tailieunhanh - báo cáo khoa học: "Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability | Mihaescu et al. Genome Medicine 2011 3 51 http content 3 7 51 RESEARCH Open Access Predictive genetic testing for the identification of high-risk groups a simulation study on the impact of predictive ability Raluca Mihaescu1 Ramal Moonesinghe2 Muin J Khoury3 and A Cecile JW Janssens1 Abstract Background Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models. Methods We assessed sensitivity specificity and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population the frequency of the high-risk group and the discriminative accuracy of the risk model as assessed by the area under the receiver-operating characteristic curve AUC . In a simulation study we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants. Results We show that when the frequency of the high-risk group was lower than the disease frequency positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency sensitivity was high but positive predictive value remained low. When both frequencies were equal both positive predictive value and sensitivity increased with increasing AUC but higher AUC was needed to maximize both measures. Conclusions The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in