Susan Halabi

Overview:

Design and analysis of clinical trials, statistical analysis of biomarker and high dimensional data, development and validation of prognostic and predictive models.

Positions:

Professor of Biostatistics and Bioinformatics

Biostatistics & Bioinformatics
School of Medicine

Chief, Division of Biostatistics

Biostatistics & Bioinformatics
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 1994

University of Texas Health Sciences Center at Houston

Grants:

PCRP Clinical Consortium: Duke University Clinical Research Site

Administered By
Medicine, Medical Oncology
Awarded By
Department of Defense
Role
Co Investigator
Start Date
End Date

Prognostic Models of Clinical Outcomes In Men With Castration Resistant Prostate Cancer

Administered By
Biostatistics & Bioinformatics
Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Surrogate Endpoints of Overall Survival in Men with Metastatic Hormone Sensitive Prostate Cancer

Administered By
Biostatistics & Bioinformatics
Role
Principal Investigator
Start Date
End Date

Precision Medicine in Platinum-treated Lethal Bladder Cancer

Administered By
Biostatistics & Bioinformatics
Awarded By
Memorial Sloan Kettering Cancer Center
Role
Principal Investigator
Start Date
End Date

Serum Androgens and Survival in CRPC

Administered By
Duke Cancer Institute
Awarded By
University of California, San Francisco
Role
Principal Investigator
Start Date
End Date

Publications:

Challenges and Opportunities to Updating Prescribing Information for Longstanding Oncology Drugs.

A number of important drugs used to treat cancer-many of which serve as the backbone of modern chemotherapy regimens-have outdated prescribing information in their drug labeling. The Food and Drug Administration is undertaking a pilot project to develop a process and criteria for updating prescribing information for longstanding oncology drugs, based on the breadth of knowledge the cancer community has accumulated with the use of these drugs over time. This article highlights a number of considerations for labeling updates, including selecting priorities for updating; data sources and evidentiary criteria; as well as the risks, challenges, and opportunities for iterative review to ensure prescribing information for oncology drugs remains relevant to current clinical practice.
Authors
Balogh, EP; Bindman, AB; Eckhardt, SG; Halabi, S; Harvey, RD; Jaiyesimi, I; Miksad, R; Moses, HL; Nass, SJ; Schilsky, RL; Sun, S; Torrente, JM; Warren, KE
MLA Citation
Balogh, Erin P., et al. “Challenges and Opportunities to Updating Prescribing Information for Longstanding Oncology Drugs..” Oncologist, Dec. 2019. Pubmed, doi:10.1634/theoncologist.2019-0698.
URI
https://scholars.duke.edu/individual/pub1423440
PMID
31801899
Source
pubmed
Published In
Oncologist
Published Date
DOI
10.1634/theoncologist.2019-0698

Improved Reporting in Abstracts When Uncertainty Is Inevitable.

Authors
Halabi, S; Day, S
MLA Citation
Halabi, Susan, and Simon Day. “Improved Reporting in Abstracts When Uncertainty Is Inevitable..” Jama Netw Open, vol. 2, no. 12, Dec. 2019. Pubmed, doi:10.1001/jamanetworkopen.2019.17543.
URI
https://scholars.duke.edu/individual/pub1423441
PMID
31834388
Source
pubmed
Published In
Jama Network Open
Volume
2
Published Date
Start Page
e1917543
DOI
10.1001/jamanetworkopen.2019.17543

Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology.

The identification of prognostic factors and building of risk assessment prognostic models will continue to play a major role in 21st century medicine in patient management and decision making. Investigators are often interested in examining the relationship between host, tumor-related, and environmental variables in predicting clinical outcomes. We make a distinction between static and dynamic prediction models. In static prediction modelling, typically variables collected at baseline are utilized in building models. On the other hand, dynamic predictive models leverage the longitudinal data of covariates collected during treatment or follow-up, and hence provide accurate predictions of patients prognoses. To date, most risk assessment models in oncology have been based on static models. In this article, we cover topics that are related to the analysis of prognostic factors, centering on factors that are both relevant at the time of diagnosis or initial treatment and during treatment. We describe the types of risk prediction and then provide a brief description of the penalized regression methods. We then review the state-of-the art methods for dynamic prediction and compare the strengths and the limitations of these methods. While static models will continue to play an important role in oncology, developing and validating dynamic models of clinical outcomes need to take a higher priority. It is apparent that a framework for developing and validating dynamic tools in oncology is still needed. One of the limitations in oncology that modelers may be constrained by the lack of access to the longitudinal biomarker data. It is highly recommended that the next generation of risk assessments consider the longitudinal biomarker data and outcomes so that prediction can be continually updated.
Authors
Halabi, S; Li, C; Luo, S
MLA Citation
Halabi, Susan, et al. “Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology..” Jco Precis Oncol, vol. 3, 2019. Pubmed, doi:10.1200/PO.19.00068.
URI
https://scholars.duke.edu/individual/pub1424329
PMID
31840130
Source
pubmed
Published In
Jco Precision Oncology
Volume
3
Published Date
DOI
10.1200/PO.19.00068

Palbociclib (P) in patients (pts) with non-small cell lung cancer (NSCLC) with CDKN2A alterations: Results from the Targeted Agent and Profiling Utilization Registry (TAPUR) Study.

