Example

See table 10 [453].

Explanation

Although the eligibility criteria (item 12a) indicate who was eligible for the trial, it is also important to know the characteristics of the participants who were actually included. This information allows readers, especially clinicians, to judge how relevant the results of a trial might be to an individual patient. Participant baseline demographics may include characteristics such as age, sex and/or gender,[182] place of residence, race and/or ethnicity, culture and/or religion, language, occupation, education, or socioeconomic status. Baseline clinical characteristics include those which are identical, or closely related, to the trial outcomes [454].

 

Randomised trials aim to compare groups of participants that differ only with respect to the intervention (treatment). Although proper random assignment prevents selection bias, it does not guarantee similarity of the groups at baseline. Any differences in baseline characteristics are, however, the result of chance rather than bias [294]. Important demographic and clinical characteristics should be presented so that readers can assess how similar the groups were at baseline. Baseline data are especially valuable for outcomes that can also be measured at the start of the trial (eg, blood pressure).

 

Baseline information is most efficiently presented in a table. For continuous variables, such as weight or blood pressure, the variability of the data should be reported, along with average values. Continuous variables can be summarised for each group by the mean and standard deviation. When continuous data have an asymmetrical distribution, a preferable approach may be to quote the median and percentile values (eg, the 25th and 75th percentiles) [365]. Standard errors and CIs are not appropriate for describing variability—they are inferential rather than descriptive statistics. Variables with a small number of ordered categories (such as stages of disease I to IV) should not be treated as continuous variables; instead, numbers and proportions should be reported for each category [364, 365].

 

Significance testing of baseline differences is not recommended and should not be reported [269, 294, 455]. Such significance tests assess the probability that observed baseline differences could have occurred by chance; however, providing the randomisation has not been subverted or comprised, any differences are caused by chance. Unfortunately, such significance tests are still relatively common [456-458]. Such hypothesis testing is superfluous and can mislead investigators and their readers [459]. Rather, comparisons at baseline should be based on consideration of the prognostic strength of the variables measured and the magnitude of any chance imbalances that have occurred [459].