| | From Submission to Publication: A Retrospective Review of the Tables and Figures in a Cohort of Randomized Controlled Trials Submitted to the British Medical JournalPresented at the 5th International Congress on Peer Review and Biomedical Publication, Chicago, IL, September 16-September 18, 2005. Received 27 March 2006; received in revised form 30 May 2006; accepted 1 June 2006. published online 19 September 2006. Study objectiveWe characterize the quantity and quality of data tables and figures in reports of randomized controlled trials (RCTs) submitted to the British Medical Journal (BMJ) and published in peer-reviewed journals. We investigate how the peer review process affected table and figure quality. MethodsWe reviewed 62 consecutive reports of RCTs submitted to the BMJ in 2001 that were later published in the BMJ (n=12) or elsewhere. We counted and categorized the tables and figures in both the initial submissions and published articles. Using standardized instruments and procedures, we analyzed the quality of these tables and figures and checked BMJ editorial documents to see whether changes were triggered by their review process. ResultsThe numbers of tables and figures did not change markedly between submission and publication. Five percent of publications had no data tables; 56% had no data figures. Data density was low for published tables and figures. Tables seldom showed data stratified on important covariates; 88% of published tables were simple lists or were stratified on only 1 variable. Less than half the figures met their data presentation potential, with most failing to portray by-subject data and few displaying advanced features such as pairing, symbolic dimensionality, or small multiples. BMJ external peer reviewers seldom commented on tables or figures. ConclusionTables and figures can convey details and complex relationships not easily described in text. Although tables are included in most submitted and published articles, they are not presented optimally; figures are used sparingly and are also of suboptimal quality. Journals should consider improving their table and figure quality in the hope that improved graphics will empower readers to scrutinize the data, thereby dissuading authors from presenting biased analyses and misrepresented conclusions. Introduction  Background Tables and figures are a powerful means of conveying investigational results.1, 2, 3, 4, 5, 6 When properly constructed, they succinctly convey complex relationships prose cannot describe. Tables and figures bring readers closer to the actual data than prose, and their use can reduce the potential for biases that can occur when the dimensionality of data is reduced.7 Dimensionality is reduced when the investigator presents summaries of the data rather than the data points themselves. For example, an investigator who collected the values of 2 continuous variables on 100 patients could present them as a scatterplot (200 numbers), 2 box plots (10 numbers+any outlying values), or a bar graph of each variable’s mean (2 numbers [+4 more if confidence intervals (CIs) are presented]). Only the scatterplot preserves the dimensionality of the data and conveys the relationship between the 2 variables within each patient that is inherent to this data set.⁎ Editor’s Capsule SummaryWhat is already known on this topic Tables and figures are a fundamental means of scientific communication. Evidence suggests that in many reports of clinical research, the tables and figures are suboptimal. What question this study addressed This study compares the quality of tables and figures in the submitted and published versions of a series of randomized trials submitted to the British Medical Journal and examines the effect of peer review on table and figure quality. What this study adds to our knowledge Tables and figures were generally simplistic and failed to optimize the amount of information portrayed or the style in which it was portrayed. The peer review process had little effect on table and figure quality. How this might change clinical practice This study will not change clinical practice, but it may lead to improvements in the way that medical research results are communicated. With each decrease in dimensionality, there is the potential that truth and nuance will be masked. Importance Given the potential importance of tables and figures, one might expect that the medical journal peer-review process would pay particular attention to them. Preliminary results suggest otherwise.8 Day et al found that only 6% of general reviewer comments concerned tables or figures (D. Schriger, written communication, August 2002). Cooper et al9, 10 found that many figures in large- and medium-circulation journals were suboptimal. Although there has been little empirical research on the qualities that make for a good table or figure, there has been a fair amount of theoretical work. This work suggests that tables and figures should be self-explanatory, should show overall patterns and detailed information, and should have a data density—the amount of information presented per cm2—that justifies their existence (ie, are denser than text).1, 2, 3, 4, 5, 6, 7 Goals of This Investigation