ArticleHuman heredity2012
Evaluation of a bayesian model integration-based method for censored data.
Article in Human heredity, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Gene × smoking interactions on human brain gene expression: finding common mechanisms in adolescents and adults.Journal of child psychology and psychiatry, and allied disciplines · 2013Pooled it
- Forming Big Datasets through Latent Class Concatenation of Imperfectly Matched Databases Features.Genes · 2019Article
- A genome scan for loci shared by autism spectrum disorder and language impairment.The American journal of psychiatry · 2014Article
- Employing MCMC under the PPL framework to analyze sequence data in large pedigrees.Frontiers in genetics · 2013Article
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Authors and funding
3 authors.
Funding
Abstract
objectiveNon-random missing data can adversely affect family-based linkage detection through loss of power and possible introduction of bias depending on how censoring is modeled. We examined the statistical properties of a previously proposed quantitative trait threshold (QTT) model developed for when censored data can be reasonably inferred to be beyond an unknown threshold.
methodsThe QTT model is a Bayesian model integration approach implemented in the PPL framework that requires neither specification of the threshold nor imputation of the missing data. This model was evaluated under a range of simulated data sets and compared to other methods with missing data imputed.
resultsAcross the simulated conditions, the addition of a threshold parameter did not change the PPL's properties relative to quantitative trait analysis on non-censored data except for a slight reduction in the average PPL as a reflection of the lowered information content due to censoring. This remained the case for non-normally distributed data and extreme sampling of pedigrees.
conclusionsOverall, the QTT model showed the smallest loss of linkage information relative to alternative approaches and therefore provides a unique analysis tool that obviates the need for ad hoc imputation of censored data in gene mapping studies.
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