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Mary Beth Terry - One of the best experts on this subject based on the ideXlab platform.
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breast cancer risk assessment across the risk continuum genetic and nongenetic risk factors contributing to Differential Model performance
Breast Cancer Research, 2012Co-Authors: Anne S Quante, Alice S Whittemore, Thomas H Shriver, Konstantin Strauch, Mary Beth TerryAbstract:Introduction: Clinicians use different breast cancer risk Models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used Models that include nongenetic factors - BCRAT, also called Gail Model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick Model, generally used for patients with above-average risk. Methods: We evaluated the performance of the BCRAT and IBIS Models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine Model performance across subgroups of women. For Model calibration, we divided the cohort into quartiles of Model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For Model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs). Results: The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRATand IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort’s 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X4 2 =7 .2,P value 0.13) than BCRAT (HL X4 2 =2 2.0,P value <0.001). The IBIS Model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT Model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative).
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breast cancer risk assessment across the risk continuum genetic and nongenetic risk factors contributing to Differential Model performance
Breast Cancer Research, 2012Co-Authors: Anne S Quante, Alice S Whittemore, Thomas H Shriver, Konstantin Strauch, Mary Beth TerryAbstract:Clinicians use different breast cancer risk Models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used Models that include nongenetic factors - BCRAT, also called Gail Model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick Model, generally used for patients with above-average risk. We evaluated the performance of the BCRAT and IBIS Models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine Model performance across subgroups of women. For Model calibration, we divided the cohort into quartiles of Model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For Model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs). The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X42 = 7.2, P value 0.13) than BCRAT (HL X42 = 22.0, P value <0.001). The IBIS Model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT Model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Models developed using extended family history and genetic data, such as the IBIS Model, also perform well in women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Extending such Models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.
Rune Vejlin - One of the best experts on this subject based on the ideXlab platform.
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estimation of a roy search compensating Differential Model of the labor market
Econometrica, 2020Co-Authors: Christopher Taber, Rune VejlinAbstract:The four most important Models of post-schooling wage determination in economics are almost certainly human capital, the Roy Model, the compensating Differentials Model, and the search Model. All four lead to wage heterogeneity. While separating human capital accumulation from the others is quite common, we know remarkably little about the relative importance of the other three sources of inequality. The key aspect of the Roy Model is comparative advantage in which some workers earn more than others as a result of different skill levels at labor market entry. Workers choose the job for which they achieve the highest level of earnings. By contrast, in a compensating wage Differentials Model a worker is willing to be paid less in order to work on a job that they enjoy more. Thus, workers with identical talent can earn different salaries. Finally, workers may have had poor luck in finding their ideal job. This type of search friction can also lead to heterogeneity in earnings as some workers may work for higher wage firms. In short, one worker may earn more than another a) because he has more talent at labor market entry (Roy Model), b) because he has accu- mulated more human capital while working (human capital), c) because he has chosen more unpleasant job (compensating Differentials), or d) because he has had better luck in finding a good job (search frictions). The goal of this work is to uncover the contribution of these different components to overall earnings inequality.
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estimation of a roy search compensating Differential Model of the labor market
Research Papers in Economics, 2016Co-Authors: Christopher Taber, Rune VejlinAbstract:In this paper we develop a Model capturing key features of the Roy Model, a search Model, compensating Differentials, and human capital accumulation on-the-job. We establish which features of the Model can be non-parametrically identified and which cannot. We estimate the Model and use it to assess the relative contribution of the different factors for overall wage inequality. We find that Roy Model inequality is the most important component accounting for the majority of wage variation. We also demonstrate that there is substantial interaction between the other features – most notably the importance of the job match obtained by search frictions varies from around 9% to around 29% depending on how we account for other features. Compensating Differentials and search are both very important for explaining other features of the data such as the variation in utility. Search is important for turnover, but so is compensating Differentials: 1/3 of all choices between two jobs would have resulted in a different outcome if the worker only cared about wages.
Anne S Quante - One of the best experts on this subject based on the ideXlab platform.
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breast cancer risk assessment across the risk continuum genetic and nongenetic risk factors contributing to Differential Model performance
Breast Cancer Research, 2012Co-Authors: Anne S Quante, Alice S Whittemore, Thomas H Shriver, Konstantin Strauch, Mary Beth TerryAbstract:Introduction: Clinicians use different breast cancer risk Models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used Models that include nongenetic factors - BCRAT, also called Gail Model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick Model, generally used for patients with above-average risk. Methods: We evaluated the performance of the BCRAT and IBIS Models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine Model performance across subgroups of women. For Model calibration, we divided the cohort into quartiles of Model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For Model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs). Results: The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRATand IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort’s 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X4 2 =7 .2,P value 0.13) than BCRAT (HL X4 2 =2 2.0,P value <0.001). The IBIS Model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT Model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative).
