The Editors' Notepad
The goal of this blog is to help EPIDEMIOLOGY authors produce papers that clearly and effectively communicate their science.

The views and recommendations of contributors do not necessarily indicate official endorsements or opinions of the Journal, WK, or the ISEE. All views are those of the authors and the authors alone.​​


Tuesday, May 2, 2017

Because we’ve written recently about figures, it seemed a good time to address the other aspect of data presentation: tables. If you’ve been reading this blog regularly, it won’t surprise you to know that our first priority for tables in Epidemiology is clarity. And if you’ve read from the beginning, you may remember that I once confessed to my rusty, out-of-date knowledge of methodology. 


Well, I have another confession: I find it hard to look at big, cluttered tables of numbers. When I was analyzing data for my dissertation, I would have to extract data from SAS crosstab output that looked like thiswere cluttered and complicated, and I would get really anxious. I wasn’t sure which numbers I wanted, I often grabbed the wrong one, and sometimes I would switch digits. I often asked a colleague to check my work. To this day, I find it challenging to check table data versus text when the tables are not laid out clearly.


Reasonable epidemiologists can differ on their general preference for presenting data in figures versus tables. At Epidemiology, though, if a figure and a table convey the data equally effectively, we prefer you use a table to gain the advantage of the exact data. However, for the reason I mentioned just above, the visual characteristics of a table matter, and journal style and statistical requirements play into table presentation as well, so here are our recommendations:


Table size


In general, aim to have a table fit on no more than one whole vertical page; a page in Epidemiology is about 275mm x 200 mm, minus margins. Tables can spill over onto another page, or be printed at 90 degrees to the main text, but such scenarios (for example the short but wide table) often end up wasting space on the page. That said, a full-page table is pretty big, and we always appreciate your willingness to manage the size and number of tables by putting data in supplementary digital content. In a recently published meta-analysis, half of the vertical space in a forest plot (more of a hybrid table-figure) consisted of lettered footnotes indicating adjustment variables. The author was willing to put these footnotes in SDC. 


A main concern when we edit tables is that the authors have been too comprehensive. For example, we often receive tables that show the same estimate of association from several different models (e.g, crude, adjusted for age and sex, and adjusted for a more comprehensive set of control variables). We prefer that authors decide which model is most valid and precise, and present only that result. This brevity may allow a table to be deleted altogether, with results presented only in the text. The detailed information can always be presented in the SDC for readers with interested in a more comprehensive view. The main point is that authors should evaluate the information in their tables to assure that every included result is important for the reader to comprehend the study’s results. Information extraneous to that goal should be deleted or included as SDC.


Abbreviations and footnotes


Because of the limited size of column and row headings, we can often be more flexible about allowing abbreviations that in the main text we would ask you to expand, as long as they are defined in a non-lettered footnote.


Other than the one footnote that defines abbreviations, footnotes should follow our convention of superscript letters - a,b,c - rather than numbers (which may be confused with bibliographic citations) or symbols.


Precision and interpretability


We prefer two significant figures for most numbers, except when substantial statistical power allows more; we will flag significant figures exceeding two, but we do leave the final decision to the author. When we say two significant figures, we mean independent of the decimal, so:


XX, X.X, 0.XX


In other words, significant figures is not equivalent to decimal places. This holds for percentages, descriptive statistics, and risk estimates, including those on a log scale.


A special note for descriptive tables, typically Table 1. We are aiming for tables that don’t mix categorical and continuous data. The most useful statistics for describing the distribution of a variable is quantiles, usually quartiles that include a median. Mixing categorical and continuous data can also make a table confusing to read. For these reasons, we prefer authors report any means and standard deviations (SD) in the text. We understand that, when presenting descriptive statistics within subgroups, it may be unwieldy to move means and SDs to text; a possible solution is to group continuous variables separately from categorical ones, so that the respective statistics can be clearly labeled.



