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Table 2 Percent absolute relative bias (RB) and mean squared error (MSE) for computer simulation study

From: Combining population-based administrative health records and electronic medical records for disease surveillance

\( {prev}_{Y_1}, \) \( {prev}_{Y_2} \)

\( {\overline{\rho}}_x \)

RB; prev T = 20%

\( {\boldsymbol{\rho}}_{{\boldsymbol{Y}}_{\mathbf{1}}{\boldsymbol{Y}}_{\mathbf{2}}} \) = 0.85

\( {\boldsymbol{\rho}}_{{\boldsymbol{Y}}_{\mathbf{1}}{\boldsymbol{Y}}_{\mathbf{2}}} \) = 0.65

OR

AND

RSSA

PSSA (16)

PSSA (8)

OR

AND

RSSA

PSSA (16)

PSSA (8)

18, 15%

0.00

9.5

47.5

7.5

9.5

11.3

23.1

59.4

1.3

48.3

49.5

0.20

9.0

47.7

7.9

2.1

7.4

22.9

59.4

1.5

41.7

54.3

0.50

10.1

47.2

7.0

24.3

21.1

23.7

59.1

0.9

99.0

78.6

18, 10%

0.00

0.3

58.6

18.2

1.1

3.0

10.8

67.1

12.8

28.9

37.5

0.20

0.9

58.9

18.7

5.9

5.9

10.5

67.2

13.0

31.1

54.3

0.50

0.2

58.3

17.7

48.8

26.1

11.2

66.8

12.4

108.8

90.1

15, 15%

0.00

4.1

49.2

11.5

3.7

4.3

17.6

61.7

5.8

41.3

42.5

0.20

3.6

49.4

12.0

3.1

3.6

17.2

61.9

6.1

37.6

50.1

0.50

4.8

48.7

10.9

20.5

12.5

18.1

61.5

5.3

102.0

70.7

\( {prev}_{Y_1}, \) \( {prev}_{Y_2} \)

\( {\overline{\rho}}_x \)

MSE; prev T = 20%

\( {\boldsymbol{\rho}}_{{\boldsymbol{Y}}_{\mathbf{1}}{\boldsymbol{Y}}_{\mathbf{2}}} \) = 0.85

\( {\boldsymbol{\rho}}_{{\boldsymbol{Y}}_{\mathbf{1}}{\boldsymbol{Y}}_{\mathbf{2}}} \) = 0.65

OR

AND

RSSA

PSSA (16)

PSSA (8)

OR

AND

RSSA

PSSA (16)

PSSA (8)

18, 15%

0.00

0.04

0.90

0.02

0.06

0.47

0.22

1.41

< 0.01

0.99

1.76

0.20

0.03

0.91

0.03

0.02

0.52

0.21

1.41

< 0.01

0.82

2.25

0.50

0.04

0.89

0.02

1.06

1.47

0.23

1.40

0.00

4.68

4.46

18, 10%

0.00

< 0.01

1.37

0.13

0.02

0.69

0.05

1.80

0.07

0.40

1.70

0.20

< 0.01

1.39

0.14

0.06

1.16

0.05

1.80

0.07

0.70

3.48

0.50

< 0.01

1.36

0.13

2.28

2.32

0.05

1.79

0.06

5.36

6.16

15, 15%

0.00

0.01

0.97

0.05

0.03

0.46

0.13

1.53

0.01

0.74

1.62

0.20

0.01

0.98

0.06

0.02

0.72

0.12

1.53

0.02

0.74

2.20

0.50

0.01

0.95

0.05

1.03

1.29

0.13

1.51

0.01

4.84

3.96

\( {prev}_{Y_1}, \) \( {prev}_{Y_2} \)

\( {\overline{\rho}}_x \)

RB; prev T = 10%

\( {\boldsymbol{\rho}}_{{\boldsymbol{Y}}_{\mathbf{1}}{\boldsymbol{Y}}_{\mathbf{2}}} \) = 0.85

