Modelling of Infectious Disease CEntre
Imperfect Gold Standard Models
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MODEL101 : The 2-tests in 2-populations Model (Simplified Interface)
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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
Burn-in iterations (5000)
MCMC Iterations (20000)
Burn-in 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 Mahidol-Oxford Tropical Medicine Research Unit (MORU) and Faculty of Tropical Medicine, Mahidol University, Thailand.
The content of this site is © 2011 MORU but may be used under the terms of the Creative Commons Attribution Non-Commercial Licence which permits unrestricted use, distribution, and reproduction for non-commercial purposes and provided the original work is properly cited.