Table 4 Detailed features of traditional group sequential and adaptive designs

How was interim analyses planned

Pre-planned

39

90.7%

Pre-planned by DSMB

4

9.3%

Frequency of interim analyses planned

N = 1

9

20.9%

N = 2

16

37.2%

N > 2

18

41.9%

How was the interim analysis set

Time

15

34.9%

Patients

5

11.6%

Events

20

46.5%

NA

3

7%

Pre-planned decision rule

The O’Brien-Fleming group sequential boundaries

3

7%

Haybittle and Peto type of stopping rule

7

16.3%

An alpha spending function (Lan and DeMets): the O’Brien- Fleming-type

10

23.3%

Triangular sequential design (A Wang-Tsiatis group sequential design)

2

4.7%

Asymmetrical group sequential (a Peto-type boundary)

1

2.3%

Others

9

20.9%

NA

11

25.6%

Was the trial stopped early

Yes

29

67.4%

No

14

32.6%

Reason for stopping early

Futility

5

11.6%

Efficacy

14

32.6%

Safety

9

20.9%

Others

1

2.3%

Adaptive designs

Numbers (n = 13)

Percentages

(23.2%)

How was interim analyses planned

Pre-planned

13

100%

Frequency of interim analyses planned

N = 1

9

69.2%

N = 2

2

15.4%

N > 2

2

15.4%

How was the interim analysis set

Time

0

0

Patients

10

76.9%

Events

2

15.4%

NA

1

7.7%

Was the trial stopped early

Yes

7

53.8%

No

6

46.2%

Reason for stopping early

Futility

4

30.8%

Efficacy

2

15.4%

Safety

-

-

Others (slow enrollment)

1

7.7%

Type

Methods

Re-estimation of sample size

Bayesian adaptive approach

2

15.7%

Simulation

2

15.7%

Others

2

15.7%

NA

2

15.7%

Response-adaptive randomization

Bayesian approach

1

7.7%

Dose-Response

Bayesian method

1

7.7%

Enrichment

Bayesian method

1

7.7%

Two-stage adaptive

Simulation used to assess the type I error. A group sequential approach with efficacy boundary based on a gamma (− 10) α spending function.

1

7.7%

Seamless phase IIb/III

Simulation used to assess the type I error. A truncated Levin-Robbins sequential elimination procedure for selecting a right dose.

1

7.7%