Friday, March 10, 2017

It's safe to say that the 45th President of the United States is not a fan of science. The views he has expressed on climate change have ranged from skepticism to dismissal as a hoax perpetrated by China, and the White House reportedly demanded that the EPA remove all its web pages referring to climate change, though this order was quickly rescinded. The President believes vaccines cause autism and has proposed a commission on vaccine safety led by anti-vaccine activists. He has said that the U.S. National Institutes of Health is "terrible." His newly appointed director of Health and Human Services, which comprises several science agencies among others, had a thin track record on science funding during his tenure in Congress. This journal recently published a Commentary on behalf of the International Society of Environmental Epidemiology, this journal's sponsoring society, advocating against the President's nominee for administrator of the Environmental Protection Agency. The nominee, who was confirmed, seems to hold the view that climate and environmental health science are at best inconvenient. Looking further to the Congress, a current legislative initiative aims to shut down EPA entirely by the end of 2018.

None of this will be news to Epidemiology's readership, but governmental research, government sponsored research, and the voices of all researchers are at risk of being suppressed. Government scientists at a range of agencies have already been warned not to speak directly with legislators or the press. Although requirements for agency clearance around politically sensitive topics are not unprecedented, communications from the White House and agencies have been somewhat messy and contradictory, leading to uncertainty. The landscape changes daily, perhaps intentionally cultivating chaos. In reaction, scientists, programmers, and others are scrambling to download pages and data, especially climate change data, from government websites and servers, lest they be destroyed.

In this political climate, government and government-funded epidemiologists may understandably want to protect the data they collect and the knowledge they create. It may be helpful to our contributors to know that, via the Editorial Manager submission system (administered by Epidemiology's publisher, Lippincott Williams & Wilkins) any articles submitted, even if rejected without review, are archived for one year. This archived material includes all original content, including submitted manuscripts and supplemental digital materials, which may include original data. After one year, the system compiles and saves indefinitely a PDF from the materials, but this may not include all supplemental digital materials. Editors and authors can access the materials using their password-protected account.​

We will continue to monitor the news and to think about ways the journal can support and protect public health science, and we welcome readers' input to that end.

 

The views and recommendations of contributors do not necessarily indicate official endorsements or opinions of the Journal, Wolters-Kluwer, or the ISEE. All views are those of the authors and the authors alone.

 

 


Sunday, January 1, 2017

A well-prepared figure conveys information more effectively than tables or text. A poorly prepared figure discourages readers and uses page space inefficiently. Choosing when to use figures and preparing them well is therefore central to writing an effective manuscript.

Figures in Epidemiology usually either convey features of study design or study results. For figures of both types, the first consideration is whether a figure will convey the information more effectively than text or tables used to occupy the same amount of space. Figures occupy a lot of space in a manuscript—we budget 250 words per figure—so if the figure’s information can be conveyed as effectively in that many words or less, then text should be used instead of a figure. Similarly, when presenting study results, tables provide exact results whereas figures do not. If the figure’s information can be conveyed as effectively as a table, then a table should be used to gain the advantage of the exact data.

The advantages offered by a figure over a table are to visually convey changes in information along the figure axes. If there is no compelling change in information along at least one axis of the figure, then a table should convey the information as effectively and more precisely. Compelling figures show changes in information along both axes, and sometimes in a third dimension as well. For example, meta-analyses often include a forest plot in which the point estimates, confidence intervals, and relative weights of each study are plotted along the vertical axis. You can find an example here. While figures of this design are quite common in the meta-analysis literature, the vertical axis has no function other than to separate studies from one another. Simply ranking the point estimates provides additional information, as shown here for a similar set of studies. Additional information along the study-scale can be added by plotting the inverse normal of rank percentile as the vertical axis scale (shown here), instead of equally spacing the ranked studies. This stretches the outlying studies further apart and compresses the studies near the central tendency closer together, which adds information to what is conveyed on the study scale axis. The point is not to advocate for a change to the way meta-analyses are presented, but rather to encourage authors to design figures that convey information along all axes in their figures.

Once the content of the figure and its axes has been decided, preparation of the figure itself comes next. In general, the quality of published figures would improve dramatically if all authors realized and reacted to one fundamental problem: the default settings of most graphics-preparation tools yield figures in which everything is too small. Line and axis thickness, marker size, font size, error bars: these are all too light or too small in default settings. Simply by making everything bigger and heavier, the quality of figures would improve. Try exaggerating these settings, then ratchet back a notch or two.

