A semiparametric method for the analysis of outcomes during a gap in HIV care under incomplete outcome ascertainment

dc.contributor.authorBakoyannis, Giorgos
dc.contributor.authorDiero, Lameck
dc.contributor.authorMwangi, Ann
dc.contributor.authorWools-Kaloustian, Kara K.
dc.contributor.authorYiannoutsos, Constantin T.
dc.contributor.departmentBiostatistics, School of Public Healthen_US
dc.date.accessioned2023-03-08T12:35:31Z
dc.date.available2023-03-08T12:35:31Z
dc.date.issued2020-09
dc.description.abstractObjectives: Estimation of the cascade of HIV care is essential for evaluating care and treatment programs, informing policy makers and assessing targets such as 90-90-90. A challenge to estimating the cascade based on electronic health record concerns patients "churning" in and out of care. Correctly estimating this dynamic phenomenon in resource-limited settings, such as those found in sub-Saharan Africa, is challenging because of the significant death under-reporting. An approach to partially recover information on the unobserved deaths is a double-sampling design, where a small subset of individuals with a missed clinic visit is intensively outreached in the community to actively ascertain their vital status. This approach has been adopted in several programs within the East Africa regional IeDEA consortium, the context of our motivating study. The objective of this paper is to propose a semiparametric method for the analysis of competing risks data with incomplete outcome ascertainment. Methods: Based on data from double-sampling designs, we propose a semiparametric inverse probability weighted estimator of key outcomes during a gap in care, which are crucial pieces of the care cascade puzzle. Results: Simulation studies suggest that the proposed estimators provide valid estimates in settings with incomplete outcome ascertainment under a set of realistic assumptions. These studies also illustrate that a naïve complete-case analysis can provide seriously biased estimates. The methodology is applied to electronic health record data from the East Africa IeDEA Consortium to estimate death and return to care during a gap in care. Conclusions: The proposed methodology provides a robust approach for valid inferences about return to care and death during a gap in care, in settings with death under-reporting. Ultimately, the resulting estimates will have significant consequences on program construction, resource allocation, policy and decision making at the highest levels.en_US
dc.eprint.versionAuthor's manuscripten_US
dc.identifier.citationBakoyannis G, Diero L, Mwangi A, Wools-Kaloustian KK, Yiannoutsos CT. A semiparametric method for the analysis of outcomes during a gap in HIV care under incomplete outcome ascertainment. Stat Commun Infect Dis. 2020;12(Suppl 1):20190013. doi:10.1515/scid-2019-0013en_US
dc.identifier.urihttps://hdl.handle.net/1805/31713
dc.language.isoen_USen_US
dc.publisherDe Gruyteren_US
dc.relation.isversionof10.1515/scid-2019-0013en_US
dc.relation.journalStatistical Communications in Infectious Diseasesen_US
dc.rightsPublisher Policyen_US
dc.sourcePMCen_US
dc.subjectCompeting risksen_US
dc.subjectHIV care cascadeen_US
dc.subjectMissing dataen_US
dc.subjectSemiparametric methoden_US
dc.titleA semiparametric method for the analysis of outcomes during a gap in HIV care under incomplete outcome ascertainmenten_US
dc.typeArticleen_US
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