We executed this study to assess formally the quality of tables and figures in reports of randomized controlled trials (RCTs) submitted to a general medical journal and to examine how often and in what ways they were modified between submission and publication. We chose to study the figures and tables in reports on a single study design, the RCT, for 2 reasons. First, the kinds of tables and figures required to present data from studies of similar design should be few and would be expected to be somewhat consistent among articles. Second, much of the content of reports of RCTs has been standardized in recent years because of CONSORT, and we wanted to see whether the CONSORT statement, though it makes no recommendations about data tables and data figures, might nevertheless have influenced the content of tables and figures.11, 12 In short, we wanted to understand how tables and figures were being used in reports of RCTs, whether tables and figures met their potential as a powerful means of conveying study results, and whether the peer-review process improved the quality of published tables and figures. Materials and methods  We identified 72 consecutive reports of RCTs submitted to the British Medical Journal (BMJ) in 2001, 12 of which were subsequently published in that journal. By searching MEDLINE and Google by article title and author names, we identified an additional 50 articles that were published in specialty journals after rejection by BMJ; none were published in another general medical journal. We presume that the 10 remaining submissions have not yet been published as of September 2006. We obtained and reviewed all editorial material held by the BMJ for each submission to determine whether the article was immediately rejected by a screening editor or received formal written evaluation by an external peer reviewer or a BMJ statistician. We reviewed all formal written evaluations to determine whether there were any comments related to the submitted tables or figures. The raters who assessed the quality of the tables and figures were not blinded to the purpose of the study or, for the second step of the evaluation (see below), journal title and author names. Furthermore, typefaces were such that raters knew whether they were viewing a submitted or published table or figure. The study was approved by the BMJ ethics committee. Figures We assessed the quality of all data figures in the submitted and published articles (with the exception of flow diagrams that show the number of subjects completing each phase of the study) by using a methodology used to study the quality of figures in Annals of Emergency Medicine and the Journal of the American Medical Association.9, 10 In this 2-step process, the figure and its caption are first reviewed in isolation from the remainder of the article so that the type of figure and the figure’s clarity, completeness, use of special features, and data density can be assessed. The definition and method for scoring each figure characteristic and the method for calculating data density—the number of data elements per square centimeter of figure—can be found in Appendix E1, Appendix E2 (available online at http://www.annemergmed.com). In the second step, the figure is viewed in the context of the article, and issues related to redundancy, contradiction, and the appropriateness of the figure type for the data being portrayed are considered (see Table 1, Table 2 for a list of items). We also made a subjective judgment about whether the figure “met its potential” using principles espoused by Tufte,1, 2, 3 Cleveland,4 and Wainer.5, 6 All figures were independently scored by 2 authors, and interrater reliability was assessed as percentage of agreement. Differences were adjudicated by consensus of the raters. For the purpose of this study, we developed additional forms to assess differences between submitted and published figures to note whether changes were improvements and to indicate whether changes were prompted by comments from BMJ peer reviewers. | | |  | Characteristic | Submitted Version | Published Version |  |
|---|
 | Articles with a figure, No. (%) | 26 (42) | 27 (44) |  |  | Number of figures | 36 | 41 |  |  | Type of figure, No. (%) | | |  |  | Univariate display, simple | 26 (72) | 28 (68) |  |  | Bar graph without CI | 4 (11) | 9 (22) |  |  | Bar graph with CI | 3 (8) | 5 (12) |  |  | Point graph without CI | 14 (39) | 10 (24) |  |  | Point graph with CI | 5 (14) | 4 (10) |  |  | Univariate display | 6 (17) | 8 (19) |  |  | One-way plots | 0 (0) | 0 (0) |  |  | Box-and-whisker plots | 2 (6) | 2 (5) |  |  | Histogram | 1 (3) | 1 (2) |  |  | Survival curve | 3 (8) | 5 (12) |  |  | Bivariate display | 1 (3) | 2 (5) |  |  | Scatterplot | 1 (3) | 2 (5) |  |  | Other | 3 (8) | 3 (8) |  |  | Special features | | |  |  | Illustration of pairing | 1 (3) | 2 (5) |  |  | Symbolic dimensionality | 1 (3) | 1 (2) |  |  | Small multiples | 0 (0) | 1 (2) |  | | | |
| | |  | Characteristic | Submitted Version, n=36 | Published Version, n=41 |  |
|---|