The space between the axes is valuable real estate – fill it. The space outside the axes is also valuable real estate: so fill that by using large font labels sparsely placed rather than small sparse labels or (even worse) many small labels.

For all text elements of the figure, choose fonts that are easy to read, usually a sans-serif font such as Arial or Helvetica. Slightly moderate font sizes so that the most important information has the largest font and the least important information has the smallest font. Usually that would mean that axis titles have the largest font, axis labels have intermediate font, and legend text and other text elements such as data labels have the smallest font.

Avoid clutter in the figure. Never use figure titles because the caption will suffice. Do not outline the figure or the plotted areas because the axes will suffice. Make sure that every data element is important. For example, in a plot showing results stratified by gender, do not include a line for all genders combined unless that combined information is as important as the gender-specific information. Grid lines should also be avoided. If the location of plotted elements must be so closely inspected as to require grid lines, then the data are probably better suited to a table than to a figure.

Embed legends between the axes, rather than above or below, especially when the distribution of data leaves blank spaces between the axes. Using this empty space for the legend allows the size of the chart to increase because no space is reserved for the legend. Legends are more effective when embedded in the figure than when embedded in the caption. Even better is to label plot elements with text labels directly next to the element, thereby deleting the legend.

Many authors present results in figure panels. These can be quite effective when used judiciously. To start, consider whether a single figure can be used instead of a figure panel by reducing the number of compared categories. If there are so many essential data categories that a figure panel is required, then always keep the scale of all axes constant in all panels of the figure. The point of a figure panel is to visually compare results within and across panels. If the axis scales change in different panels, then the visual comparison across panels will be misleading.

While we encourage authors to embolden their figures by making elements large and thick, we strongly discourage the use of ornamentation such as shadows, shaded backgrounds, and word art. Three dimensional figures are, in general, very difficult to comprehend. Unless a surface must be plotted, it is better to convey the third-dimension information as separate lines within the plot or in a figure panel.

Figure captions are critical to high-quality figures. A figure caption should describe what the reader will find in the figure and from what data it was generated. Readers should be able to picture the figure and understand the study setting in which the data were generated by reading the figure caption. Avoid duplicating the caption information in the main text of the manuscript, but be sure to define any abbreviations, even if they are also defined in the main text. Although Epidemiology disallows almost all abbreviations in the text (see our earlier blog), we will sometimes allow abbreviations in figures (data or axis labels, for instance) that we do not allow in the text.


Epidemiology accepts figures prepared in color. Figures printed in color incur extra charges, as described in the instructions for authors, unless the authors have also paid an Open Access fee. Authors can submit figures in color to appear in only the on-line version, and a gray-scale version to appear in print and in the PDF version. This type of submission incurs no extra charges, but then it is imperative that all figure elements are as easily identifiable in the gray-scale version as in the color version and that the figures are identical except for the color itself. When preparing color figures, authors must choose colors that make the figure content accessible to persons with color vision impairments. Be careful that the graphics software does not gratuitously add color in the form of a pastel background or other elements that add no information.

Our editors examine the quality of figures at different sizes during the editing process, but sometimes when a figure appears in page proofs it looks fuzzy. We will then ask the author to submit a higher-resolution version. The best ways to avoid these last-minute inconveniences are to submit the highest resolution figure that is practical (1200 dpi should be good for most line drawings) and to export the figure directly to a graphics format (.tif, .png, and .pdf work best) rather than re-importing into Word or Powerpoint.

Creating a compelling figure requires a substantial investment of energy and creativity. The guidance above may help to avoid common pitfalls, but the quality of the figure will ultimately be determined primarily by the effort put into the creation.



Tuesday, November 1, 2016

Let’s say right up front that, under our hybrid publishing model, space is limited in the print version of EPIDEMIOLOGY. You already know this. We have a strict budget for print pages each issue, and competition for space is fierce. We have been working with Production to make the best use of this limited space, by thinking about the efficiency of the page layout and by keeping an eye on proofs to avoid mostly blank pages. A great way to advance the goal of space efficiency is to put content online, in supplementary digital content (SDC). Your editors may ask you to do this, for example with sensitivity or subgroup analyses, or you can opt to do so voluntarily. Shorter papers are often more engaging to read, authors save page charges, and the journal can publish more papers within its page budget. Everybody wins when papers are short, as long as they are also complete.


