Efficient estimation of human immunodeficiency virus incidence rate using a pooled cross-sectional cohort study design
© 2020 John Wiley & Sons, Ltd..
Development of methods to accurately estimate human immunodeficiency virus (HIV) incidence rate remains a challenge. Ideally, one would follow a random sample of HIV-negative individuals under a longitudinal study design and identify incident cases as they arise. Such designs can be prohibitively resource intensive and therefore alternative designs may be preferable. We propose such a simple, less resource-intensive study design and develop a weighted log likelihood approach which simultaneously accounts for selection bias and outcome misclassification error. The design is based on a cross-sectional survey which queries individuals' time since last HIV-negative test, validates their test results with formal documentation whenever possible, and tests all persons who do not have documentation of being HIV-positive. To gain efficiency, we update the weighted log likelihood function with potentially misclassified self-reports from individuals who could not produce documentation of a prior HIV-negative test and investigate large sample properties of validated sub-sample only versus pooled sample estimators through extensive Monte Carlo simulations. We illustrate our method by estimating incidence rate for individuals who tested HIV-negative within 1.5 and 5 years prior to Botswana Combination Prevention Project enrolment. This article establishes that accurate estimates of HIV incidence rate can be obtained from individuals' history of testing in a cross-sectional cohort study design by appropriately accounting for selection bias and misclassification error. Moreover, this approach is notably less resource-intensive compared to longitudinal and laboratory-based methods.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:39 |
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| Enthalten in: |
Statistics in medicine - 39(2020), 24 vom: 30. Okt., Seite 3255-3271 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Molebatsi, Kesaobaka [VerfasserIn] |
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| Links: |
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| Themen: |
Cross-sectional cohort |
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| Anmerkungen: |
Date Completed 21.06.2021 Date Revised 05.10.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/sim.8661 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM314486038 |
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| 520 | |a © 2020 John Wiley & Sons, Ltd. | ||
| 520 | |a Development of methods to accurately estimate human immunodeficiency virus (HIV) incidence rate remains a challenge. Ideally, one would follow a random sample of HIV-negative individuals under a longitudinal study design and identify incident cases as they arise. Such designs can be prohibitively resource intensive and therefore alternative designs may be preferable. We propose such a simple, less resource-intensive study design and develop a weighted log likelihood approach which simultaneously accounts for selection bias and outcome misclassification error. The design is based on a cross-sectional survey which queries individuals' time since last HIV-negative test, validates their test results with formal documentation whenever possible, and tests all persons who do not have documentation of being HIV-positive. To gain efficiency, we update the weighted log likelihood function with potentially misclassified self-reports from individuals who could not produce documentation of a prior HIV-negative test and investigate large sample properties of validated sub-sample only versus pooled sample estimators through extensive Monte Carlo simulations. We illustrate our method by estimating incidence rate for individuals who tested HIV-negative within 1.5 and 5 years prior to Botswana Combination Prevention Project enrolment. This article establishes that accurate estimates of HIV incidence rate can be obtained from individuals' history of testing in a cross-sectional cohort study design by appropriately accounting for selection bias and misclassification error. Moreover, this approach is notably less resource-intensive compared to longitudinal and laboratory-based methods | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a cross-sectional cohort | |
| 650 | 4 | |a incidence rate | |
| 650 | 4 | |a misclassification error | |
| 650 | 4 | |a selection bias | |
| 650 | 4 | |a weighted log likelihood | |
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| 700 | 1 | |a Mokgatlhe, Lucky |e verfasserin |4 aut | |
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| 700 | 1 | |a Gaseitsiwe, Simani |e verfasserin |4 aut | |
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| 700 | 1 | |a DeGruttola, Victor |e verfasserin |4 aut | |
| 700 | 1 | |a Tchetgen Tchetgen, Eric |e verfasserin |4 aut | |
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