 | Internal graph characteristics, % | | |  |  | Symbols and abbreviations not adequately defined | 44 | 46 |  |  | Graph not self-explanatory | 36 | 27 |  |  | Internal contradictions | 14 | 15 |  |  | Lack of visual clarity | 8 | 12 |  |  | Nonstandard graphing conventions used | 14 | 20 |  |  | Numeric distortion | 6 | 3 |  |  | Mode of comparison (>1 may apply), % | | |  |  | Position along a common scale | 92 | 90 |  |  | Position on identical nonaligned scales | 19 | 22 |  |  | Length | 6 | 5 |  |  | Slope/angle | 39 | 41 |  |  | Area/volume | 0 | 0 |  |  | Color (hue, saturation or density) | 0 | 0 |  |  | Efficiency of data presentation | | |  |  | Internal redundancy, % | 8 | 5 |  |  | Chartjunk⁎, % | 6 | 10 |  |  | Data density index (DDI) (data/ cm2), mean, n=29† | 0.5 | 0.5 |  | | | |
| ⁎ The definition of chartjunk and other terms can be found in the glossary (Appendix E1, available online at http://www.annemergmed.com). †Data density of submitted article figures was calculated assuming that the figure was the same size as the published figure. |
Tables After performing an unsuccessful search of MEDLINE and Google for valid instruments for scoring table quality, we developed a methodology for this purpose, based on instruments used to score figures and the relevant literature on tables.1, 2, 3, 5, 6, 9, 10, 13, 14 First, we categorized each published table as informational or comparative and classified the nature of the information or comparison. To conserve labor, we then identified 100 tables for detailed quality scoring by randomly selecting 1 table from each of the 59 articles that had tables and randomly selecting the remaining 41 from the unselected tables. As we did for figures, we first judged the 100 tables in isolation to assess their dimensionality, cell content, and self-sufficiency (ie, was the table comprehensible without referring to the text) (see Appendix E3 for details of scoring methods, available online at http://www.annemergmed.com). Dimensionality (no-way, 1-way, 2-way, or 3-way) refers to the number of variables used to stratify the quantity that is the focus of the table. For example, a table that listed characteristics of the entire sample is no-way, a table that showed the mean age for intervention and control groups is 1-way (age stratified on group), and a table that describes mean age for each sex in each group (eg, men in intervention group, women in control group) is 2-way. The data content of tables was measured using scoring rules developed for that purpose (Appendix E4, available online at http://www.annemergmed.com). We counted the total number of numeric elements in the data matrix, as well as the number of elements that were undefined, had undefined units, had undefined Ns, or were inferential statistics such as P values and χ2 statistics. The 100 tables were then scored in the context of the article to assess redundancy with text and figures, whether the table presented internal values or marginal totals, and whether the data would have been better presented as a figure or as text (see Appendix E1 for definitions, available online at http://www.annemergmed.com). Raters trained on 20 tables not selected for detailed quality scoring. Scoring rules and forms were revised after examining discrepancies. Two raters independently scored 30 tables, interrater reliability was assessed as percentage of agreement, and discrepancies were adjudicated by the raters and first author. The remaining 70 tables were scored by individual raters. We developed 3 forms for examining how tables changed from submission to publication: one for tables that were dropped, one for tables that were added, and one for tables that appeared in both the submitted and published versions of the article. The appropriate form was completed for each table. For tables represented in both submitted and published versions, we noted whether changes were made to each table element (title, box [column headers], stub [row headers], data cells, footnotes, shading, and gridlines) and judged whether each change improved the table, degraded the table, or had a neutral effect. We cross-indexed these data with the list of comments about tables made by BMJ peer reviewers to determine whether peer-reviewer suggestions had been heeded. For dropped tables, we determined what happened to the material; for added tables, we determined the origin of the material. For both added and deleted tables, we made a subjective judgment about whether the change was an improvement by using criteria suggested by Tufte,1 Wainer,5, 6 Walker and Durost,13 and Finney.14 Results  The 62 submissions that have been published composed our study sample. Records of the editorial process were available for 57 articles (5 records could not be found). Thirty-five of these articles had been formally reviewed by external peer reviewers or BMJ statisticians. Five (14%) of the 35 external reviews contained comments about the article’s figures, and 16 (46%) had comments about the tables. Figures There were 41 figures in the 62 submitted articles, 36 of which were available for evaluation (5 were referred to in the article, but no figures were found either because they were lost in the BMJ filing process or were never submitted). Between submission and publication, 8 of the 41 figures were dropped and 8 figures were added, resulting in 41 published