Robust Bayesian sensitivity analysis for case-control studies with uncertain exposure misclassification probabilities

Exposure misclassification in case–control studies leads to bias in odds ratio estimates. There has been considerable interest recently to account for misclassification in estimation so as to adjust for bias as well as more accurately quantify uncertainty. These methods require users to elicit suitable values or prior distributions for the misclassification probabilities. In the event where exposure misclassification is highly uncertain, these methods are of limited use, because the resulting posterior uncertainty intervals tend to be too wide to be informative. Posterior inference also becomes very dependent on the subjectively elicited prior distribution. In this paper, we propose an alternative “robust Bayesian” approach, where instead of eliciting prior distributions for the misclassification probabilities, a feasible region is given. The extrema of posterior inference within the region are sought using an inequality constrained optimization algorithm. This method enables sensitivity analyses to be conducted in a useful way as we do not need to restrict all of our unknown parameters to fixed values, but can instead consider ranges of values at a time.published_or_final_versio

Rapid Identification of Pathogenic Variants in Two Cases of Charcot-Marie-Tooth Disease by Gene-Panel Sequencing

published_or_final_versio

Choosing a robust healthcare demand projection model for healthcare manpower planning and projection

Conference Theme: Medical Leadership and Management - Global Outlook and Local LandscapeAbstract & poster presentatio

Projecting the doctor workforce in Hong Kong

Abstract & poster presentationConference Theme: Medical Leadership and Management - Global Outlook and Local Landscap

An improved polygenic score for prediction in genome-wide association studies

Poste

Whole Exome Sequencing versus Target Gene Panels for Evaluation of Isolated CaseProbands with Dilated and Hypertrophic Cardiomyopathy

Background: Targeted Next Generation Sequencing of gene panels has become a popular tool for the genetic diagnosis of hypertrophic (HCM) and dilated cardiomyopathy (DCM). As the number of genes associated with HCM and DCM grows, however, Whole Exome Sequencing (WES) may become a suitable alternative. The coverage of WES on genes and variants related to HCM and DCM is unknown, however. Objective: We aimed to assess the coverage of WES on genes and variants related to HCM and DCM, as compared to several commercial gene panels used for HCM and DCM diagnosis. We also reported on a case study of using WES for genetic diagnosis in a group of 40 HCM and DCM patients. Methods: We followed current standards in carrying out WES on 40 HCM and DCM patients recruited locally, and examined their coverage of genes and a set of potentially pathogenic variants curated from Clinvar. Genetic diagnosis of the 40 patients was carried out using a procedure developed based on the ACMG guidelines. Results: Although the coverage of WES on genes and potentially pathogenic variants were high for most genes and variants, the coverage appeared inadequate for a number of genes included in target gene panels forclosely related to HCM and DCM, including TNNT2 and TNNI3. Within our study, 6 pathogenic or likely pathogenic variants were found among 14 HCM patients, and 4 pathogenic or likely pathogenic variants were found among the 26 DCM patients. Conclusion: Our study highlighted potential inadequacy in the coverage of current WES on variants and genes related to HCM and DCM

Whole Exome Sequencing versus Target Gene Panels for Evaluation of Isolated CaseProbands with Dilated and Hypertrophic Cardiomyopathy

Background: Targeted Next Generation Sequencing of gene panels has become a popular tool for the genetic diagnosis of hypertrophic (HCM) and dilated cardiomyopathy (DCM). As the number of genes associated with HCM and DCM grows, however, Whole Exome Sequencing (WES) may become a suitable alternative. The coverage of WES on genes and variants related to HCM and DCM is unknown, however. Objective: We aimed to assess the coverage of WES on genes and variants related to HCM and DCM, as compared to several commercial gene panels used for HCM and DCM diagnosis. We also reported on a case study of using WES for genetic diagnosis in a group of 40 HCM and DCM patients. Methods: We followed current standards in carrying out WES on 40 HCM and DCM patients recruited locally, and examined their coverage of genes and a set of potentially pathogenic variants curated from Clinvar. Genetic diagnosis of the 40 patients was carried out using a procedure developed based on the ACMG guidelines. Results: Although the coverage of WES on genes and potentially pathogenic variants were high for most genes and variants, the coverage appeared inadequate for a number of genes included in target gene panels forclosely related to HCM and DCM, including TNNT2 and TNNI3. Within our study, 6 pathogenic or likely pathogenic variants were found among 14 HCM patients, and 4 pathogenic or likely pathogenic variants were found among the 26 DCM patients. Conclusion: Our study highlighted potential inadequacy in the coverage of current WES on variants and genes related to HCM and DCM