Accounting for dropout reason in longitudinal studies with nonignorable dropout

Camille M. Moore, Samantha MaWhinney, Jeri E. Forster, Nichole E. Carlson, Amanda Allshouse, Xinshuo Wang, Jean Pierre Routy, Brian Conway, Elizabeth Connick

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Dropout is a common problem in longitudinal cohort studies and clinical trials, often raising concerns of nonignorable dropout. Selection, frailty, and mixture models have been proposed to account for potentially nonignorable missingness by relating the longitudinal outcome to time of dropout. In addition, many longitudinal studies encounter multiple types of missing data or reasons for dropout, such as loss to follow-up, disease progression, treatment modifications and death. When clinically distinct dropout reasons are present, it may be preferable to control for both dropout reason and time to gain additional clinical insights. This may be especially interesting when the dropout reason and dropout times differ by the primary exposure variable. We extend a semi-parametric varying-coefficient method for nonignorable dropout to accommodate dropout reason. We apply our method to untreated HIV-infected subjects recruited to the Acute Infection and Early Disease Research Program HIV cohort and compare longitudinal CD4+ T cell count in injection drug users to nonusers with two dropout reasons: anti-retroviral treatment initiation and loss to follow-up.

Original languageEnglish (US)
Pages (from-to)1854-1866
Number of pages13
JournalStatistical Methods in Medical Research
Volume26
Issue number4
DOIs
StatePublished - Aug 1 2017
Externally publishedYes

Keywords

  • B-spline
  • HIV/AIDS
  • dropout
  • longitudinal data
  • nonignorable missing data
  • varying-coefficient model

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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