figures. Interrater agreement was 96% on the 20 items; no item had less than 92% agreement. Thirty-five published articles had no figures, 18 had 1, 4 had 2, and 5 had 3 (Figure 1). Eighty-eight percent (36/41) of published figures were univariate, and 79% (28/36) of these graphed a statistic about the data (eg, mean, median, proportion) rather than the data itself (Table 1). The number of detailed figures (those showing univariate or bivariate distributions) increased from submission to publication but only by 5% (7/36 to 10/41). Two scatterplots and 5 survival curves (17% of all figures) showed data for individual participants. Ten percent (2/19) of published figures of paired data depicted the pairing. Thirty-two percent (10/31) of figures that concerned data that had an underlying distribution (ie, data that were not binary or proportions) portrayed that distribution, and 22% of figures were deemed wholly redundant with text or tables. We judged that 49% of published figures met their potential. The internal characteristics of the figures improved slightly from submission to publication, though a substantial number of published figures remained suboptimal (Table 2). The mode of comparison and the efficiency of data presentation were essentially unchanged. Of the 29 figures whose submitted and published versions were similar, 15 were unchanged and 14 had minor modifications: type of figure (4), labels (7), statistics (2), legends (2), gridlines (5), and shading (2). The mean data density index—the number of data points per square centimeter—was essentially identical (0.50 data elements/cm2) in the submitted and published versions of these 29 figures (Figure 2). Peer reviewers at BMJ made comments about the figures of 5 articles. One reviewer commented on the title and axis labels for one figure. This figure was omitted in the published article. The other 4 figures were published unchanged despite the reviewers’ suggestions. The reviewers had requested that authors rename the axis in one figure, add error bars in one figure, and completely redraw the other 2. For one of these, the authors provided the journal with the requested revision but successfully (and appropriately) argued that the original figure was better. Tables There were 207 tables (205 available for evaluation) in 58 submissions. Thirty-four were dropped and 24 added between submission and publication, resulting in 197 published tables in 59 articles. Three publications had no tables; the majority had 3 or 4 (Figure 1). Eighty-four percent (166/197) of published tables were comparative; the others were informational (Table 3). Our 2 raters achieved at least 86% agreement on 17 categorical items (mean agreement 93%). On continuous items, raters agreed on 89% and were within 5% on 99% of items (see Table 3, Table 4 and the text below for items). | | |  | Purpose of Table | Published Tables, %, n=197 |  |
|---|
 | Information | 16 |  |  | Model results | 50 |  |  | About participation | 22 |  |  | Results | 16 |  |  | Model inputs | 3 |  |  | Adverse effects | 3 |  |  | Other | 6 |  |  | Comparison | 84 |  |  | Main results | 36 |  |  | Baseline characteristics | 35 |  |  | Secondary results | 20 |  |  | Adverse effects | 4 |  |  | Subgroups | 2 |  |  | Participants | 1 |  | | | |
| | |  | Characteristic | Published Tables, n=100 |  |
|---|
 | Number of strata | |  |  | None (ie, a list) | 10 |  |  | One-way (eg, mean height in men vs women) | 78 |  |  | Two-way (eg, mean height by sex and ethnicity) | 12 |  |  | Table characteristics | |  |  | Table is not self-explanatory | 4 |  |  | Table has undefined symbols or abbreviations | 23 |  |  | Table has internal contradictions | 5 |  |  | Table has inappropriately precise entries (eg, 4.6654%) | 21 |  |  | Table content (out of 8,888 elements), % | |  |  | Data elements | 66 |  |  | Inferential statistical elements | 8 |  |  | Label elements (box, stub) | 26 |  |  | Undefined elements (155/6,609 numeric elements) | 2 |  |  | Elements with undefined units (eg, mm Hg) (32/872) | 4 |  |  | Elements with unstated denominator (307/6,328) | 5 |  |  | Data density (data elements/cm2), mean | 1.2 |  | | | |
In our quality analysis of 100 randomly selected tables, only 12 were 2-way; time (before-after) was the second variable in 8 of these. Only 1 table stratified on a variable of interest to examine confounding. Three tables failed to show the core data, presenting only marginal totals. Two tables were deemed to contradict the text, 3 were redundant with text or figures, 4 could have been folded into other tables, 4 would have been better presented as figures, and 2 should have been presented as text. There were 6,609 numeric and 2,279 explanatory elements in these tables, an average of 89 elements per table (median 76; interquartile range [IQR] 49, 115; range 11 to 352). One hundred fifty-five (2.3%) numeric elements in 9 tables were undefined, meaning that the table provided no indication of what the number represented. Twenty-one tables presented numbers that were too precise (did not heed the concept of significant figures). Forty tables had data elements that had units (eg, mm Hg or cm/second) associated with them. The units were not specified for 