This multi-disciplinary effort by biostatisticians, radiologists, and medical physicists aims to develop innovative statistical methods for evaluation and validation of new and low cost diagnostic modalities.
Specific Aims are (1) statistical methods for determining non-inferiority of diagnostic techniques; (2) statistical design and analysis methods for rapid and accurate evaluation of the chronological association and treatment efficacy of new radiological markers with disease outcome; (3) applying the statistical methods to clinical research and developing user-friendly software.
Specific Aim 1 studies evaluation of non- inferiority for a new diagnostic test based on the receiver operating characteristics curve under various conditions (missing data, mixed data types, and clustered data), effects of covariates (non-inferiority after adjustment of covariates and identification of subgroups within which the new test is inferior, superior or equivalent), and Bayesian utility models.
Specific Aim 2 studies a new cross-sectional case-control short- term follow-up (CSCCSTFU) design and analysis methods to estimate the chronological odds ratio of a new diagnostic test for a rare outcome. It will also propose methods to estimate the lower boundary of treatment efficacy for patients diagnosed by the new test. Mathematical theory and simulation studies will be used to understand the conditions in which the estimated odds ratio of the CSCCSTFU design can be generalized.
Specific Aim 3 will apply results from Specific Aim 1to data from the Study of Osteoporotic Fractures and the Osteoporosis and Ultrasound Study to test whether and when quantitative ultrasound, peripheral bone mineral density (BMD), and biochemical bone markerscan serve as non-inferior or equivalent techniques to BMD of hip and spine for assessing osteoporotic fracture risks. It will also use results of Specific Aim 2 to determine whether the volumetric breast density and structural parameters of the new single X-ray absorptiometry technique can prospectively predict breast cancer in an on-going study of 15,000 women. An advisory committee of internationally recognized leaders in osteoporosis and breast cancer research will advise the research team on outreach to physicians and opportunities for ancillary studies. Research results will be disseminated through publication of software, research and tutorial papers in both clinical and statistical journals and by influencing position papers of professional organizations.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB004079-03
Application #
7392153
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Cohen, Zohara
Project Start
2006-04-01
Project End
2009-12-31
Budget Start
2008-04-01
Budget End
2009-12-31
Support Year
3
Fiscal Year
2008
Total Cost
$240,757
Indirect Cost
Name
University of California San Francisco
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
Li, Caixia; Glüer, Claus-C; Eastell, Richard et al. (2012) Tree-structured subgroup analysis of receiver operating characteristic curves for diagnostic tests. Acad Radiol 19:1529-36
Jin, Hua; Lu, Ying (2011) Cost-saving tree-structured survival analysis for hip fracture of study of osteoporotic fractures data. Med Decis Making 31:299-307
Kornak, John; Lu, Ying (2011) Bayesian decision analysis for choosing between diagnostic/prognostic prediction procedures. Stat Interface 4:27-36
Li, Caixia; Lu, Ying (2010) Evaluating the improvement in diagnostic utility from adding new predictors. Biom J 52:417-35
Jin, Hua; Lu, Ying (2009) Permutation test for non-inferiority of the linear to the optimal combination of multiple tests. Stat Probab Lett 79:664-669
Jin, Hua; Lu, Ying (2009) A non-inferiority test of areas under two parametric ROC curves. Contemp Clin Trials 30:375-9
Koziol, James A; Jia, Zhenyu (2009) The concordance index C and the Mann-Whitney parameter Pr(X>Y) with randomly censored data. Biom J 51:467-74
Jin, Hua; Lu, Ying (2009) The Optimal Linear Combination of Multiple Predictors Under the Generalized Linear Models. Stat Probab Lett 79:2321-2327
Shepherd, John A; Morgan, Sarah L; Lu, Ying (2008) Comparing BMD results between two similar DXA systems using the generalized least significant change. J Clin Densitom 11:237-42
Jin, Hua; Lu, Ying (2008) A procedure for determining whether a simple combination of diagnostic tests may be noninferior to the theoretical optimum combination. Med Decis Making 28:909-16

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