Authors
Ahn, ER; Mangat, PK; Garrett-Mayer, E; Halabi, S; Dib, EG; Haggstrom, DE; Alguire, KB; Alvarez, RH; Calfa, CJ; Cannon, TL; Crilley, PA; Gaba, AG; Marr, AS; Sangal, A; Thota, R; Antonelli, KR; Islam, S; Rygiel, AL; Bruinooge, SS; Schilsky, RL
MLA Citation
URI
https://scholars.duke.edu/individual/pub1415711
Source
wos
Published In
Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology
Volume
37
Published Date

CALGB 90601 (Alliance): Randomized, double-blind, placebo-controlled phase III trial comparing gemcitabine and cisplatin with bevacizumab or placebo in patients with metastatic urothelial carcinoma.

Authors
Rosenberg, JE; Ballman, KV; Halabi, S; Watt, C; Hahn, OM; Steen, PD; Dreicer, R; Flaig, TW; Stadler, WM; Sweeney, C; Mortazavi, A; Morris, MJ
MLA Citation
Rosenberg, Jonathan E., et al. “CALGB 90601 (Alliance): Randomized, double-blind, placebo-controlled phase III trial comparing gemcitabine and cisplatin with bevacizumab or placebo in patients with metastatic urothelial carcinoma..” Journal of Clinical Oncology, vol. 37, no. 15_suppl, American Society of Clinical Oncology (ASCO), 2019, pp. 4503–4503. Crossref, doi:10.1200/jco.2019.37.15_suppl.4503.
URI
https://scholars.duke.edu/individual/pub1415712
Source
crossref
Published In
Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology
Volume
37
Published Date
Start Page
4503
End Page
4503
DOI
10.1200/jco.2019.37.15_suppl.4503

Research Areas:

Adenocarcinoma
Adenocarcinoma, Clear Cell
African Americans
Age Factors
Aged, 80 and over
Alkaline Phosphatase
Alleles
Arab countries
Area Under Curve
Biological Markers
Biomarkers, Pharmacological
Breast Neoplasms
Cancer Vaccines
Carcinoma
Carcinoma, Renal Cell
Case-Control Studies
Chemoprevention
Chemotherapy
Chi-Square Distribution
Clinical Trials, Phase II as Topic
Clinical trials
Cohort Studies
Computer Simulation
Confidence Intervals
Construction Materials
Contraceptives, Oral
DNA Damage
DNA Primers
DNA Repair
DNA, Neoplasm
Data Interpretation, Statistical
Decision Making
Decision Support Techniques
Diagnostic Imaging
Disease Progression
Disease-Free Survival
Drug Design
Dust
Efficiency, Organizational
Endpoint Determination
Equipment Design
Factor Analysis, Statistical
Family relationships
Gels
Gene Expression
Genes, Immunoglobulin
Genetic Predisposition to Disease
Genetics, Medical
Genotype
Germany
Graft vs Host Disease
HIV Infections
Hispanic Americans
Individualized Medicine
Kaplan-Meier Estimate
Ketoconazole
Lasso
Logistic Models
Lymphokines
Mining
Models, Biological
Models, Statistical
Models, Theoretical
Molecular Sequence Data
Multiprotein Complexes
Multivariate Analysis
Mutation
Neoplasms, Hormone-Dependent
Nomograms
Odds Ratio
Outcome Assessment (Health Care)
Ovarian Neoplasms
Personalized medicine
Population
Population Surveillance
Precision Medicine
Predictive Value of Tests
Pregnancy
Probability
Prognosis
Proportional Hazards Models
Prospective Studies
ROC Curve
Randomized Controlled Trials as Topic
Receptors, Progesterone
Registries
Reproducibility of Results
Research Design
Residence Characteristics
Retrospective Studies
Ribosomal Protein S6 Kinases
Risk
Risk Assessment
Risk Factors
Sample Size
Selective Estrogen Receptor Modulators
Sensitivity and Specificity
Statistics as Topic
Survival
Survival Analysis
Survival Rate
Tamoxifen
Translocation, Genetic
Treatment Failure
Treatment Outcome
Tumor Markers, Biological
United States
Urologic Neoplasms
Validation Studies as Topic
Vascular Endothelial Growth Factors