3.7% of the 872 numbers that needed them. Ninety-six percent of data elements had an associated N, the number of subjects the element was based on. This N was unavailable for 4.9% of these elements. Forty tables contained elements that presented test statistics or P values. In these tables, these inferential statistics comprised 30% of all numeric elements (median 18%; IQR 12%, 32%). Overall, 11% of all numeric cells were inferential statistics. The mean data density of these 100 tables was 1.2 elements/cm2 (median 1.1; IQR 0.8, 2.9) (Figure 2). We judged 92 of the 100 table titles to be adequate, though 51% failed to define the predominant cell content. Five stubs (row headers) were not clear, and 8 were not logically organized. For boxes (column headers), these numbers were 3 and 5, respectively. Nineteen tables used shading for decorative purpose and 10 for navigational purposes (helping readers track across rows or down columns); none used shading to direct readers to the most important cells in the table.15 None of the 171 tables that appeared in both submitted and published articles had a major revision (eg, strata added); however, all but 8 had minor changes (title 56%, box 48%, stub 53%, cells 53%, footnotes 51%, shading 22%, gridlines 47%). We judged 59% of the 664 changes as improvements, 37% as neutral, and 4% as degradations. One hundred sixteen tables were in articles that were formally peer reviewed at the BMJ; peer reviewers made 47 comments about 37 (32%) of them. Authors heeded 18 (57%) of the 26 comments about articles published in BMJ in contrast to 9 (31%) of the 21 comments about articles subsequently published elsewhere (difference 26%; 95% CI –1% to 54%). Nine of the 34 dropped tables were replaced with text or figures, 14 had content folded into an existing or new table, and 11 were simply omitted. Thirty of these changes were deemed improvements. Seven of the 24 new tables were made by combining existing tables, 1 was converted from a figure, and 7 from text. The remaining 9 were completely new. Twenty changes were considered improvements. Limitations  There is no established, validated standard for graphic and tabular excellence. We believe that scientific articles should present sufficient data so that the reader can fully evaluate the information and reach his or her own conclusions about the results. Ziman16 called this “consensibility” and stressed that it was a necessary condition for the scientific process. The judgments we made in this article are consonant with this belief; we would be remiss, however, if we failed to acknowledge the lack of empirical evidence about which graphic formats are best: increase consensibility, better educate readers, and are most palatable to readers. There is a tension between achieving high dimensionality in a graphic and overwhelming readers with more information than they desire or can comprehend. We err on the side of providing readers with more information so that they can make more informed judgments but acknowledge that a case can be made for a less-is-more approach. We desired to develop an objective means of judging the complexity of the graphic as a function of the complexity of the data (ie, high-dimension data require high-dimension graphics), but without access to the actual data, we could not establish a valid way of quantifying the data’s dimensionality. Instead, we used the subjective question, Does the figure meet its potential? to address this concept. Only 49% of figures did so. We had good interrater reliability on this item and other highly subjective items such as “would this table be better as a figure” and “is this change an improvement.” Although this suggests that our concepts of graphic and tabular quality can be communicated to raters and consistently applied, we have no proof that they have meaning or validity. Our findings are based on a small sample, 62 published reports of RCTs submitted to the BMJ in 2001, and may not be applicable to tables and figures in RCTs submitted to other journals or to tables and figures in articles describing investigations that used other study designs. There were an insufficient number of articles published in the BMJ (12) to justify comparing these articles to those that were published elsewhere. Finally, we did not have access to peer-review documents for journals that reviewed these RCTs before or after their submission to BMJ and therefore have only partial insight into how peer review modifies tables and figures. Discussion  Figures can convey complex information and can be used to tell the story of an investigation. Detailed tables, especially when stratified on variables of interest, can do the same. Yet only 42% of this cohort of reports of RCTs submitted to BMJ in 2001 had any data figures, and the editorial process at BMJ and other journals that refereed these articles increased this number by only 2%. Gelman et al17 argue persuasively that figures should replace tables in many circumstances, but we saw little evidence of their advice being heeded either before submission or during the publication process. The few figures that were published were predominantly (72%) simple univariate displays, and 88% of tables