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Articles 1 - 9 of 9
Full-Text Articles in Biostatistics
The Use Of Imputed Values In The Meta-Analysis Of Genome-Wide Association Studies., Shuo Jiao, Li Hsu, Carolyn Hutter, Ulrike Peters
The Use Of Imputed Values In The Meta-Analysis Of Genome-Wide Association Studies., Shuo Jiao, Li Hsu, Carolyn Hutter, Ulrike Peters
Shuo Jiao
In genome-wide association studies (GWAS), it is a common practice to impute the genotypes of untyped single nucleotide polymorphism (SNP) by exploiting the linkage disequilibrium structure among SNPs. The use of imputed genotypes improves genome coverage and makes it possible to perform meta-analysis combining results from studies genotyped on different platforms. A popular way of using imputed data is the "expectation-substitution" method, which treats the imputed dosage as if it were the true genotype. In current practice, the estimates given by the expectation-substitution method are usually combined using inverse variance weighting (IVM) scheme in meta-analysis. However, the IVM is not …
Meta-Analysis Of New Genome-Wide Association Studies Of Colorectal Cancer Risk.
Meta-Analysis Of New Genome-Wide Association Studies Of Colorectal Cancer Risk.
Shuo Jiao
Colorectal cancer is the second leading cause of cancer death in developed countries. Genome-wide association studies (GWAS) have successfully identified novel susceptibility loci for colorectal cancer. To follow up on these findings, and try to identify novel colorectal cancer susceptibility loci, we present results for GWAS of colorectal cancer (2,906 cases, 3,416 controls) that have not previously published main associations. Specifically, we calculated odds ratios and 95% confidence intervals using log-additive models for each study. In order to improve our power to detect novel colorectal cancer susceptibility loci, we performed a meta-analysis combining the results across studies. We selected the …
Social Marketing, Stages Of Change, And Public Health Smoking Interventions, Paula Diehr
Social Marketing, Stages Of Change, And Public Health Smoking Interventions, Paula Diehr
Paula Diehr
As a "thought experiment," the authors used a modified stages of change model for smoking to define homogeneous segments within various hypothetical populations. The authors then estimated the population effect of public health interventions that targeted the different segments. Under most assumptions, interventions that emphasized primary and secondary prevention, by targeting the Never Smoker, Maintenance, or Action segments, resulted in the highest nonsmoking life expectancy. This result is consistent with both social marketing and public health principles. Although the best thing for an individual smoker is to stop smoking, the greatest public health benefit is achieved by interventions that target …
Prevalence Of Piscine Myocarditis Virus (Pmcv) In Marine Fish Species, Torstein Tengs Dr.
Prevalence Of Piscine Myocarditis Virus (Pmcv) In Marine Fish Species, Torstein Tengs Dr.
Dr. Torstein Tengs
No abstract.
Compound Treatments, Transportability, And The Structural Causal Model: The Power And Simplicity Of Causal Graphs., Maya Petersen
Compound Treatments, Transportability, And The Structural Causal Model: The Power And Simplicity Of Causal Graphs., Maya Petersen
Maya Petersen
No abstract provided.
Generalized Benjamini-Hochberg Procedures Using Spacings, Debashis Ghosh
Generalized Benjamini-Hochberg Procedures Using Spacings, Debashis Ghosh
Debashis Ghosh
For the problem of multiple testing, the Benjamini-Hochberg (B-H) procedure has become a very popular method in applications. We show how the B-H procedure can be interpreted as a test based on the spacings corresponding to the p-value distributions. Using this equivalence, we develop a class of generalized B-H procedures that maintain control of the false discovery rate in finite-samples. We also consider the effect of correlation on the procedure; simulation studies are used to illustrate the methodology.
Software For Assumption Weighting For Meta-Analysis Of Genomic Data, Debashis Ghosh, Yihan Li
Software For Assumption Weighting For Meta-Analysis Of Genomic Data, Debashis Ghosh, Yihan Li
Debashis Ghosh
This is the software that accompanies Li and Ghosh, "Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data."
A Causal Framework For Surrogate Endpoints With Semi-Competing Risks Data, Debashis Ghosh
A Causal Framework For Surrogate Endpoints With Semi-Competing Risks Data, Debashis Ghosh
Debashis Ghosh
In this note, we address the problem of surrogacy using a causal modelling framework that differs substantially from the potential outcomes model that pervades the biostatistical literature. The framework comes from econometrics and conceptualizes direct effects of the surrogate endpoint on the true endpoint. While this framework can incorporate the so-called semi-competing risks data structure, we also derive a fundamental non-identifiability result. Relationships to existing causal modelling frameworks are also discussed.
Propensity Score Modelling In Observational Studies Using Dimension Reduction Methods, Debashis Ghosh
Propensity Score Modelling In Observational Studies Using Dimension Reduction Methods, Debashis Ghosh
Debashis Ghosh
Conditional independence assumptions are very important in causal inference modelling as well as in dimension reduction methodologies. These are two very strikingly different statistical literatures, and we study links between the two in this article. The concept of covariate sufficiency plays an important role, and we provide theoretical justication when dimension reduction and partial least squares methods will allow for valid causal inference to be performed. The methods are illustrated with application to a medical study and to simulated data.