Table 1 Definitions of four main forms of interim result measures

Interim Control Event Rate (IControlER)

The number of events observed among control participants at some planned interim point into the trial divided by number of control participants admitted at that same planned interim point (e.g., a planned interim point can be 6 months from the start of the trial)

Example:

• Total number of deaths in the placebo group, 6 months from the start of the trial = 15

• Total number of participants in the placebo group, 6 months from the start of the trial = 250

• Calculation: 15/250 = 0.06 or 6%

Therefore, the Interim Control Event Rate at the trial’s interim analysis, 6 months from the start of the trial, is 6%

Interim Combined Event Rate (ICombinedER)

“The total number of events observed at some planned interim point into the trial divided by the total number of participants admitted at that same planned interim point (e.g., a planned interim point can be 6 months from the start of the trial or after enrolling a certain number of participants)

Example:

• Total number of deaths in both the placebo group and new intervention group, 6 months from the start of the trial = 80

• Total number of participants in both the placebo group and the new intervention group, 6 months from the start of the trial = 700

• Calculation: 80/700 = 0.114 or 11.4%

Therefore, the Interim Combined Event Rate at the trial’s interim analysis, 6 months from the start of the trial, is 11.4%.” [8]

Adaptive Conditional Power (ACP)

“The probability of rejecting the null hypothesis of no effect by the end of the trial (i.e., finding a statistically significant effect in favor of the intervention), at some predetermined interim point in the trial when the adaptive conditional power is scheduled to be calculated. The assumption made is that the observed interim effect (i.e., relative risk reduction) in the trial will remain the same until the end of the trial

Example statement:

Given the interim data (data collected 2 years into the trial that is planned to last for 3 years), and assuming the observed interim effect (i.e., relative risk reduction) at the 2-year point to be the true effect for the remainder of the trial, the probability of rejecting the null hypothesis of no effect (i.e., finding a statistically significant effect in favor of the intervention) at the end of the trial is 60%.

The following pieces of information are used to calculate Adaptive Conditional Power at trial interim:

• Control event rate and experimental event rate

• Information Fraction; a ratio of the planned sample size and the number of patients recruited in the trial at the interim analysis

• Z score and B value at interim

• Drift parameter.” [8]

Unconditional Conditional Power (UCP)

“The probability of correctly rejecting the null hypothesis of no effect at the end of the trial (i.e., finding a statistically significant effect in favor of the intervention) and accepting the alternative hypothesis when indeed the alternative hypothesis is true, at some interim point in the trial

The following pieces of information are used to calculate Unconditional Conditional Power at interim:

1. The hypothesized treatment effect at the design stage (i.e., relative risk reduction) of the trial, assuming the hypothesized treatment effect at the design stage to be true and correct for the remainder of the trial

2. The sample size calculated at the design stage for the trial and

3. The combined event rate calculated at the trial’s interim, assuming this rate to be true for the remainder of the trial

Example statement:

Given the interim combined event rate and assuming the treatment effect (i.e., relative risk reduction) hypothesized at the design stage of the trial to be true for the remainder of the trial, the probability of correctly rejecting the null hypothesis of no effect (i.e., finding a statistically significant effect in favor of the intervention) at the end of the trial is 89%.” [8]