Modelling of Infectious Disease CEntre
Imperfect Gold Standard Models
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MODEL102 : The 2tests in 2populations Model
(Advanced Interface)
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General Description

Model Designation

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Publication and Other Information
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Data input
Test A
Test B
Population 1
Population 2
Positive
Negative
Positive
Negative
Positive
Negative
Prior distributions
(Using example value)
Parameters
Options
Values
Prevalence of population 1
Default
Adjust manually
Beta distribution
Beta distribution (0.5,0.5)
Range (0,1)
Beta distribution
Range
(
,
)
(
,
)
Fix the value
(
)
Prevalence of population 2
Default
Adjust manually
Beta distribution
Beta distribution (0.5,0.5)
Range (0,1)
Beta distribution
Range
(
,
)
(
,
)
Fix the value
(
)
Sensitivity of test A
Default
Adjust manually
Beta distribution
Fix the value
Beta distribution (0.5,0.5)
Range (0,1)
Beta distribution
Range
(
,
)
(
,
)
Fix the value
(
)
Sensitivity of test B
Default
Adjust manually
Beta distribution
Fix the value
Beta distribution (0.5,0.5)
Range (0,1)
Beta distribution
Range
(
,
)
(
,
)
Fix the value
(
)
Specificity of test A
Default
Adjust manually
Beta distribution
Fix the value
Beta distribution (0.5,0.5)
Range (0.4,1)
Beta distribution
Range
(
,
)
(
,
)
Fix the value
(
)
Specificity of test B
Default
Adjust manually
Beta distribution
Fix the value
Beta distribution (0.5,0.5)
Range (0.4,1)
Beta distribution
Range
(
,
)
(
,
)
Fix the value
(
)
Initial values
(Using example value)
Note!
The
prevalance of test A
is set to a fixed value in the above section; therefore, its initial will not be taken into account during Bayesian LCM analysis.
The
prevalance of test B
is set to a fixed value in the above section; therefore, its initial will not be taken into account during Bayesian LCM analysis.
The
sensitivity of test A
is set to a fixed value in the above section; therefore, its initial will not be taken into account during Bayesian LCM analysis.
The
sensitivity of test B
is set to a fixed value in the above section; therefore, its initial will not be taken into account during Bayesian LCM analysis.
The
specificity of test A
is set to a fixed value in the above section; therefore, its initial will not be taken into account during Bayesian LCM analysis.
The
specificity of test B
is set to a fixed value in the above section; therefore, its initial will not be taken into account during Bayesian LCM analysis.
Parameters
Options
Values
Prevalence of population 1
Default
Adjust manually
Initial 1 (0.9)
Initial 2 (0.3)
Initial 1
Initial 2
(
)
(
)
Prevalence of population 2
Default
Adjust manually
Initial 1 (0.9)
Initial 2 (0.3)
Initial 1
Initial 2
(
)
(
)
Sensitivity of test A
Default
Adjust manually
Initial 1 (0.9)
Initial 2 (0.7)
Initial 1
Initial 2
(
)
(
)
Sensitivity of test B
Default
Adjust manually
Initial 1 (0.9)
Initial 2 (0.7)
Initial 1
Initial 2
(
)
(
)
Specificity of test A
Default
Adjust manually
Initial 1 (0.9)
Initial 2 (0.99)
Initial 1
Initial 2
(
)
(
)
Specificity of test B
Default
Adjust manually
Initial 1 (0.9)
Initial 2 (0.99)
Initial 1
Initial 2
(
)
(
)
Settings
(Using example value)
Parameters
Options
Values
Iterations
Default
Adjust manually
Burnin iterations (5000)
MCMC Iterations (20000)
Burnin iterations
MCMC Iterations
(
)
(
)
Thin
Default
Adjust manually
thin (10)
thin
(
)
Conventional method to be compared with the imperfect gold standard model
(Using example value)
Test A was assumed as a perfect gold standard
Test B was assumed as a perfect gold standard
Positive results of either test A or B was assumed as a perfect gold standard
Positive results of both test A and B was assumed as a perfect gold standard
MICE is funded by Li Ka Shing and Wellcome trust, and initiated under collaboration between MahidolOxford Tropical Medicine Research Unit (MORU) and Faculty of Tropical Medicine, Mahidol University, Thailand.
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