ArticleBiometrics2017
A general instrumental variable framework for regression analysis with outcome missing not at random.
Article in Biometrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Analytical methods used in estimating the prevalence of HIV/AIDS from demographic and cross-sectional surveys with missing data: a systematic review.BMC medical research methodology · 2020Pooled it
- Dealing with differential misclassification of an outcome or a covariate in association studies with an internally validated sample selected not at random.BMC medical research methodology · 2025Article
- Treating gaps and biases in biodiversity data as a missing data problem.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study.BMC medical research methodology · 2024Article
- Using instruments for selection to adjust for selection bias in Mendelian randomization.Statistics in medicine · 2024Article
- Sociodemographic disparities and potential biases in persistent pain estimates: Findings from 5 waves of the Irish Longitudinal Study on Ageing (TILDA).European journal of pain (London, England) · 2024Observational
- Descriptive inference using large, unrepresentative nonprobability samples: An introduction for ecologists.Ecology · 2024Article
- Methods in causal inference. Part 3: measurement error and external validity threats.Evolutionary human sciences · 2024Article
- Causal Inference With Outcome-Dependent Missingness And Self-Censoring.Proceedings of machine learning research · 2023Article
- Semiparametric marginal regression for clustered competing risks data with missing cause of failure.Biostatistics (Oxford, England) · 2023Article
- Non-parametric inference about mean functionals of non-ignorable non-response data without identifying the joint distribution.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2023Article
- Credible Mendelian Randomization Studies in the Presence of Selection Bias Using Control Exposures.Frontiers in genetics · 2021Article
- A Nuisance-Free Inference Procedure Accounting for the Unknown Missingness with Application to Electronic Health Records.Entropy (Basel, Switzerland) · 2020Article
- A semiparametric method for the analysis of outcomes during a gap in HIV care under incomplete outcome ascertainment.Statistical communications in infectious diseases · 2020Article
- Semiparametric Estimation with Data Missing Not at Random Using an Instrumental Variable.Statistica Sinica · 2018Article
- Accounting for non-response bias using participation incentives and survey design: An application using gift vouchers.Economics letters · 2018Article
- Implementation of Instrumental Variable Bounds for Data Missing Not at Random.Epidemiology (Cambridge, Mass.) · 2018Article
- Large-scale loyalty card data in health research.Digital healthArticle
Corrections and comments
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2 authors.
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Abstract
The instrumental variable (IV) design is a well-known approach for unbiased evaluation of causal effects in the presence of unobserved confounding. In this article, we study the IV approach to account for selection bias in regression analysis with outcome missing not at random. In such a setting, a valid IV is a variable which (i) predicts the nonresponse process, and (ii) is independent of the outcome in the underlying population. We show that under the additional assumption (iii) that the IV is independent of the magnitude of selection bias due to nonresponse, the population regression in view is nonparametrically identified. For point estimation under (i)-(iii), we propose a simple complete-case analysis which modifies the regression of primary interest by carefully incorporating the IV to account for selection bias. The approach is developed for the identity, log and logit link functions. For inferences about the marginal mean of a binary outcome assuming (i) and (ii) only, we describe novel and approximately sharp bounds which unlike Robins-Manski bounds, are smooth in model parameters, therefore allowing for a straightforward approach to account for uncertainty due to sampling variability. These bounds provide a more honest account of uncertainty and allows one to assess the extent to which a violation of the key identifying condition (iii) might affect inferences. For illustration, the methods are used to account for selection bias induced by HIV testing nonparticipation in the evaluation of HIV prevalence in the Zambian Demographic and Health Surveys.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.