Table 3 Summary of key components of a Bayesian monitoring plan

Prior distributions

• Previous studies on the control rate or treatment effect can be used as prior information

• Prior beliefs about the treatment effect should be elicited from experts to inform the strength of the evidence needed to convince them

Two-arm trial of Treatment A versus B (control):

• Evidence from three previous trials on rate of outcome under treatment B: 17 %, 25 %, 30 %

• Evidence from two studies on treatment effect for different population: RR 0.98 (95 % CI: 0.73–1.3); RR 0.75 (95 % CI: 0.56–1.0)

Prior distributions, center (95 % CrI):

• Control rate: 25 % (5–55 %)

• Skeptical prior for treatment effect: RR 1.10 (0.7–2.0)

• Enthusiastic prior for treatment effect: RR 0.85 (0.5–1.0)

Clinically important treatment effect

• Investigators should specify how big a treatment effect needs to be in order to stop a trial and recommend its use or advise against it

• A relative risk reduction of 15 % or more is needed to recommend treatment A, RR < 0.85

• An absolute increase of 2 % in safety outcome would be unacceptable, RD > 0.02

Stopping thresholds

• For each type of monitoring, i.e., safety, efficacy, or futility, the level of confidence to stop the trial early needs to be specified

• For most cases, it should be based on a clinically important effect

• Efficacy: the trial will stop early if the likelihood of seeing a clinically important effect is very large, even under a skeptical prior

• Futility: if the likelihood of a clinically important effect is small even under an enthusiastic prior, the trial would stop early

• Safety: the trial would stop if the probability of increasing harm is large enough under an enthusiastic prior

At any preplanned interim analysis, any of these occurrences would make the DSMC consider stopping the trial:

• Efficacy under skeptical prior:

 Pr(RR < 0.85) > 0.99

• Futility under enthusiastic prior:

 Pr(RR < 0.85) < 0.10

• Safety under enthusiastic prior:

 Pr(RD > 0.02) > 0.70