@article {849, title = {The sensitivity and specificity of markers for event times.}, journal = {Biostatistics}, volume = {7}, year = {2006}, month = {2006 Apr}, pages = {182-97}, abstract = {

The statistical literature on assessing the accuracy of risk factors or disease markers as diagnostic tests deals almost exclusively with settings where the test, Y, is measured concurrently with disease status D. In practice, however, disease status may vary over time and there is often a time lag between when the marker is measured and the occurrence of disease. One example concerns the Framingham risk score (FR-score) as a marker for the future risk of cardiovascular events, events that occur after the score is ascertained. To evaluate such a marker, one needs to take the time lag into account since the predictive accuracy may be higher when the marker is measured closer to the time of disease occurrence. We therefore consider inference for sensitivity and specificity functions that are defined as functions of time. Semiparametric regression models are proposed. Data from a cohort study are used to estimate model parameters. One issue that arises in practice is that event times may be censored. In this research, we extend in several respects the work by Leisenring et al. (1997) that dealt only with parametric models for binary tests and uncensored data. We propose semiparametric models that accommodate continuous tests and censoring. Asymptotic distribution theory for parameter estimates is developed and procedures for making statistical inference are evaluated with simulation studies. We illustrate our methods with data from the Cardiovascular Health Study, relating the FR-score measured at enrollment to subsequent risk of cardiovascular events.

}, keywords = {Aged, Aged, 80 and over, Biomarkers, Biometry, Cardiovascular Diseases, Female, Humans, Male, Models, Statistical, Risk Factors, ROC Curve, Sensitivity and Specificity, Time Factors}, issn = {1465-4644}, doi = {10.1093/biostatistics/kxi047}, author = {Cai, Tianxi and Pepe, Margaret Sullivan and Zheng, Yingye and Lumley, Thomas and Jenny, Nancy Swords} } @article {1281, title = {Comparing costs associated with risk stratification rules for t-year survival.}, journal = {Biostatistics}, volume = {12}, year = {2011}, month = {2011 Oct}, pages = {597-609}, abstract = {

Accurate risk prediction is an important step in developing optimal strategies for disease prevention and treatment. Based on the predicted risks, patients can be stratified to different risk categories where each category corresponds to a particular clinical intervention. Incorrect or suboptimal interventions are likely to result in unnecessary financial and medical consequences. It is thus essential to account for the costs associated with the clinical interventions when developing and evaluating risk stratification (RS) rules for clinical use. In this article, we propose to quantify the value of an RS rule based on the total expected cost attributed to incorrect assignment of risk groups due to the rule. We have established the relationship between cost parameters and optimal threshold values used in the stratification rule that minimizes the total expected cost over the entire population of interest. Statistical inference procedures are developed for evaluating and comparing given RS rules and examined through simulation studies. The proposed procedures are illustrated with an example from the Cardiovascular Health Study.

}, keywords = {Biostatistics, Coronary Disease, Cost-Benefit Analysis, Costs and Cost Analysis, Humans, Models, Statistical, Risk Assessment, Risk Factors, Time Factors}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxr001}, author = {Cai, Tianxi and Tian, Lu and Lloyd-Jones, Donald M} } @article {9916, title = {Robust Automated Harmonization of Heterogeneous Data Through Ensemble Machine Learning: Algorithm Development and Validation Study.}, journal = {JMIR Med Inform}, volume = {13}, year = {2025}, month = {2025 Jan 22}, pages = {e54133}, abstract = {

BACKGROUND: Cohort studies contain rich clinical data across large and diverse patient populations and are a common source of observational data for clinical research. Because large scale cohort studies are both time and resource intensive, one alternative is to harmonize data from existing cohorts through multicohort studies. However, given differences in variable encoding, accurate variable harmonization is difficult.

OBJECTIVE: We propose SONAR (Semantic and Distribution-Based Harmonization) as a method for harmonizing variables across cohort studies to facilitate multicohort studies.

METHODS: SONAR used semantic learning from variable descriptions and distribution learning from study participant data. Our method learned an embedding vector for each variable and used pairwise cosine similarity to score the similarity between variables. This approach was built off 3 National Institutes of Health cohorts, including the Cardiovascular Health Study, the Multi-Ethnic Study of Atherosclerosis, and the Women{\textquoteright}s Health Initiative. We also used gold standard labels to further refine the embeddings in a supervised manner.

RESULTS: The method was evaluated using manually curated gold standard labels from the 3 National Institutes of Health cohorts. We evaluated both the intracohort and intercohort variable harmonization performance. The supervised SONAR method outperformed existing benchmark methods for almost all intracohort and intercohort comparisons using area under the curve and top-k accuracy metrics. Notably, SONAR was able to significantly improve harmonization of concepts that were difficult for existing semantic methods to harmonize.

CONCLUSIONS: SONAR achieves accurate variable harmonization within and between cohort studies by harnessing the complementary strengths of semantic learning and variable distribution learning.

}, keywords = {Algorithms, Cohort Studies, Female, Humans, Machine Learning, Semantics}, issn = {2291-9694}, doi = {10.2196/54133}, author = {Yang, Doris and Zhou, Doudou and Cai, Steven and Gan, Ziming and Pencina, Michael and Avillach, Paul and Cai, Tianxi and Hong, Chuan} }