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tistically much more strong than procedures that combine P values or Z scores40. Additionally to these classic frequentist approaches, Bayesian models for meta-analysis may very well be particularly valuable in cancer pharmacogenomics because they enable sequential incorporation of new data as it becomes available, maybe even prior to a clinical trial ends49. Preceding analyses form the prior belief and estimates of association are updated with each and every new information set to produce a posterior belief43,50,51. This strategy has been used to determine threat markers for prostate cancer and colorectal cancer52,53. The incorporation of cancer-specific along with other prospective covariates in cancer pharmacogenomic studies is discussed in BOX 2. If GWASs are combined inside a metaanalysis and if some research contain specific covariates, whereas other folks usually do not, the outcomes should be interpreted very carefully. The prime SNPs from such a meta-analysis are most likely to become those with associations that are largely independent from the covariates41. A further source of heterogeneity among studies inside a meta-analysis could possibly be population differences; SNPs thatNat Rev Genet. Author manuscript; out there in PMC 2014 January 01.Wheeler et al.Pageare associated with a https://britishrestaurantawards.org/members/pansy07africa/activity/282734/ phenotype in all populations are prioritized more than those related in only among the list of populations. Random effects models deal with the possibility of heterogeneity among research greater than fixed effects models: the trade-off is that the normal errors are larger41. Tests of heterogeneity can assist researchers in deciding which model to choose41,43,54. Box 2 Covariates in cancer pharmacogenomics As in any genome-wide association study (GWAS), critical covariates to think about in cancer pharmacogenomics research incorporate age, sex and genetic ancestry, which can be typically estimated by principal elements analysis118. Additionally, various potential confounders certain to cancer drug studies really should be collected when achievable and tested for association with phenotypes of interest. If an association with phenotype is detected, then the variable need to be integrated as a covariate inside the regression models testing for SNP associations. Covariates to think about for inclusion in cancer pharmacogenomics research are listed.NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptCovariate Treatment arm or regimen Cancer subtype Cancer stage Cumulative drug dose Somatic mutations Extra medicines Physique surface location Age Sex AncestryVariable type Discrete Discrete Discrete Continuous Discrete (present or absent) Discrete or continuous (if dose details) Continuous Continuous Discrete Continuous (principal components)An option strategy is usually to incorporate the cumulative dose of a drug each and every patient has received into a phenotype of interest. This approach is similar to survival analysis, and this accounts for censoring within the information. Though survival evaluation models `time to event', this method models `dose to event'. The event could possibly be an adverse occasion, tumour progression or death. Dose-to-event evaluation has been successfully employed to recognize genetic variants connected with paclitaxel-induced sensory peripheral neuropathy22 (TABLE two). In this instance, the phenotype tested was the cumulative dose of paclitaxel that either triggered the very first grade 2 or greater sensory peripheral neuropathy episode or the total dose of paclitaxel that the patient received if no neuropathy was experienced22. Sufferers without having neuropathy are correctly `right-censor