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

American Journal of Epidemiology

Volumn 185, Issue 4, 2017, Pages 304-315

  Leacy, Finbarr P ^a,b   Floyd, Sian ^c   Yates, Tom A ^d   White, Ian R ^b  

^a ROYAL COLLEGE OF SURGEONS IN IRELAND   (Ireland)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

^d UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Causal mediation analysis; Incomplete data; Nonignorable nonresponse; Sensitivity analysis

Indexed keywords

ACQUIRED IMMUNE DEFICIENCY SYNDROME; DATA SET; DETECTION METHOD; DISEASE PREVALENCE; HEALTH STATUS; HEALTH SURVEY; HUMAN IMMUNODEFICIENCY VIRUS; MEDICAL GEOGRAPHY; SENSITIVITY ANALYSIS; TUBERCULOSIS;

ADULT; CONTROLLED STUDY; FEMALE; HIV TEST; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; INFECTION RISK; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; PREVALENCE; REVIEW; SENSITIVITY ANALYSIS; TUBERCULOSIS; YOUNG ADULT; ADOLESCENT; AIDS-RELATED OPPORTUNISTIC INFECTIONS; HIV INFECTIONS; ODDS RATIO; RISK FACTOR; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ZAMBIA;

HUMAN IMMUNODEFICIENCY VIRUS;

ADOLESCENT; ADULT; AIDS-RELATED OPPORTUNISTIC INFECTIONS; DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC METHODS; FEMALE; HIV INFECTIONS; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; MODELS, STATISTICAL; ODDS RATIO; PREVALENCE; RISK FACTORS; TUBERCULOSIS; YOUNG ADULT;

EID: 85015902821     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kww107     Document Type: Review

References (30)

1. Rubin DB. Inference and missing data. Biometrika. 1976; 63(3):581-592.
2. National Research Council. The Prevention and Treatment of Missing Data in Clinical Trials. Washington, DC: The National Academies Press; 2010.
3. Valeri L, VanderWeele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods. 2013;18(2):137-150.
4. Alyes H, Muyoyeta M, Du Toit E, et al. Effect of household and community interventions on the burden of tuberculosis in southern Africa: the ZAMSTAR community-randomised trial. Lancet. 2013;382(9899):1183-1194.
5. Reniers G, Eaton J. Refusal bias in HIV prevalence estimates from nationally representative seroprevalence surveys. AIDS. 2009;23(5):621-629.
6. Floyd S, Molesworth A, Dube A, et al. Underestimation of HIV prevalence in surveys when some people already know their status, and ways to reduce the bias. AIDS. 2013;27(2):223-242.
7. Bärnighausen T, Tanser F, Malaza A, et al. HIV status and participation in HIV surveillance in the era of antiretroviral treatment: A study of linked population-based and clinical data in rural South Africa. Trop Med Int Health. 2012;17(8):e103-e110.
8. Larmarange J, Mossong J, Bärnighausen T, et al. Participation dynamics in population-based longitudinal HIV surveillance in rural South Africa. PLoS One. 2015;10(4):e0123345.
9. World Health Organization/United Nations Programme on HIV and AIDS. Guidelines for Measuring National HIV Prevalence in Population-Based Surveys. Geneva, Switzerland: World Health Organization; 2005.
10. Shanaube K, Sismanidis C, Ayles H, et al. Annual risk of tuberculous infection using different methods in communities with a high prevalence of TB and HIV in Zambia and South Africa. PLoS One. 2009;4(11):e7749.
11. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York, NY: John Wiley and Sons, Inc.; 1987.
12. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377-399.
13. van Buuren S, Groothuis-Oudshoorn K. MICE: multivariate imputation by chained equations in R. J Stat Softw. 2011; 45(3):1-67.
14. Rubin DB. Formalizing subjective notions about the effect of nonrespondents in sample surveys. J Am Stat Assoc. 1977; 72(359):538-543.
15. van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med. 1999;18(6):681-694.
16. Resseguier N, Giorgi R, Paoletti X. Sensitivity analysis when data are missing not-at-random [letter]. Epidemiology. 2011; 22(2):282.
17. Carpenter JR, Kenward, MG. Missing Data in Randomised Controlled Trials-A Practical Guide. Birmingham, UK: National Institute for Health Research; 2008 (Publication RM03/JH17/MK).
18. Carpenter JR, Kenward MG. Multiple Imputation and Its Application. Chichester, UK: John Wiley and Sons, Inc.; 2013.
19. Moreno-Betancur M, Chavance M. Sensitivity analysis of incomplete longitudinal data departing from the missing at random assumption: methodology and application in a clinical trial with drop-outs. Stat Methods Med Res. 2013; 25(4):1471-1489.
20. Liublinska V, Rubin DB. Sensitivity analysis for a partially missing binary outcome in a two-arm randomized clinical trial. Stat Med. 2014;33(24):4170-4185.
21. VanderWeele TJ, Vansteelandt S. Odds ratios for mediation analysis for a dichotomous outcome. Am J Epidemiol. 2010; 172(12):1339-1348.
22. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173-1182.
23. Daniels MJ, Hogan JW. Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis. Boca Raton, FL: Chapman and Hall/CRC Press; 2008.
24. Siddique J, Harel O, Crespi CM. Addressing missing data mechanism uncertainty using multiple-model multiple imputation: application to a longitudinal clinical trial. Ann Appl Stat. 2012;6(4):1814-1837.
25. Siddique J, Harel O, Crespi CM, et al. Binary variable multiple-model multiple imputation to address missing data mechanism uncertainty: application to a smoking cessation trial. Stat Med. 2014;33(17):3013-3028.
26. Yan X, Lee S, Li N. Missing data handling methods in medical device clinical trials. J Biopharm Stat. 2009;19(6):1085-1098.
27. Tchetgen Tchetgen EJ, Phiri K. Bounds for pure direct effect. Epidemiology. 2014;25(5):775-776.
28. Naimi AI. Invited commentary: boundless science-putting natural direct and indirect effects in a clearer empirical context. Am J Epidemiol. 2015;182(2):109-114.
29. Naimi AI, Kaufman JS. Counterfactual theory in social epidemiology: reconciling analysis and action for the social determinants of health. Curr Epidemiol Rep. 2015;2(1): 52-60.
30. Ratitch B, O'Kelly M, Tosiello R. Missing data in clinical trials: from clinical assumptions to statistical analysis using pattern mixture models. Pharm Stat. 2013;12(6): 337-347.