Estimating the distribution of the window period for recent HIV infections: A comparison of statistical methods

Sweeting, Michael; Angelis, Daniela De; Parry, John; Suligoi, Barbara

doi:10.1002/sim.3941

Cited by 21 publications

(38 citation statements)

References 22 publications

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“…The precision and accuracy of methods based on ‘snapshot’, or cross-sectional sampling, to estimate HIV incidence, rely on the characteristics of the antibody response soon after infection [4–7] and are being continuously developed to identify recent infection [8]. …”

Section: Introductionmentioning

confidence: 99%

Mixture models for calibrating the BED for HIV incidence testing

Mahiane

Fiamma

Auvert

2013

Statistics in Medicine

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A number of antibody biomarkers have been developed to distinguish between recent and established Human Immunodeficiency Virus (HIV) infection and used for HIV incidence estimation from cross-sectional specimens. In general, a cut-off value is specified and estimates of the following parameters are needed: a) the mean time interval (w) between seroconversion and reaching that cut-off, b) the probability of correctly identifying individuals who became infected in the last w years (sensitivity) and c) the probability of correctly identifying individuals who have been infected for more than w years (specificity). We develop two statistical methods to study the distribution of a biomarker and derive a formula for estimating HIV incidence from a cross sectional survey. Both methods allow handling interval censored data and basically consist of using a generalised mixture model to model the growth of the biomarker as a function of time since infection. The first uses data from all followed-up individuals and allows incidence estimation in the cohort while the second only uses data from seroconverters. We illustrate our methods using repeated measures of the IgG capture BED enzyme immunoassay. Estimates of calibration parameters i.e. mean window period, mean recency period, sensitivity and specificities obtained from both models are comparable. The formula derived for incidence estimation gives the maximum likelihood estimate of incidence which, for a given window period, depends only on sensitivity and specificity. The optimal choice of the window period is discussed. Numerical simulations suggest that data from seroconverters can provide reasonable estimates of the calibration parameters.

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Section: Introductionmentioning

confidence: 99%

Mixture models for calibrating the BED for HIV incidence testing

Mahiane

Fiamma

Auvert

2013

Statistics in Medicine

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“…Sweating et al have described two further statistical approaches to estimate this period for the AxSym avidity assay. Their method accounts for the interval-censored nature of both the date of seroconversion and the date of crossing a specific threshold [70].…”

Section: Local Assay Validation Estimating Frrs and Mean Duration Ofmentioning

confidence: 99%

Recent infection testing algorithm (RITA) applied to new HIV diagnoses in England, Wales and Northern Ireland, 2009 to 2011

Aghaizu

Murphy²,

Tosswill³

et al. 2014

Eurosurveillance

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“…Regardless of whether the unknown infection times are handled simplistically (using a proxy expected infection time) or formally accommodated (Brookmeyer, Konikoff & Laeyendecker 2013;Mahiane, Fiamma & Auvert 2014;Sommen, Commenges & Le Vu 2011;Sweeting, De Angelis & Parry 2010), incorrect assumptions about the underlying process will lead to bias. The assumptions about, and flexibilities allowed in, infection times also interact with other aspects of the estimation, such as the assumed form of the biomarker signal, to determine the overall MDRI bias.…”

Section: Discussionmentioning

confidence: 99%

“…The Bayesian implementation of the mixed models (Methods 18-23) makes use of the MCMC approach provided by WinBUGS to derive the posterior distribution of the unknown parameters, as described elsewhere (Sweeting, De Angelis & Parry 2010). The models use either a midpoint infection time or a uniform prior distribution for infection times.…”

Section: Mdri Estimation Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Cross-Sectional HIV Incidence Surveillance: A Benchmarking of Approaches for Estimating the ‘Mean Duration of Recent Infection’

Kassanjee

Angelis

Farah

et al. 2017

Statistical Communications in Infectious Diseases

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Abstract:The application of biomarkers for 'recent' infection in cross-sectional HIV incidence surveillance requires the estimation of critical biomarker characteristics. Various approaches have been employed for using longitudinal data to estimate the Mean Duration of Recent Infection (MDRI) -the average time in the 'recent' state. In this systematic benchmarking of MDRI estimation approaches, a simulation platform was used to measure accuracy and precision of over twenty approaches, in thirty scenarios capturing various study designs, subject behaviors and test dynamics that may be encountered in practice. Results highlight that assuming a single continuous sojourn in the 'recent' state can produce substantial bias. Simple interpolation provides useful MDRI estimates provided subjects are tested at regular intervals. Regression performs the best -while 'random effects' describe the subject-clustering in the data, regression models without random effects proved easy to implement, stable, and of similar accuracy in scenarios considered; robustness to parametric assumptions was improved by regressing 'recent'/'non-recent' classifications rather than continuous biomarker readings. All approaches were vulnerable to incorrect assumptions about subjects' (unobserved) infection times. Results provided show the relationships between MDRI estimation performance and the number of subjects, inter-visit intervals, missed visits, loss to follow-up, and aspects of biomarker signal and noise.

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Estimating the distribution of the window period for recent HIV infections: A comparison of statistical methods

Cited by 21 publications

References 22 publications

Mixture models for calibrating the BED for HIV incidence testing

Mixture models for calibrating the BED for HIV incidence testing

Recent infection testing algorithm (RITA) applied to new HIV diagnoses in England, Wales and Northern Ireland, 2009 to 2011

Cross-Sectional HIV Incidence Surveillance: A Benchmarking of Approaches for Estimating the ‘Mean Duration of Recent Infection’

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