Bias Bee - version RCT-tool to assess bias in RCTs

Example

In a trial that randomly allocates participants into two groups for a drug trial, Bias Bee checks if the allocation was genuinely random and if baseline characteristics between groups are balanced.

Scenario

A study evaluating a new diabetes drug uses random numbers to assign participants to either the experimental or control group. Bias Bee assesses whether the sequence generation and concealment minimized bias and if any baseline differences suggest issues with randomization.

Example

In an open-label trial where participants know their treatment, Bias Bee checks if participants changed behavior due to knowledge of their assignment, such as seeking additional treatments outside the study protocol.

Scenario

A study on physical therapy for back pain does not blind participants. Bias Bee analyzes if knowing their intervention led some participants to engage in extra exercises, creating bias due to deviations from the protocol.

Example

In a long-term study, many participants drop out before the final follow-up. Bias Bee assesses whether the missing data likely influenced the trial’s outcome and if any biases resulted from it.

Scenario

A weight-loss intervention study with a 12-month follow-up loses 20% of participants due to dropouts. Bias Bee examines if these missing data are related to the participants' health status, and whether this affects the trial’s findings.