Quantifying unrecognised replication present in reports of HIV diagnoses

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Sfikas, Nikolaos and Greenhalgh, David and Huo, Wenwen and Mortimer, Janet and Robertson, Chris (2014) Quantifying unrecognised replication present in reports of HIV diagnoses. Statistics in Medicine, 33 (16). pp. 2774-2796. ISSN 0277-6715 (https://doi.org/10.1002/sim.6121)

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Abstract

New diagnoses of HIV infection were reported confidentially to the Public Health Laboratory Service (PHLS) AIDS Centre under a national voluntary surveillance scheme. Two sets of data drawn from the national datasets were made available to us for analysis, the first in 1991, the second in 1994, by which time the replication of reports had been reduced. The data used in the analyses consisted of the numbers of replications of the reported full date of birth in the individual records (one, two, three and so on), for each year of birth. This paper uses a non-parametric maximum likelihood estimation method for quantifying the amount of replication in the data. The estimated amount of replication was 3.37% (95% confidence interval (0.98%,11.83%)) in the 1991 and 0.58% (95% confidence interval (0%,2.64%)) in the 1994 dataset.

ORCID iDs

Sfikas, Nikolaos, Greenhalgh, David ORCID logoORCID: https://orcid.org/0000-0001-5380-3307, Huo, Wenwen, Mortimer, Janet and Robertson, Chris;
  • Item type:Article
    ID code:49771
    Dates:
    Date
    Event
    20 July 2014
    Published
    26 February 2014
    Published Online
    4 February 2014
    Accepted
    Notes:. This is the peer reviewed version of the following article: Sfikas, N., Greenhalgh, D., Huo, W., Mortimer, J. and Robertson, Chris. (2014), Quantifying unrecognised replication present in reports of HIV diagnoses. Statist. Med., 33: 2774–2796., which has been published in final form at http://dx.doi.org/10.1002/sim.6121. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
    Subjects:Science > Microbiology > Virology
    Science > Mathematics > Probabilities. Mathematical statistics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:13 Oct 2014 15:28
    Last modified:11 Nov 2024 10:48
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/49771
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