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Table 2 Simulation results: estimations of bias and mean squared error for the exponential model

From: Estimating time-to-onset of adverse drug reactions from spontaneous reporting databases

    

Naive estimator

 

TBE

 

λ

p

n

BIAS( λ ̂ )

MSE( λ ̂ )

BIAS( λ ̂ )

MSE( λ ̂ )

NPM

0.05

0.25

100

0.498

0.250

0.030

0.005

224

  

500

0.498

0.248

0.007

0.001

79

0.05

0.50

100

0.195

0.038

0.008

0.001

85

  

500

0.193

0.037

<0.001

<0.001

1

0.05

0.80

100

0.073

0.005

<0.001

<0.001

2

  

500

0.072

0.005

<0.001

<0.001

0

1

0.25

100

10.06

102

0.462

2.17

72

  

500

9.95

99

0.046

0.48

10

1

0.50

100

3.91

15.4

0.126

0.49

29

  

500

3.86

14.9

-0.022

0.12

0

1

0.80

100

1.45

2.16

0.004

0.11

0

  

500

1.45

2.11

0.004

0.02

0

  1. The mean squared error formula is MSE( λ ̂ )=Var( λ ̂ )+ ( BIAS ( λ ̂ ) ) 2 . Calculations were made on the replications where there was no problem of maximization. In the last column appear the number of problems of maximization for the truncation-based approach. There was no problem of maximization for the naive approach. Abbreviations: TBE truncation-based estimator, MSE mean squared error, NPM number of maximization problems.