Regression with Interval‐Censored Covariates: Application to Cross‐Sectional Incidence Estimation

Biometrics ◽  
2021 ◽  
Author(s):  
Doug Morrison ◽  
Oliver Laeyendecker ◽  
Ron Brookmeyer
Keyword(s):  
Cross Sectional ◽  
Incidence Estimation ◽  
Interval Censored

scholarly journals Evaluation of multi-assay algorithms for identifying individuals with recent HIV infection: HPTN 071 (PopART)

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0258644
Author(s):  
Wendy Grant-McAuley ◽  
Ethan Klock ◽  
Oliver Laeyendecker ◽  
Estelle Piwowar-Manning ◽  
Ethan Wilson ◽  
...  
Keyword(s):  
Viral Suppression ◽  
Point Of Care ◽  
Population Level ◽  
Hiv Viral Load ◽  
Cross Sectional ◽  
Individual Level ◽  
Viral Loads ◽  
Incidence Estimation ◽  
Duration Of Infection ◽  
Recent Hiv Infection

Background Assays and multi-assay algorithms (MAAs) have been developed for population-level cross-sectional HIV incidence estimation. These algorithms use a combination of serologic and/or non-serologic biomarkers to assess the duration of infection. We evaluated the performance of four MAAs for individual-level recency assessments. Methods Samples were obtained from 220 seroconverters (infected <1 year) and 4,396 non-seroconverters (infected >1 year) enrolled in an HIV prevention trial (HPTN 071 [PopART]); 28.6% of the seroconverters and 73.4% of the non-seroconverters had HIV viral loads ≤400 copies/mL. Samples were tested with two laboratory-based assays (LAg-Avidity, JHU BioRad-Avidity) and a point-of-care assay (rapid LAg). The four MAAs included different combinations of these assays and HIV viral load. Seroconverters on antiretroviral treatment (ART) were identified using a qualitative multi-drug assay. Results The MAAs identified between 54 and 100 (25% to 46%) of the seroconverters as recently-infected. The false recent rate of the MAAs for infections >2 years duration ranged from 0.2%-1.3%. The MAAs classified different overlapping groups of individuals as recent vs. non-recent. Only 32 (15%) of the 220 seroconverters were classified as recent by all four MAAs. Viral suppression impacted the performance of the two LAg-based assays. LAg-Avidity assay values were also lower for seroconverters who were virally suppressed on ART compared to those with natural viral suppression. Conclusions The four MAAs evaluated varied in sensitivity and specificity for identifying persons infected <1 year as recently infected and classified different groups of seroconverters as recently infected. Sensitivity was low for all four MAAs. These performance issues should be considered if these methods are used for individual-level recency assessments.

scholarly journals Cross‐sectional HIV incidence estimation in an evolving epidemic

2019 ◽  
Author(s):  
Doug Morrison ◽  
Oliver Laeyendecker ◽  
Ron Brookmeyer
Keyword(s):  
Hiv Incidence ◽  
Cross Sectional ◽  
Incidence Estimation

scholarly journals Cross-Sectional HIV Incidence Estimation with Missing Biomarkers

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Doug Morrison ◽  
Oliver Laeyendecker ◽  
Jacob Konikoff ◽  
Ron Brookmeyer
Keyword(s):  
Missing Data ◽  
Estimation Methods ◽  
Hiv Incidence ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Window Period ◽  
Subtype B ◽  
Incidence Estimation ◽  
Multiple Biomarkers ◽  
The Mean

Abstract Considerable progress has been made in the development of approaches for HIV incidence estimation based on a cross-sectional survey for biomarkers of recent infection. Multiple biomarkers when used in combination can increase the precision of cross-sectional HIV incidence estimates. Multi-assay algorithms (MAAs) for cross-sectional HIV incidence estimation are hierarchical stepwise algorithms for testing the biological samples with multiple biomarkers. The objective of this paper is to consider some of the statistical challenges for addressing the problem of missing biomarkers in such testing algorithms. We consider several methods for handling missing biomarkers for (1) estimating the mean window period, and (2) estimating HIV incidence from a cross sectional survey once the mean window period has been determined. We develop a conditional estimation approach for addressing the missing data challenges and compare that method with two naïve approaches. Using MAAs developed for HIV subtype B, we evaluate the methods by simulation. We show that the two naïve estimation methods lead to biased results in most of the missing data scenarios considered. The proposed conditional approach protects against bias in all of the scenarios.

