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− | + | A dozen To match assessment over different scenarios, a subgroup size |Ersus| regarding 30% ended up being specific around various subgroup explanations. Subgroup membership rights for each patient was firm while using pursuing a few POC situations (be aware that Alcoholics anonymous means the most important homozygote): Superior therapy impact can be driven by the individual SNP located in a great LD obstruct: Si={1?if?Gi,rs11790326=AB,BB0?if?Gi,rs11790326=AA Enhanced treatment effect is driven by a single SNP not in LD with other SNPs (ie, maximum pairwise r2 of 0.2): Si={1?if?Gi,rs199894164=AB,BB0?if?Gi,rs199894164=AA Enhanced treatment effect is driven by two SNPs located in the same gene (but in different LD blocks): Si={0??if?Gi,rs4100654=AA?or?if?Gi,rs12347784=AA1otherwise Note that the MAFs of SNPs rs11790326, rs199894164, [http://en.wikipedia.org/wiki/TRIB1 TRIB1] rs4100654, [http://www.selleckchem.com/products/ON-01910.html selleck kinase inhibitor] and rs12347784 are 0.19, 0.19, 0.08, and 0.09, respectively, generating subgroup sizes of approximately 30% for all three scenarios. Additional considerations and performance metrics For each of the 30 scenarios outlined in the sections titled, ��Simulation of phenotypic data�� and ��Simulation of genotypic data and assignment of patients to genetically defined subgroups��, 1,000 datasets were simulated and randomly combined to generate 500 pairs of trials representing a discovery trial and a replication trial. For each trial, P-values for SSAT, SS-RBAT, and VC-RBAT were recorded for all variants in the ABCA1 region or the entire [http://www.selleckchem.com/products/Vandetanib.html see more] ABCA1 region, respectively. For computational efficiency, P-values for the remaining 24 genes and 3,546 independent variants across these genes were drawn from a uniform distribution, since only variants in gene ABCA1 provided a genetic contribution to the treatment effect. For the discovery trial, multiplicity adjustment was performed using a Bonferroni correction for 25 tests for VC-RBAT and 3,800 effective tests for SSAT (after consideration to dependency among SNPs). Significance was determined using an alpha-level ��1 of 0.05 or 0.20 (as an alternative, relaxed threshold in the case where more risk is acceptable at the discovery stage). For the replication trial, the multiplicity adjustment was performed using a Bonferroni correction, adjusting for the number of genes/SNPs that were significant at alpha-level ��1 in the discovery trial for VC-RBAT or SSAT, respectively. Significance in the replication trial was determined using an alpha-level ��2 of 0.05. |
Version du 14 février 2017 à 08:31
A dozen To match assessment over different scenarios, a subgroup size |Ersus| regarding 30% ended up being specific around various subgroup explanations. Subgroup membership rights for each patient was firm while using pursuing a few POC situations (be aware that Alcoholics anonymous means the most important homozygote): Superior therapy impact can be driven by the individual SNP located in a great LD obstruct: Si={1?if?Gi,rs11790326=AB,BB0?if?Gi,rs11790326=AA Enhanced treatment effect is driven by a single SNP not in LD with other SNPs (ie, maximum pairwise r2 of 0.2): Si={1?if?Gi,rs199894164=AB,BB0?if?Gi,rs199894164=AA Enhanced treatment effect is driven by two SNPs located in the same gene (but in different LD blocks): Si={0??if?Gi,rs4100654=AA?or?if?Gi,rs12347784=AA1otherwise Note that the MAFs of SNPs rs11790326, rs199894164, TRIB1 rs4100654, selleck kinase inhibitor and rs12347784 are 0.19, 0.19, 0.08, and 0.09, respectively, generating subgroup sizes of approximately 30% for all three scenarios. Additional considerations and performance metrics For each of the 30 scenarios outlined in the sections titled, ��Simulation of phenotypic data�� and ��Simulation of genotypic data and assignment of patients to genetically defined subgroups��, 1,000 datasets were simulated and randomly combined to generate 500 pairs of trials representing a discovery trial and a replication trial. For each trial, P-values for SSAT, SS-RBAT, and VC-RBAT were recorded for all variants in the ABCA1 region or the entire see more ABCA1 region, respectively. For computational efficiency, P-values for the remaining 24 genes and 3,546 independent variants across these genes were drawn from a uniform distribution, since only variants in gene ABCA1 provided a genetic contribution to the treatment effect. For the discovery trial, multiplicity adjustment was performed using a Bonferroni correction for 25 tests for VC-RBAT and 3,800 effective tests for SSAT (after consideration to dependency among SNPs). Significance was determined using an alpha-level ��1 of 0.05 or 0.20 (as an alternative, relaxed threshold in the case where more risk is acceptable at the discovery stage). For the replication trial, the multiplicity adjustment was performed using a Bonferroni correction, adjusting for the number of genes/SNPs that were significant at alpha-level ��1 in the discovery trial for VC-RBAT or SSAT, respectively. Significance in the replication trial was determined using an alpha-level ��2 of 0.05.