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整合微生物 16S 与宏基因组测序数据以提高差异丰度检验效率AI 解读

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Surprisingly, many microbiome studies have performed both experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, suggesting that an integrative analysis of both datasets could enhance the testing power for these signatures. However, differential experimental biases,…

家系研究中单倍型与疾病关联的局部有效稳健估计AI 解读

Modelling human genetic variation is critical to understanding the genetic basis of complex disease. The Human Genome Project has discovered millions of binary DNA sequence variants, called single nucleotide polymorphisms, and millions more may exist. As coding for proteins takes place along chromosomes, organisation of polymorphisms along each chromosome, the haplotype phase structure, may prove to be most important in discovering genetic variants associated with disease.…

区间删失比例风险模型中基于插补失效时间的推断AI 解读

We propose an approach to the proportional hazards model for interval-censored data in which parameter estimates are obtained by solving estimating equations that are the partial likelihood score equations for the full-data proportional hazards model, averaged over all rankings of imputed failure times consistent with the observed censoring intervals. Imputed failure times are generated using a parametric estimate of the baseline distribution; the parameters of the baseline…

用于分位数同时置信区间的上下界分布AI 解读

We propose a new method for constructing parametric confidence intervals for quantiles of an unknown distribution. These confidence intervals are constructed so that the joint probability that all intervals simultaneously contain their respective percentiles is at least a preset value. Thus we may also use the set of either upper or lower confidence limits to define an upper (or lower) bound distribution function, which we call an upper (or lower) tolerance distribution…

用于分位数同时置信区间的上下界分布AI 解读

We propose a new method for constructing parametric confidence intervals for quantiles of an unknown distribution. These confidence intervals are constructed so that the joint probability that all intervals simultaneously contain their respective percentiles is at least a preset value. Thus we may also use the set of either upper or lower confidence limits to define an upper (or lower) bound distribution function, which we call an upper (or lower) tolerance distribution…

利用暴露概率信息推断暴露与疾病的关联AI 解读

Unconditional and conditional likelihood methods are proposed for modeling odds and odds ratios relating a binary disease variable and an exposure variable that is only known probabilistically (e.g., via measurement of a Surrogate exposure variable), while adjusting appropriately for a vector of covariables. Assuming nondifferential errors, a probabilistic structure is developed that permits analysis of the marginal relation between disease and the Surrogate while…

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