Adjustment for Missing Data in Complex Surveys Using Doubly Robust Estimation
- 1 November 2010
- journal article
- research article
- Published by Ovid Technologies (Wolters Kluwer Health) in Epidemiology
- Vol. 21 (6), 863-871
- https://doi.org/10.1097/ede.0b013e3181f57571
Abstract
Background: The Demographic and Health Survey program routinely collects nationally representative information on HIV-related risk behaviors in many countries, using face-to-face interviews and a complex sampling scheme. If respondents skip questions about behaviors perceived as socially undesirable, such interviews may introduce bias. We sought to implement a doubly robust estimator to correct for dependent missing data in this context. Methods: We applied 3 methods of adjustment for nonresponse on self-reported commercial sexual contact data from the 2005–2006 India Demographic Health Survey to estimate the prevalence of sexual contact between sexually active men and female sex workers. These methods were inverse-probability weighted regression, outcome regression, and doubly robust estimation—a recently-described approach that is more robust to model misspecification. Results: Compared with an unadjusted prevalence of 0.9% for commercial sexual contact prevalence (95% confidence interval = 0.8%–1.0%), adjustment for nonresponse using doubly robust estimation yielded a prevalence of 1.1% (1.0%–1.2%). We found similar estimates with adjustment by outcome regression and inverse-probability weighting. Marital status was strongly associated with item nonresponse, and correction for nonresponse led to a nearly 80% increase in the prevalence of commercial sexual contact among unmarried men (from 6.9% to 12.1%–12.4%). Conclusions: Failure to correct for nonresponse produced a bias in self-reported commercial sexual contact. To facilitate the application of these methods (including the doubly robust estimator) to complex survey data settings, we provide analytical variance estimators and the corresponding SAS and MATLAB code. These variance estimators remain valid regardless of whether the modeling assumptions are correct.This publication has 22 references indexed in Scilit:
- The Semiparametric Case‐Only EstimatorBiometrics, 2010
- Indian Men's Use of Commercial Sex Workers: Prevalence, Condom Use, and Related Gender AttitudesJAIDS Journal of Acquired Immune Deficiency Syndromes, 2010
- A Simple Implementation of Doubly Robust Estimation in Logistic Regression With Covariates Missing at RandomEpidemiology, 2009
- Logistic Regression With Incomplete Covariate Data in Complex Survey SamplingEpidemiology, 2009
- A randomised controlled trial comparing computer-assisted with face-to-face sexual history taking in a clinical settingSexually Transmitted Infections, 2006
- Doubly Robust Estimation in Missing Data and Causal Inference ModelsBiometrics, 2005
- Audio computer assisted interviewing to measure HIV risk behaviours in a clinic populationSexually Transmitted Infections, 2005
- Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse ModelsJournal of the American Statistical Association, 1999
- HIV and India: looking into the abyssTropical Medicine & International Health, 1996
- Risk factors for HIV infection in people attending clinics for sexually transmitted diseases in IndiaBMJ, 1995