Incidence estimation using a single cross-sectional age-specific prevalence survey with differential mortality

2013 ◽  
Vol 33 (3) ◽  
pp. 422-435 ◽  
Author(s):  
Elizabeth L. Turner ◽  
Michael J. Sweeting ◽  
Robert J. Lindfield ◽  
Daniela DeAngelis
Keyword(s):  
Differential Mortality ◽  
Prevalence Survey ◽  
Cross Sectional ◽  
Single Cross ◽  
Incidence Estimation

scholarly journals Cross-Sectional HIV Incidence Estimation in HIV Prevention Research

2013 ◽  
Vol 63 ◽  
pp. S233-S239 ◽  
Author(s):  
Ron Brookmeyer ◽  
Oliver Laeyendecker ◽  
Deborah Donnell ◽  
Susan H. Eshleman
Keyword(s):  
Hiv Prevention ◽  
Prevention Research ◽  
Hiv Incidence ◽  
Cross Sectional ◽  
Incidence Estimation

scholarly journals Development of Methods for Cross-Sectional HIV Incidence Estimation in a Large, Community Randomized Trial

PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. e78818 ◽  
Author(s):  
Oliver Laeyendecker ◽  
Michal Kulich ◽  
Deborah Donnell ◽  
Arnošt Komárek ◽  
Marek Omelka ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Hiv Incidence ◽  
Cross Sectional ◽  
Large Community ◽  
Incidence Estimation

scholarly journals Optimal accounting for age and time structure of HIV incidence estimates based on cross-sectional survey data with ascertainment of ‘recent infection’

2021 ◽  
Author(s):  
Laurette Mhlanga ◽  
Grebe Eduard ◽  
Alex Welte
Keyword(s):  
Survey Data ◽  
Age Structure ◽  
Estimation Procedure ◽  
Time Dependent ◽  
Data Sets ◽  
Hiv Incidence ◽  
Recent Infection ◽  
Cross Sectional ◽  
Incidence Estimation ◽  
Dependent Mortality

Abstract BackgroundMany surveys have attempted to estimate HIV incidence from cross-sectional data which includes ascertainment of ‘recent infection’, but the inevitable age and time structure of this data has never been systematically explored – no doubt partly because statistical precision in such estimates is often insufficient to allow for satisfactory disaggregation. Given the non-trivial age structure of HIV incidence and prevalence, and the enormous investments that have been made in such data sets, it is important to understand effective ways to extract valid age structure from these precious data sets. MethodsUsing a comprehensive demographic/epidemiological simulation platform developed for this, and some wider, purposes (documented in more detail separately) we simulated a complex ‘South Africa inspired’ HIV epidemic, with explicitly specified 1) age/time dependent incidence, 2) age/time dependent mortality for uninfected individuals, and 3) age/time/time-since-infection dependent mortality for infected individuals. In this simulated world, we conducted cross-sectional surveys at various times, and applied variants of the recent infection based incidence estimation methodology of Kassanjee et al. We analysed in considerable detail how to smooth, and average over, the age structure in these surveys to produce the incidence estimates, paying attention to the fundamental trade-off between bias and statistical error.ResultsWe summarise our detailed observations about incidence estimates, generated by various age smoothing or age disaggregation procedures, into a straightforward fully specified ‘one size fits most’ algorithm for processing the survey data into age-specific incidence estimates: 1) generalised linear regression to turn observations into ‘prevalence’ of ‘infection’ and ‘recent infection’ (logit, and complementary log log, link functions, respectively; fitting coefficients of up to cubic terms in age/time); 2) a ‘moving window’ data inclusion recipe which handles each age/time point of interest separately; 3) post hoc age averaging of resulting pseudo continuously fitted incidence; 4) bootstrapping as a generic variance/significance estimation procedure.ConclusionsAs far as we are aware, this is the first analysis of several fine details of how age structure in cross-sectional surveys interacts with recency-based incidence estimation. Our proposed default estimation procedure generates incidence estimates with negligible bias and near-optimal precision, and can be readily applied to complex survey data sets by any group in possession of such data. Our code is available, in part freely through the R computing platform, and in part upon request.

scholarly journals Estimating the force of infection for HCV in injecting drug users using interval-censored data

2011 ◽  
Vol 140 (6) ◽  
pp. 1064-1074 ◽  
Author(s):  
A. CASTRO-SÁNCHEZ ◽  
Z. SHKEDY ◽  
N. HENS ◽  
M. AERTS ◽  
R. GESKUS ◽  
...  
Keyword(s):  
Drug Users ◽  
Injecting Drug Users ◽  
Interval Censoring ◽  
Risk Of Infection ◽  
Cross Sectional ◽  
Force Of Infection ◽  
Single Cross ◽  
Interval Censored ◽  
Amsterdam Cohort Study ◽  
The Impact

SUMMARYInjecting drug users (IDUs) account for most new HCV infections. The objectives of this study were: to estimate the force of infection for hepatitis C virus in IDUs within the interval-censoring framework and to determine the impact of risk factors such as frequency of injection, drug injected, sharing of syringes and time of first injection on the time to HCV infection. We used data from the Amsterdam Cohort Study collected in The Netherlands and focused on those individuals who were HCV negative upon entry into the study. Based on the results, the force of infection was found to vary with time of first injection. The risk of infection was higher in the first 3 years of an IDU's career, implying estimates based on single cross-sectional studies could be biased. Frequency of injection and type of drug injected were found to be highly significant predictors, whereas sharing syringes was not.

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