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Table 2 Prevalence of events assumed in each trial, according to the scenario and to the application data set, and weight and maximal rates of misclassifications assigned to each event to build the decision rule

From: A Bayesian non-inferiority approach using experts’ margin elicitation – application to the monitoring of safety events

  

Event

  

Death

IVH [1]

NEC [2]

Retinopathy

Simulation study

     

All scenarios

Prevalence in FD arm

0.39

0.15

0.06

0.04

Scenario A

Prevalence in HD arm

0.39

0.15

0.06

0.04

 

Good decision [3]

Acc

Acc

Acc

Acc

Scenario B

Prevalence in HD arm

0.26

0.10

0.04

0.03

 

Good decision [3]

Acc

Acc

Acc

Acc

Scenario C

Prevalence in HD arm

0.58

0.23

0.09

0.06

 

Good decision [3]

U

U

Acc

Acc

Scenario D

Prevalence in HD arm

0.78

0.30

0.12

0.08

 

Good decision [3]

U

U

U

U

Scenario E

Prevalence in HD arm

1.00

0.45

0.18

0.12

 

Good decision [3]

U

U

U

U

Weight[4]

100

88

70

60

Maximal misclassifications rates

    

Class a misclassifications [5]

0.10

0.10

0.10

0.10

Class b misclassifications [6]

0.10

0.16

0.25

0.30

Data set for application

    

Prevalence in FD arm

0.39

0.15

0.06

0.04

Prevalence in HD arm

0.47

0.23

0.12

0.08

Good decision [3]

U

U

U

U

  1. HD arm: half-dose arm; FD arm: full-dose arm
  2. 1IVH: Intraventricular haemorrhage
  3. 2NEC: Necrotizing enterocolitis
  4. 3Good decision= What have been considered as good decision for each scenario and event: “Acc” if the difference of prevalence of events is Acceptable, “U” if the difference is Unacceptable
  5. 4Weight = Relative severity of the event according to the experts
  6. 5Class a misclassifications rate: Trials that conclude that the difference between arms is Unacceptable, among trials with acceptable difference
  7. 6Class b misclassifications rate: Trials that conclude that the difference between arms is Acceptable, among trials with unacceptable difference