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Table 3 Summary of informative prior distribution inputs in models for detecting AD and MCI

From: Bayesian estimation for the accuracy of three neuropsychological tests in detecting Alzheimer's disease and mild cognitive impairment: a retrospective analysis of the ADNI database

Parameters

Prior distributions

For AD detection

For MCI detection

Prevalence

Beta (2.819, 16.021)

Beta (25.422, 281.851)

Sensitivity

 MoCA

Beta (270.154,20.020)

Beta (824.530, 146.329)

 MMSE

Beta (464.322,50.770)

Beta (269.497,110.668)

 ADAS-cog

Beta (125.247, 13.288)

Beta (35.209, 9.552)

Specificity

 MoCA

Beta (192.121,22.472)

Beta (440.462,117.819)

 MMSE

Beta (514.797, 69.080)

Beta (469.554,192.381)

 ADAS-cog

Beta (113.235,9.448)

Beta (56.016,11.479)

Covariancesa

Cd ~ dunif(−1, 1)

 

Cn ~ dunif(−1, 1)

  1. a Cd and Cn refer to the co-variances explaining the conditional dependence between MoCA and MMSE in detecting AD and MCI patients
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European Journal of Medical Research

ISSN: 2047-783X

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