Blinding in Clinical Trials
3. What Are The Potential Sources Of Bias in a Trial and Who Can And Should Be Blinded?
The relevance of blinding in a randomised clinical trial will vary according to circumstances.
- The trial participant
Blinding participants to the treatment they have received is particularly important when the response criteria are subjective, such as alleviation of pain, but less important for objective criteria, such as disease progression (e.g. cancer). If participants are not blinded, knowledge of group assignment may affect their behaviour in the trial and their responses to subjective outcome measures. For example, a participant who is aware that he is not receiving active treatment may be less likely to comply with the trial protocol. Those aware that they are receiving or not receiving therapy are more likely to provide biased assessments of the effectiveness of the intervention — most likely in opposite directions — than blinded participants.
- Clinical staff administering treatment
Similarly, medical staff and clinicians caring for patients should be blinded to treatment allocation to minimise possible bias in patient management and in assessing disease status. Blinded clinicians are much less likely to transfer their attitudes to participants or to provide differential treatment to the active and control (placebo) groups than are unblinded clinicians.
- The doctor assessing treatment
Blinding of data collectors and outcome adjudicators (sometimes the same individuals) is crucial to ensure unbiased ascertainment of outcomes. For example, in a randomised controlled trial in patients, neither active treatment regimen (tested medication vs. comparator) was superior to placebo when assessed by blinded specialists, but there was an apparent benefit of treatment with the test medication, when unblinded specialists performed the assessments.
- The team interpreting results
Bias may also be introduced during the statistical analysis of the trial through the selective use and reporting of statistical tests. This may be a subconscious process spurred by investigators eager to see a positive result, but the consequences are profound. The best method to avoid this potential bias is blinding of the data analyst until the entire analysis has been completed.