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Augmented Cross‐Sectional Studies with Abbreviated Follow‐up for Estimating HIV Incidence
Author(s) -
Claggett B.,
Lagakos S. W.,
Wang R.
Publication year - 2012
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2011.01632.x
Subject(s) - cross sectional study , estimator , statistics , population , medicine , cohort , incidence (geometry) , estimation , test (biology) , demography , mathematics , econometrics , environmental health , engineering , biology , paleontology , geometry , systems engineering , sociology
Summary Cross‐sectional HIV incidence estimation based on a sensitive and less‐sensitive test offers great advantages over the traditional cohort study. However, its use has been limited due to concerns about the false negative rate of the less‐sensitive test, reflecting the phenomenon that some subjects may remain negative permanently on the less‐sensitive test. Wang and Lagakos (2010, Biometrics 66 , 864–874) propose an augmented cross‐sectional design that provides one way to estimate the size of the infected population who remain negative permanently and subsequently incorporate this information in the cross‐sectional incidence estimator. In an augmented cross‐sectional study, subjects who test negative on the less‐sensitive test in the cross‐sectional survey are followed forward for transition into the nonrecent state, at which time they would test positive on the less‐sensitive test. However, considerable uncertainty exists regarding the appropriate length of follow‐up and the size of the infected population who remain nonreactive permanently to the less‐sensitive test. In this article, we assess the impact of varying follow‐up time on the resulting incidence estimators from an augmented cross‐sectional study, evaluate the robustness of cross‐sectional estimators to assumptions about the existence and the size of the subpopulation who will remain negative permanently, and propose a new estimator based on abbreviated follow‐up time (AF). Compared to the original estimator from an augmented cross‐sectional study, the AF estimator allows shorter follow‐up time and does not require estimation of the mean window period, defined as the average time between detectability of HIV infection with the sensitive and less‐sensitive tests. It is shown to perform well in a wide range of settings. We discuss when the AF estimator would be expected to perform well and offer design considerations for an augmented cross‐sectional study with abbreviated follow‐up.