Empirical Bayes and Likelihood Inference

Ahmed, S. E.; Reid, N.

doi:10.1007/978-1-4613-0141-7

Cited by 10 publications

(3 citation statements)

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“…CD4 cell count decline during natural history of HIV infection has been used in all previously proposed methods which provide subject-specific estimates of the unknown infection time [8][9][10][11][12][13] but the respective HIV-RNA evolution has only rarely been used 11 . Unlike most of the aforementioned methods, the proposed method does not require data from individuals with known infection times; it only requires the estimates of the fixed and random parameters from the fit of the bivariate model on the CASCADE data, which can be easily shared and are provided in the supplementary appendix.…”

Section: Discussionmentioning

confidence: 99%

“…[2][3][4][5][6][7] . There are though a few studies that have proposed methods through which the gap between HIV infection and diagnosis can be estimated at the individual level [8][9][10][11][12][13] . However, the application setting in most of these studies was not only a cohort comprising individuals with unknown HIV infection time (seroprevalent cases) but also a proportion of individuals with known infection times (seroincident cases).…”

Section: Introductionmentioning

confidence: 99%

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Determining the likely place of HIV acquisition for migrants in Europe combining subject-specific information and biomarkers data

Pantazis

Thomadakis

Amo

et al. 2017

Stat Methods Med Res

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In most HIV-positive individuals, infection time is only known to lie between the time an individual started being at risk for HIV and diagnosis time. However, a more accurate estimate of infection time is very important in certain cases. For example, one of the objectives of the Advancing Migrant Access to Health Services in Europe (aMASE) study was to determine if HIV-positive migrants, diagnosed in Europe, were infected pre- or post-migration. We propose a method to derive subject-specific estimates of unknown infection times using information from HIV biomarkers' measurements, demographic, clinical, and behavioral data. We assume that CD4 cell count (CD4) and HIV-RNA viral load trends after HIV infection follow a bivariate linear mixed model. Using post-diagnosis CD4 and viral load measurements and applying the Bayes' rule, we derived the posterior distribution of the HIV infection time, whereas the prior distribution was informed by AIDS status at diagnosis and behavioral data. Parameters of the CD4-viral load and time-to-AIDS models were estimated using data from a large study of individuals with known HIV infection times (CASCADE). Simulations showed substantial predictive ability (e.g. 84% of the infections were correctly classified as pre- or post-migration). Application to the aMASE study ( n = 2009) showed that 47% of African migrants and 67% to 72% of migrants from other regions were most likely infected post-migration. Applying a Bayesian method based on bivariate modeling of CD4 and viral load, and subject-specific information, we found that the majority of HIV-positive migrants in aMASE were most likely infected after their migration to Europe.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determining the likely place of HIV acquisition for migrants in Europe combining subject-specific information and biomarkers data

Pantazis

Thomadakis

Amo

et al. 2017

Stat Methods Med Res

View full text Add to dashboard Cite

show abstract

“…In the following, a few applied statistical methods that are used throughout this thesis will be introduced and motivated; a detailed derivation and explanation can be found in textbooks such as Refs. [27][28][29] , whose parts of this chapter are based on.…”

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confidence: 99%