ArticleAmerican journal of epidemiology2018
Inverse-Probability-Weighted Estimation for Monotone and Nonmonotone Missing Data.
Article in American journal of epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
30 citing papers in PubMed.
- Long-term Impact of a 10-Year Intensive Lifestyle Intervention on a Deficit Accumulation Frailty Index: Action for Health in Diabetes Trial.The journals of gerontology. Series A, Biological sciences and medical sciences · 2023Trial
- Long-Term Validation of a Consumer- vs. Industrial-Grade Indoor Radon and Air Quality Monitor at a School in Utah.Sensors (Basel, Switzerland) · 2026Article
- Mind the Gap: Addressing Missing Person Time When Estimating Outcome Incidence in Longitudinal Data.Epidemiology (Cambridge, Mass.) · 2026Article
- Assessing Treatment Effects in Observational Data With Missing Confounders: A Comparative Study of Practical Doubly-Robust and Traditional Missing Data Methods.Statistics in medicine · 2026Article
- Early life grandmother caregiving trajectories and child development: a longitudinal study.International journal of behavioral development · 2025Article
- Adjusting for selection bias due to missing eligibility criteria in emulated target trials.American journal of epidemiology · 2025Article
- Effectiveness and safety of medication abortion with vs without screening ultrasonography or pelvic examination.American journal of obstetrics and gynecology · 2025Article
- A two-stage joint model approach to handle incomplete time dependent markers in survival data through inverse probability weight and multiple imputation.Scientific reports · 2025Article
- Comparing causal inference methods for point exposures with missing confounders: a simulation study.BMC medical research methodology · 2025Article
- Revisiting representativeness.International journal of epidemiology · 2025Article
- Invited commentary: mixing multiple imputation and bootstrapping for variance estimation.American journal of epidemiology · 2024Article
- An Examination of Sleep as a Protective Factor for Depression and Anxiety in the Perinatal Period: Novel Causal Analyses in a Prospective Pregnancy Cohort.American journal of epidemiology · 2024Article
- Analysis of Missingness Scenarios for Observational Health Data.Journal of personalized medicine · 2024Article
- Moving Beyond Medical Statistics: A Systematic Review on Missing Data Handling in Electronic Health Records.Health data science · 2024Review
- Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes.Biometrics · 2023Article
- Accounting for nonmonotone missing data using inverse probability weighting.Statistics in medicine · 2023Observational
- Noninterventional studies in the COVID-19 era: methodological considerations for study design and analysis.Journal of clinical epidemiology · 2023Article
- Influence of the level of education on women's knowledge, attitude, and practices to control the transmission of COVID-19 in the Democratic Republic of the Congo.Scientific African · 2022Article
- Using observational study data as an external control group for a clinical trial: an empirical comparison of methods to account for longitudinal missing data.BMC medical research methodology · 2022Observational
- Reflection on modern methods: combining weights for confounding and missing data.International journal of epidemiology · 2022Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
Missing data is a common occurrence in epidemiologic research. In this paper, 3 data sets with induced missing values from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are provided as examples of prototypical epidemiologic studies with missing data. Our goal was to estimate the association of maternal smoking behavior with spontaneous abortion while adjusting for numerous confounders. At the same time, we did not necessarily wish to evaluate the joint distribution among potentially unobserved covariates, which is seldom the subject of substantive scientific interest. The inverse probability weighting (IPW) approach preserves the semiparametric structure of the underlying model of substantive interest and clearly separates the model of substantive interest from the model used to account for the missing data. However, IPW often will not result in valid inference if the missing-data pattern is nonmonotone, even if the data are missing at random. We describe a recently proposed approach to modeling nonmonotone missing-data mechanisms under missingness at random to use in constructing the weights in IPW complete-case estimation, and we illustrate the approach using 3 data sets described in a companion article (Am J Epidemiol. 2018;187(3):568-575).
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