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Using panel data for partial identification of human immunodeficiency virus prevalence when infection status is missing not at random

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  • Bruno Arpino
  • Elisabetta De Cao
  • Franco Peracchi

Abstract

type="main" xml:id="rssa12027-abs-0001"> Population-based surveys are often considered the ‘gold standard’ to estimate the prevalence of human immunodeficiency virus (HIV) but typically suffer from serious missing data problems. This causes considerable uncertainty about HIV prevalence. Following the partial identification approach, we produce worst-case bounds for HIV prevalence. We then exploit the availability of panel data and the absorbing nature of HIV infection to narrow the width of these bounds. Applied to panel data from rural Malawi, our approach considerably reduces the width of the worst-case bounds. It also allows us to check the credibility of the additional assumptions that are imposed by methods that point-identify HIV prevalence.

Suggested Citation

  • Bruno Arpino & Elisabetta De Cao & Franco Peracchi, 2014. "Using panel data for partial identification of human immunodeficiency virus prevalence when infection status is missing not at random," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(3), pages 587-606, June.
  • Handle: RePEc:bla:jorssa:v:177:y:2014:i:3:p:587-606
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    File URL: http://hdl.handle.net/10.1111/rssa.2014.177.issue-3
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    Cited by:

    1. Mark McGovern & David Canning & Till Bärnighausen, 2018. "Accounting for Non-Response Bias using Participation Incentives and Survey Design," CHaRMS Working Papers 18-02, Centre for HeAlth Research at the Management School (CHaRMS).
    2. Giampiero Marra & Rosalba Radice & Till Bärnighausen & Simon N. Wood & Mark E. McGovern, 2017. "A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 484-496, April.

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