Aim

To present the results of your analysis in a clear and well-organised way.

Description

Graphs and tables
It is important to present your results in a clear and well-organised way in tables and graphs, since this will make a significant contribution to the attractiveness of your article, poster or PowerPoint presentation. The choice of presenting results in a table or graph depends on the aim, number of variables, analysis methods and personal preferences. Some journals have a fixed policy on the number and design of tables and graphs, usually a maximum of 5 to 6 tables or figures. This should be taken into consideration when writing your article. See examples of guidelines in the details.

Tables and graphs need to be produced in such a way that the reader is able to understand them without having to read any additional text. The title needs to be informative and the rows and columns in the tables or axes of the graphs need to be properly labelled. All abbreviations used need to be explained in full in a footnote below the table or graph. In general tables are appropriate when you want to display the exact numbers from your analyses. Graphs are more appropriate for displaying trends or associations.

It is common practice to have the tables and figures follow a specific order in an article. Table 1 is the baseline table with the most important features of the study population. The results of the analyses of the primary outcome measures are usually displayed in Table 2 (or Figure 1). The remaining tables/figures follow after this.

Almost every results section in an article starts with a paragraph about the recruitment of research participants. These days, when describing an RCT, the majority of medical journals require a “patient flow chart” to be included in the article. This represents how many patients were approached, which ones were selected and excluded (and the exclusion criteria), the dropouts and the number of patients ultimately remaining who participated in the trial. This will usually be Figure 1 in the article.

For other articles these details can be represented in the text. Ensure that the numbers add up and that no participants appear to have disappeared (always ask someone to read through the article to check whether it is clear) (link to example)

A flow chart is also recommended for a systematic review reflecting how many articles have been scanned, how many full text articles have been requested and how many articles have been included (see the systematic review guideline). A flow chart can also be useful in clarifying a complex treatment protocol.

The baseline table (usually Table 1) is intended as a description of your research population. This will include the socio-demographic variables from your research population, such as age, gender and educational level. It will also contain the most important clinical characteristics describing your population, such as the severity of the disorder and general health status. Finally, all baseline values of the determinants, outcomes and potential prognostic variables will be included as well. The average, number of observations and standard deviation can be displayed in the baseline table (or the median and range for data not normally distributed, or ordinal data).

When including effect estimates (e.g. when comparing two study populations in a trial) the effect estimate (e.g. average difference, relative risk or odds ratio) should always be included with the 95% confidence interval.

For (multiple) linear regression analysis the regression coefficient(s) (B) should be included for all cases along, including the standard error(s) or a confidence interval. The p-value may also be included. However, this is not necessary if you present confidence intervals. Often odds ratio(s) and the 95% confidence interval are included in (multiple) logistic regression analyses.
For an association model (e.g. what is the effect of alcohol use on developing a cardiac infarction?) it is advisable to include both the raw effect estimates (e.g. odds ratio with 95% confidence interval), as well as any corrected effect estimates (e.g. corrected for age and gender).
For a prognostic model (e.g. what predicts levels of recovery after 6 months?) a measure of how well the model works needs to be included along with the regression coefficients, e.g. percentage variance explained or distinctive power (area under the ROC curve). For a prognostic model it is also necessary to properly describe the strategy used in selecting the variables and the criteria for including variables in the model.
N.b.: Please refer to the postgraduate course in logistic regression for more information about the difference between association and prognostic models.

• Scientific style and format: the CBE manual for authors, editors, and publishers, 6th ed. Style Manual Committee, Council of Biology Editors. New York: Cambridge University Press, 1994.
• Iverson C, Flanagin A, Fontanarosa PB, et al. American Medical Association manual of style: a guide for authors and editors, 9th ed. Hagerstown, Maryland, Lippincott Williams & Wilkins; 1997.

V1.1: 1 Jan 2010: English translation.
V1.0: 31 Jan 2008.