Analyses of Sensitivity to the Missing-at-Random Assumption Using Multiple Imputation With Delta Adjustment: Application to a Tuberculosis/HIV Prevalence Survey With Incomplete HIV-Status Data

Am J Epidemiol. 2017 Feb 15;185(4):304-315. doi: 10.1093/aje/kww107.


Multiple imputation with delta adjustment provides a flexible and transparent means to impute univariate missing data under general missing-not-at-random mechanisms. This facilitates the conduct of analyses assessing sensitivity to the missing-at-random (MAR) assumption. We review the delta-adjustment procedure and demonstrate how it can be used to assess sensitivity to departures from MAR, both when estimating the prevalence of a partially observed outcome and when performing parametric causal mediation analyses with a partially observed mediator. We illustrate the approach using data from 34,446 respondents to a tuberculosis and human immunodeficiency virus (HIV) prevalence survey that was conducted as part of the Zambia-South Africa TB and AIDS Reduction Study (2006-2010). In this study, information on partially observed HIV serological values was supplemented by additional information on self-reported HIV status. We present results from 2 types of sensitivity analysis: The first assumed that the degree of departure from MAR was the same for all individuals with missing HIV serological values; the second assumed that the degree of departure from MAR varied according to an individual's self-reported HIV status. Our analyses demonstrate that multiple imputation offers a principled approach by which to incorporate auxiliary information on self-reported HIV status into analyses based on partially observed HIV serological values.

Keywords: causal mediation analysis; incomplete data; nonignorable nonresponse; sensitivity analysis.

MeSH terms

  • AIDS-Related Opportunistic Infections / epidemiology*
  • Adolescent
  • Adult
  • Data Interpretation, Statistical
  • Epidemiologic Methods
  • Female
  • HIV Infections / diagnosis
  • HIV Infections / epidemiology*
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Models, Statistical*
  • Odds Ratio
  • Prevalence
  • Risk Factors
  • Tuberculosis / epidemiology*
  • Young Adult