It is known that clinical studies can generate discordant results. This observation is addressed scientifically in various ways. Deviant study results may be understood as an expression of spreading or scattering from a supposed true value (whereas deviation depends on the precision of the methods). An alternative approach is to explain differences not statistically but by way of content . In considering individual studies, there should be an estimate to what extent the study conclusions are distorted by systematic factors of bias. Here the focus lies usually on so called internal validity, the comparability of test and control groups. (Detailed definitions of internal validity and other validity categories are given in the methods section). When assessing internal validity a differentiation is made between the following factors:
◆ Selection bias: differences between test and control population regarding their structuralcomposition, e.g. in terms of age, gender, duration and severity of illness and others.
◆ Performance bias: differences in the treatment apart from the intervention tested, e.g. more contact, attention or efforts in the verum group.
◆ Detection bias: differences in observation of outcome parameters, e.g. due to inadequate blinding and respective expectations by assessors, due to training effects or others.
◆ Attrition bias: related to differences in dropouts between test and control group.
The goal is to gain the largest possible level of structural, treatment-related and observational similarity between test and control groups through randomisation and blinding, with a subsequent evaluation following the "intention to treat" (ITT) principle [1-3].