In contrast to printed content, online content is essentially unlimited, a service provided by the publisher for the free use of authors. What can go online? Pretty much anything you have produced that supports what you have written in the main text.  SDC is a good place to park large tables and figure panels, descriptions of study populations, details of methodology, and statistical computing code (which we encourage all authors to submit as SDC). You can also use color freely; color figures come with a fee in the printed journal, but are free in SDC. You, the author, are fully responsible for SDC. Although peer reviewers and editors look at it, we don’t copy-edit it; SDC goes up exactly as you have prepared it (which means it’s probably not a bad idea to save it as a PDF, rather than editable or readily copied text). We create a link to the SDC and place it appropriately in the printed text. If it needs to be revised or corrected, you can email us a new version and we’ll just swap them out.


Our only restrictions: because of server limitations, each file has to be no more than 100 MB in size. Larger total amounts of content can be broken down into smaller files. In addition, labels of sections need to correspond to the way you refer to them in the text, and for that the journal has a convention:


eTable 1, eAppendix 2, eFigure 4 etc.


Most types of content will fit into these categories, with ‘eAppendix’ referring to any text that is not a table or a figure. Numbering them helps guide readers to the relevant content, especially when all the content is saved in a single file and, as with tables and figures appearing in the main (printed) text, make sure they are cited in order.


We have been told that combining all the SDC as a readily downloadable file is helpful to readers, so we will usually ask you to combine them. Most file types, except for spreadsheets with formulas and PowerPoint files with animation, can be saved as PDFs and combined. Statistical computing code is usually in text format, and so can be exported or at least copied and pasted into a word-processing file and, from there, exported to PDF. If you have more than a handful of sections of SDC you may also want to consider including a table of contents at the top.


Because SDC is a separate document, it must - if you are citing other work - have its own bibliography. Number citations in SDC separately from those in the main text (citations can appear in both the published bibliography and the SDC), but you only need one bibliography across all the SDC content. Our copyeditors will go look at your main-text bibliography to make sure there is a corresponding citation for each reference, and will flag any that have none, or that are only in the SDC (which, again, they don’t edit). Please also note, however, that citations in SDC do NOT count towards the Science Citation Index or other indexing services.


Naturally, the less space each paper takes up, the more papers we can publish, and you, as authors, can help with this, too; it’s one small contribution you can make toward being a good member of the research community.

Tuesday, August 30, 2016

The typical outcomes paper in epidemiology usually involves a lot of numbers – multiple exposures and measures of exposures, subgroup analyses, and alternative modeling strategies. The standard of practice when making statistical comparisons is to place an effect estimate within a confidence interval, rather than using a p-value (Epidemiology generally only allows p-values for tests of trend or heterogeneity, and even then strongly discourages comparison with a Type 1 error rate). Outcomes papers thus tend to have three or four tables of data, often with more online, each with up to a dozen columns, but organized in intuitive, digestible, easy-to-follow chunks. If figures are possible, so much the better.


Writing the text of the Results section to summarize the tables and figures may feel like an afterthought. But it is still important, in part because you, as a researcher, know your data better than anyone else, and also because not all readers absorb information the same way. So it’s worth your time to think about what you want to highlight (hint: go beyond the obvious statements along the lines of x was associated with y, z was not associated with y).


I hope you’ll agree it’s also important to make the Results section appealing and useful to read. Many results sections fail to provide any mention of the descriptive finds. These, however, help to put the study into context. How many people were eligible, how many participated, how many cases were observed, and what were patterns of missingness? These and similar questions immediately help the reader to understand who was studied and the quality of the evidence.


When transitioning to internal comparisons, one element to keep in mind is context. Even if you’ve done so in the Methods section, precede each result you give with a hint of what you were looking for in that step of your analysis. Just as important is the flow of language. Of course we don’t expect an epi Results section to read like Walt Whitman, but you’d be surprised how a strategy regarding the presentation of data can improve how well the reader engages with it.


