ArticleEpidemiology (Cambridge, Mass.)2013
Sensitivity analyses for sparse-data problems-using weakly informative bayesian priors.
Article in Epidemiology (Cambridge, Mass.), 2013. 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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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
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- Prenatal Exposure to Endocrine-disrupting Chemicals in Relation to Autism Spectrum Disorder and Intellectual Disability.Epidemiology (Cambridge, Mass.) · 2019Article
- A Bayesian approach to the g-formula.Statistical methods in medical research · 2018Article
- Risk of Police-Involved Death by Race/Ethnicity and Place, United States, 2012-2018.American journal of public health · 2018Article
- The researcher and the consultant: from testing to probability statements.European journal of epidemiology · 2015Article
- Replication of breast cancer susceptibility loci in whites and African Americans using a Bayesian approach.American journal of epidemiology · 2014Article
- Maximum likelihood, profile likelihood, and penalized likelihood: a primer.American journal of epidemiology · 2014Article
- Breast cancer subtypes and previously established genetic risk factors: a bayesian approach.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2014Article
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3 authors.
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Abstract
Sparse-data problems are common, and approaches are needed to evaluate the sensitivity of parameter estimates based on sparse data. We propose a Bayesian approach that uses weakly informative priors to quantify sensitivity of parameters to sparse data. The weakly informative prior is based on accumulated evidence regarding the expected magnitude of relationships using relative measures of disease association. We illustrate the use of weakly informative priors with an example of the association of lifetime alcohol consumption and head and neck cancer. When data are sparse and the observed information is weak, a weakly informative prior will shrink parameter estimates toward the prior mean. Additionally, the example shows that when data are not sparse and the observed information is not weak, a weakly informative prior is not influential. Advancements in implementation of Markov Chain Monte Carlo simulation make this sensitivity analysis easily accessible to the practicing epidemiologist.
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