title: 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.
abstract: 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.
authors.affiliation: Division of Population Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland.|MRC Biostatistics Unit, Cambridge Institute of Public Health, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Finbarr P
authors.initials: FP
authors.lastname: Leacy
authors.affiliation: MRC Tropical Epidemiology Group, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
authors.forename: Sian
authors.initials: S
authors.lastname: Floyd
authors.affiliation: Centre for Infectious Disease Epidemiology, Research Department of Infection and Population Health, University College London, London, United Kingdom.
authors.forename: Tom A
authors.initials: TA
authors.lastname: Yates
authors.affiliation: MRC Biostatistics Unit, Cambridge Institute of Public Health, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Ian R
authors.initials: IR
authors.lastname: White
mesh_terms: D017088:AIDS-Related Opportunistic Infections / Q000453:epidemiology*; D000293:Adolescent; D000328:Adult; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D005260:Female; D015658:HIV Infections / Q000175:diagnosis / Q000453:epidemiology*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016017:Odds Ratio; D015995:Prevalence; D012307:Risk Factors; D014376:Tuberculosis / Q000453:epidemiology*; D055815:Young Adult
keywords: causal mediation analysis; incomplete data; nonignorable nonresponse; sensitivity analysis