were no way or 1 way, formats that fail to capture the detail and nuance of the data. Peer review and editorial guidance did little to change this. Similar observations have been made in other disciplines.18 This failure to adequately portray data is occurring at a time when the veracity of journal articles has come under scrutiny.19, 20, 21, 22 Given these concerns about the validity of the medical literature, it seems particularly important for authors to bring readers as close to the actual data as possible, thereby reducing the possibility for biases that can be introduced when the dimensionality of the data is decreased or the data are subjected to statistical analysis. Our findings suggest that these concerns have moved neither authors nor editors to improve the quality of tables and figures. Figures seldom portrayed by-subject data and almost never incorporated techniques such as pairing, symbolic dimensionality, or small multiples, the very features that Tufte1, 2, 3 associates with graphic excellence. Data density was low for both tables (1.1 data elements/cm2) and figures (0.5 data element/cm2). In contrast, the median data density of data figures in the Journal of the American Medical Association was 1.1 data elements/cm2, in Annals of Emergency Medicine was 0.95 data elements/cm2, and in some journals is as high as 7 elements/cm2.9, 10, 23 It is unusual for table data density to exceed figure density because figures have the potential for much higher densities, yet that is what we observed. The predominance of simple univariate displays in our sample explains this phenomenon. A bar graph showing the mean value and CI for 2 limbs of an RCT contains only 8 to 10 data elements (2 means, 4 CI values, 2 group identifiers, and possibly 2 Ns). Such figures are usually given the same amount of space as a table that contains several rows and columns, and hence the table has a greater density. Our data provide evidence that journals, or at least the BMJ, are interested in the appearance of tables. Virtually all tables were tweaked during the publication process, and many of these tweaks were considered improvements. Unfortunately, the majority of tweaks were stylistic (eg, adding blue shading behind the box and stub or spelling out abbreviations), and few truly improved the data content or the ease of use of the tables. We suspect that many of these changes were made by BMJ publication staff (eg, technical editors and graphic artists) with no or little input from medical editors. Unlike the BMJ, the majority of journals do not have the luxury of a full-time graphics staff, and tables and figures in these journals are less likely to be redrawn and improved. At all journals, a major impediment to change is that the kinds of improvements we are advocating require either a person who understands both the science and the graphics or persons who know one or the other who are given adequate time to work together on each graphic. We suspect that many authors and editors do not have ready access to such a person or persons. Our study design did not permit us to examine why tables and figures infrequently contained the attributes that Tufte associates with graphic excellence. The list of possibilities is long and includes lack of knowledge of tabular and graphic design by authors and editors; unwillingness of authors and editors to expend the considerable time it takes to make high-quality graphics; ubiquitous software with default settings that produce mediocre graphs (yes, we are talking about your products, Mr. Gates); habituation toward normative, albeit low, standards (“everyone else draws a bar graph, so we will too”); desire to hide data that do not appear as pristine as a summary statistic of the data; and belief that readers want to see the “headlines,” not the details of the data. It would be important to understand the reasons why mediocrity is the norm if we desire to improve the tabular and graphic quality of data presentation in research articles. Efforts to improve tables and figures have been sparse and sporadic. Although a few medical journals have appointed graphics editors to improve table and figure quality, only 13% of instructions for authors of high-impact journals offer advice about the content or structure of data tables and figures.24 CONSORT, a widely accepted standard for the reporting of RCTs, mandates a figure that illustrates subject flow through the study but makes no recommendations about data tables and figures.11 Expanding CONSORT to include table and figure standards is an appealing means of improving table and figure quality in RCTs, assuming that consensus could be reached about what advice would be provided. This is a nontrivial issue because the majority of writing about this topic is theoretical and, with the exception of Cleveland,4 there has been little empirical work on what graph types convey the most information or are the most palatable to readers. In the absence of empirical research, it is difficult to insist on any particular graphic standard. In summary, this study provides evidence that tables and figures in a cohort of reports of RCTs submitted to the BMJ in 2001 were suboptimal and were not substantively improved by the peer review process. Journals should consider improving their table and figure quality in the hope that improved graphics will empower readers to scrutinize the data, thereby dissuading authors from presenting biased analyses and misrepresenting conclusions. Appendix  References  1. 