Contributed Papers

Precision Medicine and Machine Learning Methods

Thu, Aug 7, 8:30 AM - 10:20 AM Room CC-201A Music City Center

Section on Statistics in Epidemiology

6 Presentations

A tree-based scan statistic for database studies with time-to-event outcomes

8:35 AM - 8:50 AM

Co-authors: Massimiliano Russo (The Ohio State University), Massimiliano Russo (The Ohio State University), Georg Hahn, Shirley Wang

Data mining Epidemiology Multiple testing Scan statistics Tree variable.

Comparing machine learning to existing risk scores when predicting CVD in type 2 diabetes patients

8:50 AM - 9:05 AM

Co-authors: Elsa Vazquez Arreola (National Institute of Diabetes and Digestive and Kidney Diseases), Emma Stinson (National Institute of Diabetes and Digestive and Kidney Diseases), Elsa Vazquez Arreola (National Institute of Diabetes and Digestive and Kidney Diseases), William Knowler (National Institute of Diabetes and Digestive and Kidney Diseases), Jonathan Krakoff (National Institute of Diabetes and Digestive and Kidney Diseases), Robert Hanson (National Institute of Diabetes and Digestive and Kidney Diseases)

survival outcome prediction model selection model validation Risk prediction

Development of an R Package to Predict Item Responses Accounting for Differential Item Functioning

9:05 AM - 9:20 AM

Co-authors: Bodawatte Gedara Muditha Lakmali (University of Manitoba), Barret Monchka (George and Fay Yee Centre for Healthcare Innovation, University of Manitoba), Bodawatte Gedara Muditha Lakmali (University of Manitoba), Lisa Lix (University of Manitoba)

Model-based recursive partitioning Measurement invariance Item responses Machine-learning Logistic regression

Functional Regression Model with Autocorrelation: Applications to Cancer Mortality Rates

9:20 AM - 9:35 AM

Co-authors: Keshav Pokhrel (University of Michigan-Dearborn), Keshav Pokhrel (University of Michigan-Dearborn)

Functional Data Autocorrelation ARIMA Registries Time Series Regression

Pooled analysis of nested case-control and case-cohort studies for risk assessment and prediction

9:35 AM - 9:50 AM

Co-authors: Jinghan Cui (University of Massachusetts, Amherst), Jinghan Cui (University of Massachusetts, Amherst), Susan Hankinson (School of Public Health and Health Sciences, University of Massachuesetts, Amherst), Jing Qian (University of Massachusetts Amherst)

biomarker data combined analysis conditional likelihood unconditional likelihood logistic regression selection bias

Validity of absolute risk estimates derived from matched case-control studies and population rates

9:50 AM - 10:05 AM

Co-authors: Li-Hsin Chien (National Dong Hwa University), Hsiao-Han Hung (National Health Research Institutes, Taiwan), Li-Hsin Chien (National Dong Hwa University), Ting-Wai Chang (National Health Research Institutes, Taiwan), Hsin-Fang Jiang (National Health Research Institutes, Taiwan), Chao Hsiung (National Health Research Institutes), I-Shou Chang (National Health Research Institutes)

Absolute risk prediction model Matched case-control studies Data synthesis Low-dose computed tomography lung cancer screening