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- Generalized Linear Model (2)
- Adjacency matrix; disease mapping; epidemiology; Markov processes (1)
- Area under the receiver operating characteristic curve; C-statistic; Cox's regression; Gaussian process; Integrated discriminiation improvement; Improvement in the area under the curve; Risk prediction (1)
- Biomarker; clinical outcome; diagnosis; incremental prediction accuracy; K-fold cross validation; prediction; subgroup analysis (1)
- Cardiovascular diseases; Cox's model; nonparametric functional estimation; risk index; ROC analysis; survival analysis (1)
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- Coronary heart disease; nonparametric functional estimation; risk factors/markers; pointwise and simultaneous confidence interval; subgroup analysis (1)
- Cross-validation; HIV-infection; Nonparametric function estimation; Personalized medicine; Subgroup analysis (1)
- Cumulative Residual (1)
- Diagnostic Accuracy (1)
- Diagnostic accuracy; generalized linear model; model checking (1)
- Infectious disease epidemics; branching processes; basic reproductive number; serial interval (1)
- Mixed Model (1)
- Model Checking (1)
- PSA (1)
- Predicted Duration (1)
- ROC Regression (1)
- Testosterone (1)
Articles 1 - 11 of 11
Full-Text Articles in Medicine and Health Sciences
Studying The Optimal Scheduling For Controlling Prostate Cancer Under Intermittent Androgen Suppression, Sunil K. Dhar, Hans R. Chaudhry, Bruce G. Bukiet, Zhiming Ji, Nan Gao, Thomas W. Findley
Studying The Optimal Scheduling For Controlling Prostate Cancer Under Intermittent Androgen Suppression, Sunil K. Dhar, Hans R. Chaudhry, Bruce G. Bukiet, Zhiming Ji, Nan Gao, Thomas W. Findley
Harvard University Biostatistics Working Paper Series
This retrospective study shows that the majority of patients’ correlations between PSA and Testosterone during the on-treatment period is at least 0.90. Model-based duration calculations to control PSA levels during off-treatment are provided. There are two pairs of models. In one pair, the Generalized Linear Model and Mixed Model are both used to analyze the variability of PSA at the individual patient level by using the variable “Patient ID” as a repeated measure. In the second pair, Patient ID is not used as a repeated measure but additional baseline variables are included to analyze the variability of PSA.
Comparing Risk Scoring Systems Beyond The Roc Paradigm In Survival Analysis, Hajime Uno, Lu Tian, Tianxi Cai, Isaac S. Kohane, L. J. Wei
Comparing Risk Scoring Systems Beyond The Roc Paradigm In Survival Analysis, Hajime Uno, Lu Tian, Tianxi Cai, Isaac S. Kohane, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei
Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Evaluating Subject-Level Incremental Values Of New Markers For Risk Classification Rule, Tianxi Cai, Lu Tian, Donald M. Lloyd-Jones, L. J. Wei
Evaluating Subject-Level Incremental Values Of New Markers For Risk Classification Rule, Tianxi Cai, Lu Tian, Donald M. Lloyd-Jones, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
Identifying Patients Who Need Additional Biomarkers For Better Prediction Of Health Outcome Or Diagnosis Of Clinical Phenotype, Lu Tian, Tianxi Cai, L. J. Wei
Identifying Patients Who Need Additional Biomarkers For Better Prediction Of Health Outcome Or Diagnosis Of Clinical Phenotype, Lu Tian, Tianxi Cai, L. J. Wei
Harvard University Biostatistics Working Paper Series
No abstract provided.
A Likelihood Based Method For Real Time Estimation Of The Serial Interval And Reproductive Number Of An Epidemic, Laura Forsberg White, Marcello Pagano
A Likelihood Based Method For Real Time Estimation Of The Serial Interval And Reproductive Number Of An Epidemic, Laura Forsberg White, Marcello Pagano
Harvard University Biostatistics Working Paper Series
No abstract provided.
Spatio-Temporal Analysis Of Areal Data And Discovery Of Neighborhood Relationships In Conditionally Autoregressive Models, Subharup Guha, Louise Ryan
Spatio-Temporal Analysis Of Areal Data And Discovery Of Neighborhood Relationships In Conditionally Autoregressive Models, Subharup Guha, Louise Ryan
Harvard University Biostatistics Working Paper Series
No abstract provided.
Regression Analysis For The Partial Area Under The Roc Curve, Tianxi Cai, Lori E. Dodd
Regression Analysis For The Partial Area Under The Roc Curve, Tianxi Cai, Lori E. Dodd
Harvard University Biostatistics Working Paper Series
No abstract provided.
Model Checking For Roc Regression Analysis, Tianxi Cai, Yingye Zheng
Model Checking For Roc Regression Analysis, Tianxi Cai, Yingye Zheng
Harvard University Biostatistics Working Paper Series
The Receiver Operating Characteristic (ROC) curve is a prominent tool for characterizing the accuracy of continuous diagnostic test. To account for factors that might invluence the test accuracy, various ROC regression methods have been proposed. However, as in any regression analysis, when the assumed models do not fit the data well, these methods may render invalid and misleading results. To date practical model checking techniques suitable for validating existing ROC regression models are not yet available. In this paper, we develop cumulative residual based procedures to graphically and numerically assess the goodness-of-fit for some commonly used ROC regression models, and …
The Sensitivity And Specificity Of Markers For Event Times, Tianxi Cai, Margaret S. Pepe, Thomas Lumley, Yingye Zheng, Nancy Swords Jenny
The Sensitivity And Specificity Of Markers For Event Times, Tianxi Cai, Margaret S. Pepe, Thomas Lumley, Yingye Zheng, Nancy Swords Jenny
Harvard University Biostatistics Working Paper Series
No abstract provided.