ArticleCurrent epidemiology reports2015
Are all biases missing data problems?
Article in Current epidemiology reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Limitations for health research with restricted data collection from UK primary care.Pharmacoepidemiology and drug safety · 2019Pooled it
- Towards a unified approach to formal "risk of bias" assessments for causal and descriptive inference.Quality & quantity · 2026Article
- Assessment of Dental Fear and Anxiety Tools for Children: A Review.Healthcare (Basel, Switzerland) · 2025Review
- How Accurate Is Multiple Imputation for Nutrient Intake Estimation? Insights from ASA24 Data.Nutrients · 2025Article
- Invited commentary: it's not all about residual confounding-a plea for quantitative bias analysis for epidemiologic researchers and educators.American journal of epidemiology · 2024Article
- Reducing Information and Selection Bias in EHR-Linked Biobanks via Genetics-Informed Multiple Imputation and Sample Weighting.medRxiv : the preprint server for health sciences · 2024Article
- Data Missingness Reporting and Use of Methods to Address It in Critical Care Cohort Studies.Critical care explorations · 2023Article
- Leveraging auxiliary data to improve precision in inverse probability-weighted analyses.Annals of epidemiology · 2022Article
- Practical data considerations for the modern epidemiology student.Global epidemiology · 2021Article
- Bias Analysis Gone Bad.American journal of epidemiology · 2021Article
- Cesarean delivery and the risk of allergic rhinitis in children.Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology · 2020Article
- Evaluating the Population Impact on Racial/Ethnic Disparities in HIV in Adulthood of Intervening on Specific Targets: A Conceptual and Methodological Framework.American journal of epidemiology · 2018Article
- Statin adherence and the risk of Parkinson's disease: A population-based cohort study.PloS one · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Estimating causal effects is a frequent goal of epidemiologic studies. Traditionally, there have been three established systematic threats to consistent estimation of causal effects. These three threats are bias due to confounders, selection, and measurement error. Confounding, selection, and measurement bias have typically been characterized as distinct types of biases. However, each of these biases can also be characterized as missing data problems that can be addressed with missing data solutions. Here we describe how the aforementioned systematic threats arise from missing data as well as review methods and their related assumptions for reducing each bias type. We also link the assumptions made by the reviewed methods to the missing completely at random (MCAR) and missing at random (MAR) assumptions made in the missing data framework that allow for valid inferences to be made based on the observed, incomplete data.
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What OpenQuestion holds
Registered trials
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.