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breast cancer risk assessment across the risk continuum genetic and nongenetic risk factors contributing to Differential Model performance
Breast Cancer Research, 2012Co-Authors: Anne S Quante, Alice S Whittemore, Thomas H Shriver, Konstantin Strauch, Mary Beth TerryAbstract:Clinicians use different breast cancer risk Models for patients considered at average and above-average risk, based largely on their family histories and genetic factors. We used longitudinal cohort data from women whose breast cancer risks span the full spectrum to determine the genetic and nongenetic covariates that differentiate the performance of two commonly used Models that include nongenetic factors - BCRAT, also called Gail Model, generally used for patients with average risk and IBIS, also called Tyrer Cuzick Model, generally used for patients with above-average risk. We evaluated the performance of the BCRAT and IBIS Models as currently applied in clinical settings for 10-year absolute risk of breast cancer, using prospective data from 1,857 women over a mean follow-up length of 8.1 years, of whom 83 developed cancer. This cohort spans the continuum of breast cancer risk, with some subjects at lower than average population risk. Therefore, the wide variation in individual risk makes it an interesting population to examine Model performance across subgroups of women. For Model calibration, we divided the cohort into quartiles of Model-assigned risk and compared differences between assigned and observed risks using the Hosmer-Lemeshow (HL) chi-squared statistic. For Model discrimination, we computed the area under the receiver operator curve (AUC) and the case risk percentiles (CRPs). The 10-year risks assigned by BCRAT and IBIS differed (range of difference 0.001 to 79.5). The mean BCRAT- and IBIS-assigned risks of 3.18% and 5.49%, respectively, were lower than the cohort's 10-year cumulative probability of developing breast cancer (6.25%; 95% confidence interval (CI) = 5.0 to 7.8%). Agreement between assigned and observed risks was better for IBIS (HL X42 = 7.2, P value 0.13) than BCRAT (HL X42 = 22.0, P value <0.001). The IBIS Model also showed better discrimination (AUC = 69.5%, CI = 63.8% to 75.2%) than did the BCRAT Model (AUC = 63.2%, CI = 57.6% to 68.9%). In almost all covariate-specific subgroups, BCRAT mean risks were significantly lower than the observed risks, while IBIS risks showed generally good agreement with observed risks, even in the subgroups of women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Models developed using extended family history and genetic data, such as the IBIS Model, also perform well in women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative). Extending such Models to include additional nongenetic information may improve performance in women across the breast cancer risk continuum.
Dmitry Valerievich Alexandrov - One of the best experts on this subject based on the ideXlab platform.
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nucleation and crystal growth kinetics during solidification the role of crystallite withdrawal rate and external heat and mass sources
Chemical Engineering Science, 2014Co-Authors: Dmitry Valerievich AlexandrovAbstract:Abstract A complete analytical solution of the integro-Differential Model describing the transient nucleation and growth of the crystals at the intermediate stage of phase transitions is constructed. The roles of external heat/mass sources appearing in the balance equations and the crystallite withdrawal rate entering in the Fokker–Planck equation are detailed. An exact analytical solution of the Fokker–Planck equation is found for arbitrary nucleation mechanisms and growth kinetics. Two important cases of the Weber–Volmer–Frenkel–Zel׳dovich and Meirs kinetics are considered in some detail. A non-linear time-dependent integral equation with memory kernel for the metastability level is analytically solved on the basis of the saddle-point method for the Laplace integral in the case of mixed kinetic-diffusion regime of crystal growth, which is of frequent occurrence. It is shown that the desupercooling/desupersaturation rate decreases with increasing the crystal withdrawal rate and intensities of external sources. The density distribution function becomes more and more broad with time. In addition, this function increases with decreasing the crystallite withdrawal rate and with increasing intensities of external sources.
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on the theory of transient nucleation at the intermediate stage of phase transitions
Physics Letters A, 2014Co-Authors: Dmitry Valerievich AlexandrovAbstract:Abstract The evolution of a system of growing aggregates in a macroscopically homogeneous medium with account of both the reduction in metastability and the continuing initiation of new nuclei is studied. The corresponding integro-Differential Model describing the intermediate stage of phase transitions is solved analytically for arbitrary nucleation kinetics and growth rates of nuclei. An exact solution of the Fokker–Planck equation is found with allowance for the diffusivity along the axis of nucleus radii. In limiting cases of purely kinetic and mixed kinetic-diffusion rates of crystal growth for a special form of diffusivity, the obtained solutions transform to earlier known expressions.
