In Randomized Controlled Trials, subjects are randomized to an intervention group or a control group. In this domain, SR project teams need to appraise the randomization process used by the authors of each RCT study included in the review. Some examples of randomization include: 

• computer-generated random numbers
• reference to a random number table
• shuffling cards or envelopes
• coin tossing
• throwing dice
• drawing lots

If the study's randomization process was based on birth or admission dates, patient record numbers, or clinician or participant decisions for instance, these don't constitute BLIND and RANDOM methods and, therefore, contribute to the risk of bias embedded in the study's methodology. 

This step involves assessing how RCT study researchers assigned the intervention to participants.

• Were participants and / or people delivering the intervention aware of participants' assigned intervention during the trial?
• Was there an imbalance between intervention / control groups (demographics, health history, etc.)?
• Did the researchers have an interest in the effect of adhering to the intervention? In other words, was the study funded by a corporation, manufacturer, or other organization that would benefit from favorable results? 

If the answer is YES (or even Probably Yes) to any of these, there may be a risk of bias embedded in the methodology of this study. In simple terms, this domain asks, "Did participants actually receive the treatment they were supposed to receive, and was anyone's knowledge about the treatment (or lack thereof) likely to have influenced the results?"

Bias due to missing outcome data would be evident if:

• Data was not available for all, or nearly all randomized participants
• The reason for missing data is not included in the study*
• Missing data is more evident in the intervention vs control group (or vice versa)
• Researchers didn't account for missing data

*Or if the reason is included, but it's not justified. For example, if participants drop out of the study because the treatment isn't working, and authors only analyze the data from participants who stayed, the results will look artificially good for that treatment.

When making judgements about whether measurement methodology includes bias or not, consider these questions: 

• Was the outcome measured fairly and consistently across all groups? 
  • In a pain study, for instance, are participants asked leading questions that may influence responses? Are the intervention and control groups asked the same questions in the same manor? 
• Were the people measuring the outcome blinded to which participants got the intervention and control? 
  • Does the person assessing the outcome know which treatment a participant received? If so, their knowledge (conscious or unconscious) could influence how they measure or interpret the results.
• Was the method of measuring the outcome appropriate? 
  • If the outcome is subjective (like pain), the risk of bias from unblinded assessors is much higher.
  • Were the tools or methods used to measure the outcome validated and reliable?

Remember, if the way a study collects the results (or outcomes) systematically favored one treatment over another, it could have lead to an inaccurate conclusion. 

In plain language, this domain asks, "Did the researchers choose to present only the 'good' results while ignoring other less favorable ones?"

The study's protocol is an important factor in this domain. Was if publicly registered before starting the study. This will help to see if the researchers stuck to their original intentions. If not, this should raise some concerns about the intentions of the study and the researchers. 

If a study appears to have selectively reported its results, it creates a risk that the conclusions are biased because only a partial or favorable view of the evidence is being presented