ArticleEpidemiology (Cambridge, Mass.)2014
Comparison of bias analysis strategies applied to a large data set.
Article in Epidemiology (Cambridge, Mass.), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Probabilistic bias analysis in pharmacoepidemiology and comparative effectiveness research: a systematic review.Pharmacoepidemiology and drug safety · 2016Pooled it
- Parameterization of Beta Distributions for Bias Parameters of Binary Exposure Misclassification in Probabilistic Bias Analysis.Epidemiology (Cambridge, Mass.) · 2025Article
- SAS and R code for probabilistic quantitative bias analysis for misclassified binary variables and binary unmeasured confounders.International journal of epidemiology · 2023Article
- Bias in Self-reported Prepregnancy Weight Across Maternal and Clinical Characteristics.Maternal and child health journal · 2021Article
- Gestational Weight Gain and Long-term Maternal Obesity Risk: A Multiple-Bias Analysis.Epidemiology (Cambridge, Mass.) · 2021Article
- The impact of setting a pregnancy weight gain goal on total weight gain.Paediatric and perinatal epidemiology · 2021Article
- Association Between Rotavirus Vaccination and Type 1 Diabetes in Children.JAMA pediatrics · 2020Article
- A primer on quantitative bias analysis with positive predictive values in research using electronic health data.Journal of the American Medical Informatics Association : JAMIA · 2019Review
- Gestational Weight Gain and Adverse Birth Outcomes in Twin Pregnancies.Obstetrics and gynecology · 2019Article
- The curse of the perinatal epidemiologist: inferring causation amidst selection.Current epidemiology reports · 2018Article
- Good Practices for Observational Studies of Maternal Weight and Weight Gain in Pregnancy.Paediatric and perinatal epidemiology · 2018Review
- Hierarchical Semi-Bayes Methods for Misclassification in Perinatal Epidemiology.Epidemiology (Cambridge, Mass.) · 2018Article
- Methadone Versus Buprenorphine for Opioid Use Dependence and Risk of Neonatal Abstinence Syndrome.Epidemiology (Cambridge, Mass.) · 2018Article
- Low Gestational Weight Gain and Risk of Adverse Perinatal Outcomes in Obese and Severely Obese Women.Epidemiology (Cambridge, Mass.) · 2016Article
- Validity of birth certificate-derived maternal weight data in twin pregnancies.Maternal & child nutrition · 2016Article
- Maternal obesity and gestational weight gain are risk factors for infant death.Obesity (Silver Spring, Md.) · 2016Article
- Comparison of gestational weight gain z-scores and traditional weight gain measures in relation to perinatal outcomes.Paediatric and perinatal epidemiology · 2015Article
- Misclassification in administrative claims data: quantifying the impact on treatment effect estimates.Current epidemiology reports · 2014Article
- Validity of birth certificate-derived maternal weight data.Paediatric and perinatal epidemiology · 2014Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundEpidemiologic data sets continue to grow larger. Probabilistic-bias analyses, which simulate hundreds of thousands of replications of the original data set, may challenge desktop computational resources.
methodsWe implemented a probabilistic-bias analysis to evaluate the direction, magnitude, and uncertainty of the bias arising from misclassification of prepregnancy body mass index when studying its association with early preterm birth in a cohort of 773,625 singleton births. We compared 3 bias analysis strategies: (1) using the full cohort, (2) using a case-cohort design, and (3) weighting records by their frequency in the full cohort.
resultsUnderweight and overweight mothers were more likely to deliver early preterm. A validation substudy demonstrated misclassification of prepregnancy body mass index derived from birth certificates. Probabilistic-bias analyses suggested that the association between underweight and early preterm birth was overestimated by the conventional approach, whereas the associations between overweight categories and early preterm birth were underestimated. The 3 bias analyses yielded equivalent results and challenged our typical desktop computing environment. Analyses applied to the full cohort, case cohort, and weighted full cohort required 7.75 days and 4 terabytes, 15.8 hours and 287 gigabytes, and 8.5 hours and 202 gigabytes, respectively.
conclusionsLarge epidemiologic data sets often include variables that are imperfectly measured, often because data were collected for other purposes. Probabilistic-bias analysis allows quantification of errors but may be difficult in a desktop computing environment. Solutions that allow these analyses in this environment can be achieved without new hardware and within reasonable computational time frames.
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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.