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Evaluation of a Bayesian Model Integration-Based Method for Censored DataHou L.a · Wang K.b · Bartlett C.W.a
aBattelle Center for Mathematical Medicine, The Research Institute at Nationwide Children’s Hospital and Department of Pediatrics, The Ohio State University, Columbus, Ohio, and bDepartment of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, USA Corresponding Author
Assist. Prof. Christopher W. Bartlett, PhD
Battelle Center for Mathematical Medicine, The Research Institute
Nationwide Children’s Hospital and The Ohio State University
JW3926, 700 Children’s Drive, Columbus, OH 43205 (USA)
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.
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