Accurate risk assessment and prediction of treatment responses are essential in health care. The potential clinical and financial consequences associated with incorrect assignment of prognostic groups signify the need for reliable prognostic indices and the rigorous evaluation of their accuracy. For complex diseases, any single marker is often inadequate for precise prediction. With dramatically increased availability of new prognostic markers, it is now possible to improve prognostic accuracy by combining information from several markers. This gives rise to the need for statistical approaches to the optimal usage of information from multiple sources to improve disease management. Our proposal aims to develop procedures to address this need. In studies designed to develop prognostic classifiers, markers are often measured at baseline and patients are followed over time for the occurrence of clinical conditions. Since the risk for the disease occurrence may change over time, the time domain must be incorporated when developing prognostic classifiers. Another challenge that arises is that the event times are not always observable due to censoring. Current statistical literature for analyzing event time data focuses primarily on model based methods and their validity relies on the model assumption. Such assumptions may not hold in practice, which may lead to biased or invalid predictions. In this proposal, we consider robust approaches to the development and evaluation of prognostic classifiers. We will focus on the following three aims.
In Aim 1, we will develop robust methods for constructing an optimal composite score based on several markers.
In Aim 2, we will evaluate and compare the prognostic potential of estimated prognostic scores and develop optimal decision rules for assigning prognostic groups.
In Aim 3, we will provide procedures for identifying subjects who would benefit from a potentially expensive or invasive prognostic evaluation given an initial assessment. This project has access to a wide variety of real datasets which will guide the methodological research. Examples include 1) data from a study of patients diagnosed with pulmonary embolism;2) data from the Cardiovascular Health Study;3) gene expression data from a breast cancer study;and 4) data from an AIDS clinical trial.
Our aims will require development of large sample distribution theory, small sample simulation studies and application to real data. Software to implement analyses will use standard statistical packages such as Splus or SAS and will be fully documented.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM079330-03
Application #
7631381
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Lyster, Peter
Project Start
2007-06-01
Project End
2011-05-31
Budget Start
2009-06-01
Budget End
2011-05-31
Support Year
3
Fiscal Year
2009
Total Cost
$123,000
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
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Xia, Yin; Cai, Tianxi; Cai, T Tony (2018) Multiple Testing of Submatrices of a Precision Matrix with Applications to Identification of Between Pathway Interactions. J Am Stat Assoc 113:328-339
Sinnott, Jennifer A; Cai, Tianxi (2018) Pathway aggregation for survival prediction via multiple kernel learning. Stat Med 37:2501-2515
Zheng, Yingye; Brown, Marshall; Lok, Anna et al. (2017) IMPROVING EFFICIENCY IN BIOMARKER INCREMENTAL VALUE EVALUATION UNDER TWO-PHASE DESIGNS. Ann Appl Stat 11:638-654
Maziarz, Marlena; Heagerty, Patrick; Cai, Tianxi et al. (2017) On longitudinal prediction with time-to-event outcome: Comparison of modeling options. Biometrics 73:83-93
Zhou, Qian M; Dai, Wei; Zheng, Yingye et al. (2017) Robust Dynamic Risk Prediction with Longitudinal Studies. Stat Theory Relat Fields 1:159-170
Sinnott, Jennifer A; Cai, Tianxi (2016) Inference for survival prediction under the regularized Cox model. Biostatistics 17:692-707
Payne, Rebecca; Neykov, Matey; Jensen, Majken Karoline et al. (2016) Kernel machine testing for risk prediction with stratified case cohort studies. Biometrics 72:372-81
Payne, Rebecca; Yang, Ming; Zheng, Yingye et al. (2016) Robust risk prediction with biomarkers under two-phase stratified cohort design. Biometrics 72:1037-1045
Li, Junlong; Zhao, Lihui; Tian, Lu et al. (2016) A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies. Biometrics 72:877-87

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