Disease Diagnosis from Immunoassays with Plate to Plate Variability: A Hierarchical Bayesian Approach

被引：2

作者：

A. Entine O. ^{[1
]}

Small D.S. ^{[2
]}

Jensen S.T. ^{[2
]}

Sanchez G. ^{[3
]}

Bastos M. ^{[3
]}

R. Verastegui M. ^{[3
]}

Levy M.Z. ^{[4
]}

机构：

[1] 250 West 50th St, Apt 21P, New York, 10019, NY

[2] Department of Statistics, The Wharton School of the University of Pennsylvania, 3730 Walnut St., Philadelphia, 19104, PA

[3] Laboratorio de Investigacion en Enfermedades Infecciosas, Facultad de Ciencias Y filosofia Alberto Cazorla Talleri, Universidad Peruana Cayetano Heredia, Av. Honorio Delgado 430, Lima

[4] Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, 423 Guardian Dr., Philadelphia, 19104, PA

来源：

Statistics in Biosciences | 2015年 / 7卷 / 2期

关键词：

Posterior Distribution; Markov Chain Monte Carlo; Conditional Posterior Distribution; Probability Cutoff; Markov Chain Monte Carlo Procedure;

D O I：

10.1007/s12561-014-9113-5

中图分类号：

学科分类号：

摘要：

The standard methods of diagnosing disease based on antibody microtiter plates are quite crude. Few methods create a rigorous underlying model for the antibody levels of populations consisting of a mixture of positive and negative subjects, and fewer make full use of the entirety of the available data for diagnoses. In this paper, we propose a Bayesian hierarchical model that provides a systematic way of pooling data across different plates, and accounts for the subtle sources of variations that occur in the optical densities of typical microtiter data. In addition to our Bayesian method having good frequentist properties, we find that our method outperforms one of the standard crude approaches (the “3 SD Rule”) under reasonable assumptions, and provides more accurate disease diagnoses in terms of both sensitivity and specificity. © 2014, International Chinese Statistical Association.

引用

页码：206 / 224

页数：18

共 21 条

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