\( {\boldsymbol{\rho}}_{{\boldsymbol{Y}}_{\mathbf{1}}{\boldsymbol{Y}}_{\mathbf{2}}} \) = 0.65

OR

AND

RSSA

PSSA (16)

PSSA (8)

OR

AND

RSSA

PSSA (16)

PSSA (8)

8, 7%

0.00

11.5

55.8

8.4

9.7

35.0

29.6

67.1

1.1

76.2

154.9

0.20

10.5

56.1

9.2

42.6

37.8

28.7

67.4

0.3

196.7

217.1

0.50

13.1

54.9

7.1

216.1

43.3

30.7

66.6

2.0

307.6

286.5

8, 5%

0.00

2.9

59.7

16.0

1.1

50.5

12.2

73.4

13.5

45.3

114.9

0.20

3.2

59.7

15.8

10.7

85.1

12.0

73.9

13.8

235.2

273.4

0.50

3.8

59.2

15.3

230.5

198.2

14.2

73.3

12.1

322.2

334.8

5, 5%

0.00

14.7

70.0

30.9

6.3

92.1

7.4

78.7

28.4

61.0

193.0

0.20

15.4

70.2

31.5

134.4

149.1

8.0

78.7

28.8

271.0

217.9

0.50

13.6

69.5

30.0

275.7

222.1

6.3

78.2

27.4

333.8

375.0

\( {prev}_{Y_1}, \) \( {prev}_{Y_2} \)

\( {\overline{\rho}}_x \)

MSE; prev T = 10%

\( {\boldsymbol{\rho}}_{{\boldsymbol{Y}}_{\mathbf{1}}{\boldsymbol{Y}}_{\mathbf{2}}} \) = 0.85

\( {\boldsymbol{\rho}}_{{\boldsymbol{Y}}_{\mathbf{1}}{\boldsymbol{Y}}_{\mathbf{2}}} \) = 0.65

OR

AND

RSSA

PSSA (16)

PSSA (8)

OR

AND

RSSA

PSSA (16)

PSSA (8)

8, 7%

0.00

0.01

0.31

0.01

0.28

2.01

0.09

0.45

< 0.01

0.87

6.33

0.20

0.01

0.31

0.01

1.29

1.31

0.08

0.45

< 0.01

5.19

8.53

0.50

0.02

0.30

0.01

5.60

6.72

0.10

0.44

< 0.01

9.97

12.77

8, 5%

0.00

< 0.01

0.36

0.03

0.12

2.78

0.02

0.54

0.02

0.28

4.31

0.20

< 0.01

0.36

0.03

0.59

3.59

0.02

0.55

0.02

7.28

12.63

0.50

< 0.01

0.35

0.02

6.39

8.45

0.02

0.54

0.02

10.91

16.73

5, 5%

0.00

0.02

0.49

0.10

0.57

6.96

0.01

0.62

0.08

1.92

9.37

0.20

0.02

0.49

0.10

4.82

8.11

0.01

0.62

0.08

9.11

9.70

0.50

0.02

0.48

0.09

8.71

10.08

< 0.01

0.61

0.08

11.79

18.41

  1. OR Rule-based OR method, AND Rule-based AND method, RSSA Rule-based sensitivity-specificity adjusted method, PSSA Probabilistic-based sensitivity-specificity adjusted; prevT denotes true population prevalence; \( {prev}_{Y_1}, \) \( {prev}_{Y_2} \) denotes outcome prevalence; \( {\rho}_{Y_1{Y}_2} \) denotes correlation between data sources; \( {\overline{\rho}}_x \) denotes average correlation amongst disease markers using the exchangeable correlation pattern. * in PSSA(*) denotes the number of model markers (i.e., covariates) for PSSA method; we multiplied each MSE value by 100; The bolded simulation condition are consistent with the conditions observed for our numeric example of hypertension