Journal article
Evaluation of a Bayesian Model Integration-Based Method for Censored Data
Human Heredity, Vol.74(1), pp.1-11
11/2012
DOI: 10.1159/000342707
PMCID: PMC3571622
PMID: 23018141
Abstract
Objective: Non-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. Methods: The 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. Results: Across 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. Conclusions: Overall, 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.
Details
- Title: Subtitle
- Evaluation of a Bayesian Model Integration-Based Method for Censored Data
- Creators
- Liping HouKai WangChristopher W Bartlett
- Resource Type
- Journal article
- Publication Details
- Human Heredity, Vol.74(1), pp.1-11
- Publisher
- Basel, Switzerland
- DOI
- 10.1159/000342707
- PMID
- 23018141
- PMCID
- PMC3571622
- ISSN
- 0001-5652
- eISSN
- 1423-0062
- Number of pages
- 11
- Language
- English
- Date published
- 11/2012
- Academic Unit
- Biostatistics
- Record Identifier
- 9983997480102771
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