confusion between the terms is perhaps attributable to the occasional use of prevalence in place of incidence in the study of rare, chronic diseases for which few newly diagnosed cases are available; however, this circumstance is unusual, and incidence and prevalence should always be distinguished from one another and used appropriately. (See also §20.9, Glossary of Statistical Terms, in the AMA Manual of Style, p 872 in print.)

Whereas prevalence is often used in general contexts to indicate predominance or general acceptance, the circumstances calling for the use of incidence in general contexts are quite few and become fewer still when one takes into account that incidence is often used when incidents (the simple plural of incident) or instance (again denoting an occurrence) would be the better choice. Perhaps incidents or instances was intended but never made it to the page — as is so often the case with homophones and near-homophones, even the careful writer who usually would not confuse incidence, incidents, and instance might one day look back over a hastily typed passage only to see that a wayward incidence has crept in; if the passage is hastily edited to boot, the error might well go unnoticed until the passage is in print and a discerning reader takes pains to point it out in a letter or e-mail. The plural form, incidences, has virtually no use outside of the epidemiologic discussed above, although it has been used to subtly disorienting effect by translators rendering the Kafkaesque works of Russian writer Daniil Kharms (1904-1942) into English, most notably when rendering the 1-word title of Incidences, Kharms’ 1934 collection of absurdist critiques on life in the Soviet Union under Stalin. However, writers who are not political dissidents aiming for absurdist effect — presumably all medical writers — would do well to proofread carefully and often. — Phil Sefton, ELS

Statistical Rounding and the (Mis)Leading Zero

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Sometimes editors (not you or I, of course) obey the rules of their institution’s preferred style manual without fully understanding, or really thinking about, why some of these rules exist. For example, some editors (not you or I, of course) automatically delete (or, if they’re lucky, their editing program deletes for them) the leading zero in a few statistics, but not all. They know exactly when and where to delete the leading zero, but not why. Or they round some statistics, but not all, assuming that all of this has something to do with saving space. It does, of course,1(p830) but this isn’t the only reason we do it.

The AMA Manual of Style defines a P value as “The probability of obtaining the observed data (or data that are more extreme) if the null hypothesis were exactly true.”1(p888) Per AMA style, P values greater than .01 are expressed to a maximum of 2 decimal places and those less than .01 are expressed to a maximum of 3 decimal places. I set out in search of the complicated statistical reason why we use this specific number of decimal places and found that, in addition to saving space, we do it for one simple reason: it’s all we need. Yep, that’s it. It’s all we need to know. P < .00000001 doesn’t tell us any more of value than P < .001. Both tell us that the probability is very low, and that’s good enough. Of course, if the author protests or rounding will make P appear nonsignificant, an exception is made (for example, if P = .046 and significance is set at P = .05).1(pp851-852) Also, studies such as genome-wide association studies report P values of P < .00001 or smaller, often in scientific notation, to address the issue of multiple comparisons; it is essential not to round these. So every rule has exceptions, I guess (remember Spanish class, anyone?).

Why then, you ask, do we not save ourselves the confusion and simply round P < .001 to P = .00? There’s a reason for that, too, and it’s the same reason we don’t use leading zeros with certain probability statistics (ah, you say, it all comes together). If probability is the chance that a given event will occur,2 and we have only surveyed a sample of a given population, probability cannot equal 1.0 or 0 because we can’t say absolutely that a null hypothesis will definitely or definitely not happen in that population.1(p889) And if P can’t equal 1.0 or 0, why include a zero that doesn’t tell us anything new? For this reason, we use P > .99 and P < .001 as the highest and lowest P values. For the same reason, and because they are used often, the leading zero rule applies to α and β probabilities as well. Why? To save space, of course.–Roya Khatiblou, MA

1. Iverson C, Christiansen S, Flanagin A, et al. AMA Manual of Style: A Guide for Authors and Editors. 10th ed. New York, NY: Oxford University Press; 2007.

2. Merriam-Webster’s Collegiate Dictionary. 10th ed. Springfield, MA: Merriam-Webster Inc; 1997.