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nucleation and particle growth with fluctuating rates at the intermediate stage of phase transitions in metastable systems
Proceedings of The Royal Society A: Mathematical Physical and Engineering Sciences, 2014Co-Authors: Dmitry Valerievich Alexandrov, I G NizovtsevaAbstract:An exact analytical solution of an integro-Differential Model describing the transient nucleation of solid particles (nuclei) and their growth with fluctuating rates at the intermediate stage of bulk phase transitions in metastable systems is constructed. Two important cases of the Weber–Volmer–Frenkel–Zel'dovich and Mier nucleation kinetics are detailed for supercooled melts and supersaturated solutions.
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nucleation kinetics and crystal growth with fluctuating rates at the intermediate stage of phase transitions
Modelling and Simulation in Materials Science and Engineering, 2014Co-Authors: Dmitry Valerievich Alexandrov, A. P. MalyginAbstract:Crystal growth kinetics accompanied by particle growth with fluctuating rates at the intermediate stage of phase transitions is analyzed theoretically. The integro-Differential Model of governing equations is solved analytically for size-independent growth rates and arbitrary dependences of the nucleation frequency on supercooling/supersaturation. Two important cases of Weber–Volmer–Frenkel–Zel'dovich and Mier nucleation kinetics are detailed. A Fokker–Plank type equation for the crystal-size density distribution function is solved explicitly.
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transient nucleation kinetics of crystal growth at the intermediate stage of bulk phase transitions
Journal of Physics A, 2013Co-Authors: Dmitry Valerievich Alexandrov, A. P. MalyginAbstract:A complete analytical solution of an integro-Differential Model describing the transient nucleation of solid particles and their subsequent growth at the intermediate stage of phase transitions in metastable systems is constructed. A Fokker–Plank type equation for the density distribution function is solved exactly for arbitrary nucleation kinetics. A non-linear integral equation with memory kernel connecting the density distribution function and the system supercooling/supersaturation is analytically solved on the basis of the saddle-point method for the Laplace integral. The analytical solution obtained shows that the process at the intermediate stage is divided into three phases: initially the high rate nucleation stage occurs, then this process is accompanied by the particle growth reducing the level of metastability, and finally the mechanism of particle coarsening becomes predominant.
Christopher Taber - One of the best experts on this subject based on the ideXlab platform.
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estimation of a roy search compensating Differential Model of the labor market
Econometrica, 2020Co-Authors: Christopher Taber, Rune VejlinAbstract:The four most important Models of post-schooling wage determination in economics are almost certainly human capital, the Roy Model, the compensating Differentials Model, and the search Model. All four lead to wage heterogeneity. While separating human capital accumulation from the others is quite common, we know remarkably little about the relative importance of the other three sources of inequality. The key aspect of the Roy Model is comparative advantage in which some workers earn more than others as a result of different skill levels at labor market entry. Workers choose the job for which they achieve the highest level of earnings. By contrast, in a compensating wage Differentials Model a worker is willing to be paid less in order to work on a job that they enjoy more. Thus, workers with identical talent can earn different salaries. Finally, workers may have had poor luck in finding their ideal job. This type of search friction can also lead to heterogeneity in earnings as some workers may work for higher wage firms. In short, one worker may earn more than another a) because he has more talent at labor market entry (Roy Model), b) because he has accu- mulated more human capital while working (human capital), c) because he has chosen more unpleasant job (compensating Differentials), or d) because he has had better luck in finding a good job (search frictions). The goal of this work is to uncover the contribution of these different components to overall earnings inequality.
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estimation of a roy search compensating Differential Model of the labor market
Research Papers in Economics, 2016Co-Authors: Christopher Taber, Rune VejlinAbstract:In this paper we develop a Model capturing key features of the Roy Model, a search Model, compensating Differentials, and human capital accumulation on-the-job. We establish which features of the Model can be non-parametrically identified and which cannot. We estimate the Model and use it to assess the relative contribution of the different factors for overall wage inequality. We find that Roy Model inequality is the most important component accounting for the majority of wage variation. We also demonstrate that there is substantial interaction between the other features – most notably the importance of the job match obtained by search frictions varies from around 9% to around 29% depending on how we account for other features. Compensating Differentials and search are both very important for explaining other features of the data such as the variation in utility. Search is important for turnover, but so is compensating Differentials: 1/3 of all choices between two jobs would have resulted in a different outcome if the worker only cared about wages.