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An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.

ABSTRACT: The generalized odds-rate model is a class of semiparametric regression models, which includes the proportional hazards and proportional odds models as special cases. There are few works on estimation of the generalized odds-rate model with interval censored data because of the challenges in maximizing the complex likelihood function. In this paper, we propose a gamma-Poisson data augmentation approach to develop an Expectation Maximization algorithm, which can be used to fit the generalized odds-rate model to interval censored data. The proposed Expectation Maximization algorithm is easy to implement and is computationally efficient. The performance of the proposed method is evaluated by comprehensive simulation studies and illustrated through applications to datasets from breast cancer and hemophilia studies. In order to make the proposed method easy to use in practice, an R package 'ICGOR' was developed. Copyright © 2016 John Wiley & Sons, Ltd.

SUBMITTER: Zhou J

PROVIDER: S-EPMC5998339 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.

Zhou Jie J Zhang Jiajia J Lu Wenbin W

Statistics in medicine 20161221 7

The generalized odds-rate model is a class of semiparametric regression models, which includes the proportional hazards and proportional odds models as special cases. There are few works on estimation of the generalized odds-rate model with interval censored data because of the challenges in maximizing the complex likelihood function. In this paper, we propose a gamma-Poisson data augmentation approach to develop an Expectation Maximization algorithm, which can be used to fit the generalized odd ...[more]

PMID: 28004414

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Project description:BackgroundPopulation genetics and association studies usually rely on a set of known variable sites that are then genotyped in subsequent samples, because it is easier to genotype than to discover the variation. This is also true for structural variation detected from sequence data. However, the genotypes at known variable sites can only be inferred with uncertainty from low coverage data. Thus, statistical approaches that infer genotype likelihoods, test hypotheses, and estimate population parameters without requiring accurate genotypes are becoming popular. Unfortunately, the current implementations of these methods are intended to analyse only single nucleotide and short indel variation, and they usually assume that the two alleles in a heterozygous individual are sampled with equal probability. This is generally false for structural variants detected with paired ends or split reads. Therefore, the population genetics of structural variants cannot be studied, unless a painstaking and potentially biased genotyping is performed first.ResultsWe present svgem, an expectation-maximization implementation to estimate allele and genotype frequencies, calculate genotype posterior probabilities, and test for Hardy-Weinberg equilibrium and for population differences, from the numbers of times the alleles are observed in each individual. Although applicable to single nucleotide variation, it aims at bi-allelic structural variation of any type, observed by either split reads or paired ends, with arbitrarily high allele sampling bias. We test svgem with simulated and real data from the 1000 Genomes Project.Conclusionssvgem makes it possible to use low-coverage sequencing data to study the population distribution of structural variants without having to know their genotypes. Furthermore, this advance allows the combined analysis of structural and nucleotide variation within the same genotype-free statistical framework, thus preventing biases introduced by genotype imputation.

Dataset Information

An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.

Publications

An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data.

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