1Tufte ER. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press; 1983;. 2. 2Tufte ER. Envisioning Information. Cheshire, CT: Graphics Press; 1990;. 3. 3Tufte ER. Visual Explanations: Images and Quantities, Evidence and Narrative. Cheshire, CT: Graphics Press; 1997;. 4. 4Cleveland WS. Visualizing Data. Summit, NJ: Hobart Press; 1993;. 5. 5Wainer H. Visual Revelations: Graphical Tales of Fate and Deception from Napoleon Bonaparte to Ross Perot. 2nd ed.. Hillsdale, NJ: Lawrence Erlbaum Associates; 2000;. 6. 6Wainer H. Graphic Discovery: A Trout in the Milk and Other Visual Adventures. Princeton, NJ: Princeton University Press; 2004;. 7. 7Schriger DL, Cooper RJ. Achieving graphical excellence: suggestions and methods for creating high-quality visual displays of experimental data. Ann Emerg Med. 2001;37:75–87. Abstract | Full Text |
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8. 8Day FC, Schriger DL, Todd C. The use of dedicated methodology and statistical reviewers for peer review: a content analysis of comments to authors made by methodology and regular reviewers. Ann Emerg Med. 2002;40:329–333. Abstract | Full Text |
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9. 9Cooper RJ, Schriger DS, Close RJH. Graphical literacy: the quality of graphs in a large-circulation journal. Ann Emerg Med. 2002;40:317–322. Abstract | Full Text |
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10. 10Cooper RJ, Schriger DS, Tashman DA. An evaluation of the graphical literacy of Annals of Emergency Medicine. Ann Emerg Med. 2001;37:13–19. Abstract | Full Text |
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11. 11Moher D, Schulz KF, Altman DG, et al. The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomized trials. Ann Intern Med. 2001;134:657–662. MEDLINE 12. 12Altman DG, Schulz KF, Moher D, et al. The revised CONSORT statement for reporting randomized trials: explanation and elaboration. Ann Intern Med. 2001;134:663–694. MEDLINE 13. 13Walker HM, Durost WN. Statistical Tables: Their Structure and Use. New York, NY: Bureau of Publications, Teachers College, Columbia University; 1936;. 14. 14Finney DJ. On presenting tables and diagrams. Scholarly Publishing. 1986;17:327–342. 15. 15Wainer H. In: Visual Revelations: Graphical Tales of Fate and Deception from Napoleon Bonaparte to Ross Perot. 2nd ed.. Hillsdale, NJ: Lawrence Erlbaum Associates; 2000;p. 101. 16. 16Ziman J. In: Reliable Knowledge: An Exploration of the Grounds for Belief in Science. Cambridge, England: Cambridge University Press; 1978;p. 6. 17. 17Gelman A, Pasarica C, Dodhia R. Let’s practice what we preach: turning tables into graphs. Am Statistician. 2002;56:121–130. 18. 18Best LA, Smith LD, Stubbs A. Graph use in psychology and other sciences. Behav Processes. 2001;45:155–165. 19. 19Horton R. The dawn of McScience. New York Rev Books. 2004;51:7–9. 20. 20Smith R. Medical journals are an extension of the marketing arm of pharmaceutical companies. PLoS Med. 2005;2:e138.
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23. 23Tufte ER. Multifunctioning graphical elements. In: Tufte ER editors. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press; 1983;p. 147. 24. 24Schriger DL, Arora S, Altman DG. The content of medical journal instructions for authors. Ann Emerg Med. 2006;48:743–749. Abstract | Full Text |
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a University of California, Los Angeles Emergency Medicine Center, Los Angeles, CA b University of California, Los Angeles School of Medicine, Los Angeles, CA c Cancer Research United Kingdom/National Health Services Centre for Statistics in Medicine, Oxford, United Kingdom d British Medical Journal, London, UK e Amherst College, Amherst, MA. Address for correspondence: David L. Schriger, MD, MPH, 924 Westwood Blvd. #300, Los Angeles, CA 90024-2924; 310-794-0593, fax 310-794-0599
Supervising editor: Michael L. Callaham, MD Author contributions: DLS and DGA conceived the study and laid out the initial design. SS obtained the research material and helped with the design. DLS designed the data forms; these were reviewed by all authors and pilot tested by RS and PYL. DLS and RS did the data abstraction for the figures study. DLS, RS, and PYL did the data abstraction for the tables study. DLS did the data analysis and drafted the article. RS drafted Appendix E4. All authors actively participated in the editing of the article and approved the final version. DLS takes responsibility for the paper as a whole. Funding and support: Ms. Sinha was funded in part by a summer research grant from the UCLA School of Medicine Short Term Training Program. Publication dates: Available online September 15, 2006. Reprints not available from the authors. PII: S0196-0644(06)00874-2 doi:10.1016/j.annemergmed.2006.06.017 © 2006 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved. | |
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