Baseline data

Item 15 - A table showing baseline demographic and clinical characteristics for each group


Example

Table 4 - Example of reporting baseline demographic and clinical characteristics*

(Adapted from table 1 of Yusuf et al (209))


Telmisartan (N=2954) Placebo (N=2972)
Age (years) 66.9 (7.3) 66.9 (7.4)
Sex (female) 1280 (43.3%) 1267 (42.6%)
Smoking status:

  Current 293 (9.9%) 289 (9.7%)
  Past 1273 (43.1%) 1283 (43.2%)
Ethnic origin:

  Asian 637 (21.6%) 624 (21.0%)
  Arab 37 (1.3%) 40 (1.3%)
  African 51 (1.7%) 55 (1.9%)
  European 1801 (61.0%) 1820 (61.2%)
  Native or Aboriginal 390 (13.2%) 393 (13.2%)
  Other 38 (1.3%) 40 (1.3%)
Blood pressure (mm Hg) 140.7 (16.8/81.8) (10.1) 141.3 (16.4/82.0) (10.2)
Heart rate (beats per min) 68.8 (11.5) 68.8 (12.1)
Cholesterol (mmol/l):

   Total 5.09 (1.18) 5.08 (1.15)
   LDL 3.02 (1.01) 3.03 (1.02)
   HDL 1.27 (0.37) 1.28 (0.41)
Coronary artery disease 2211 (74.8%) 2207 (74.3%)
Myocardial infarction 1381 (46.8%) 1360 (45.8%)
Angina pectoris 1412 (47.8%) 1412 (47.5%)
Peripheral artery disease 349 (11.8%) 323 (10.9%)
Hypertension 2259 (76.5%) 2269 (76.3%)
Diabetes 1059 (35.8%) 1059 (35.6%)

*Data are means (SD) or numbers (%).

Explanation

Although the eligibility criteria (see item 4a) 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.

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 that the groups are equivalent at baseline. Any differences in baseline characteristics are, however, the result of chance rather than bias.(32) The study groups should be compared at baseline for important demographic and clinical characteristics so that readers can assess how similar they were. Baseline data are especially valuable for outcomes that can also be measured at the start of the trial (such as blood pressure).

Baseline information is most efficiently presented in a table (see table 4). 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 a centile range (such as the 25th and 75th centiles).(177) Standard errors and confidence intervals 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.(48) (177)

Unfortunately significance tests of baseline differences are still common(23) (32) (210); they were reported in half of 50 RCTs trials published in leading general journals in 1997.(183) Such significance tests assess the probability that observed baseline differences could have occurred by chance; however, we already know that any differences are caused by chance. Tests of baseline differences are not necessarily wrong, just illogical.(211) Such hypothesis testing is superfluous and can mislead investigators and their readers. Rather, comparisons at baseline should be based on consideration of the prognostic strength of the variables measured and the size of any chance imbalances that have occurred.(211)

Page last edited: 20 April 2010