I’ll start with an example of a sentence that, while not particularly long, is seriously hard work to get through:


Similar results were found for lung cancer, colorectal cancer, and breast cancer: lower consumption of jelly beans was associated with an estimated 4%-8% lower hazard ratio (95%CI 0.67 to 1.22, 0.76 to 1.34, and 0.92 to 1.13, respectively), although these estimates were imprecise.


Do you see how you have to go back and forth from the outcomes in the first line to the confidence intervals in the third to match them up, because of the “respectively” device? In addition, it’s hard to parse that range of percentages of lower risk - if there are only three outcomes, why not give just give all three? (More about the imprecise estimates below.) To simplify, keep each outcome in the same phrase as its data:


Consumption of jelly beans was associated with a 4% lower hazard ratio (95% CI 0.67, 1.22) of lung cancer, 7% lower risk (95% CI 0.76, 1.34) of colorectal cancer, and 8% lower risk (95% CI 0.92, 1.13) of breast cancer, although the estimates were imprecise.


A second concern is the use of the percentage hazard ratio. It is too easily confused with a difference estimate of association, when in fact the associations are estimated on the ratio scale. Furthermore, it has different different units than the CI, so you can’t automatically place it within the interval. An even better revision would be:


The hazard ratio associating consumption of jelly beans with lung cancer was 0.96 (95% CI 0.67, 1.22), with colorectal lung cancer was 0.93 (95% CI 0.76, 1.34), and with breast cancer was 0.92 (95% CI 0.92, 1.13) of breast cancer, although the estimates were imprecise.


Next, I hope this idea is not too radical, but consider not putting data in a sentence at all: leave the numbers in the table, if possible, and describe the results in words. That way, a reader can first read your simple summary, and then turn to the tables to pick out the details for him or herself. This strategy works best for secondary findings; results pertaining to the primary aim should always be reported with data. Revising the report of these secondary findings, the edit of the sentence would be:


Consumption of jelly beans was associated with imprecisely measured decreased hazards of lung, colorectal, and breast cancer (Table 3).


Finally, what exactly do the authors mean when they say that the estimates were imprecisely measured? The intervals were actually fairly narrow. We suspect they mean that the intervals include the null, which has nothing to do with the precision. The final, zen edit of the troublesome sentence would be:


The hazard ratios associating jelly beans with the incidence of lung, colorectal, and breast cancer were all near null (Table 3).


We invite you to look at a few outcomes papers and think about the above. Do you even read Results sections? If not, why not? What would you do differently? We’d be happy to discuss.


Take-home messages that will take you a long way toward a readable Results section:


  • Be sure to open the results section with the descriptive findings

  • As the topic sentence in each paragraph, provide a bit of context for each section of the analysis.

  • Keep the outcome with its data (avoid the dreaded “respectively”).

  • Break up long sentences containing a lot of data.

  • Be sure to use the measure of disease occurrence that you are estimating (“risk”, “rate”, “hazard”, etc).

  • For secondary findings, consider leaving effect estimates and confidence intervals out of the text altogether.


While the above recommendations are stylistic, here’s a reminder of a couple of additional requirements relevant to reporting of results in Epidemiology: Avoid causal language – verbs such as impact, affect, increase/decrease – in favor of the language of association. And avoid significance testing as follows:


  • Leave out p-values (except for tests of trend and heterogeneity, but even then do not compare with an acceptable Type 1 error rate)

  • Instead of “x was not significantly associated with y,” just say “x was not associated with y” or “x was associated with an imprecisely measured increase/decrease in y” or “the association of x with y was near null”

  • Avoid the word “significant” in non-statistical senses of the word, and instead choose from the less-loaded words “considerable,” “important,“ “material,” “appreciable,” or “substantial.”


Null results are good! We have recently published an editorial​ seeking persuasively null results. You might even edit the result in the example one step further:


Consumption of jelly beans was not associated with decreased hazard of lung, colorectal, or breast cancer (Table 3).


Sorry, jelly beans.