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:

James B. Duke Distinguished Professor

Biostatistics & Bioinformatics, Division of Biostatistics
School of Medicine

Professor of Biostatistics & Bioinformatics

Biostatistics & Bioinformatics, Division of Biostatistics
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, 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

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

Administered By
Biostatistics & Bioinformatics, Division of Biostatistics
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

Awarded By
Prostate Cancer Foundation
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:

Figure S1 from PSMA-positive Circulating Tumor Cell Detection and Outcomes with Abiraterone or Enzalutamide Treatment in Men with Metastatic Castrate-resistant Prostate Cancer

<jats:p>&lt;p&gt;PSMA protein expression distribution from men with mCRPC at a single cell CTC level before (baseline, N= 97) and after (progression, N= 57) abiraterone or enzalutamide therapy.&lt;/p&gt;</jats:p>
Authors
Gupta, S; Halabi, S; Yang, Q; Roy, A; Tubbs, A; Gore, Y; George, DJ; Nanus, DM; Antonarakis, ES; Danila, DC; Szmulewitz, RZ; Wenstrup, R; Armstrong, AJ
MLA Citation
URI
https://scholars.duke.edu/individual/pub1578370
Source
crossref
Published Date
DOI
10.1158/1078-0432.22820182

Table S2 from PSMA-positive Circulating Tumor Cell Detection and Outcomes with Abiraterone or Enzalutamide Treatment in Men with Metastatic Castrate-resistant Prostate Cancer

<jats:p>&lt;p&gt;The prevalence of PSMA and Cell Search CTC positivity (≥5) at baseline and progression, as well as the observed median and optimal cutoffs for PSMA CTC.&lt;/p&gt;</jats:p>
Authors
Gupta, S; Halabi, S; Yang, Q; Roy, A; Tubbs, A; Gore, Y; George, DJ; Nanus, DM; Antonarakis, ES; Danila, DC; Szmulewitz, RZ; Wenstrup, R; Armstrong, AJ
MLA Citation
URI
https://scholars.duke.edu/individual/pub1578371
Source
crossref
Published Date
DOI
10.1158/1078-0432.22820167

Table S1 from PSMA-positive Circulating Tumor Cell Detection and Outcomes with Abiraterone or Enzalutamide Treatment in Men with Metastatic Castrate-resistant Prostate Cancer

<jats:p>&lt;p&gt;Comparison of CTC enumeration and PSMA+ CTC expression in 57 baseline and paired progression samples from the PROPHECY study. All the CTC parameters (mean, median, and range) were represent in CTC/mL.&lt;/p&gt;</jats:p>
Authors
Gupta, S; Halabi, S; Yang, Q; Roy, A; Tubbs, A; Gore, Y; George, DJ; Nanus, DM; Antonarakis, ES; Danila, DC; Szmulewitz, RZ; Wenstrup, R; Armstrong, AJ
MLA Citation
URI
https://scholars.duke.edu/individual/pub1578372
Source
crossref
Published Date
DOI
10.1158/1078-0432.22820170

Figure S2 from PSMA-positive Circulating Tumor Cell Detection and Outcomes with Abiraterone or Enzalutamide Treatment in Men with Metastatic Castrate-resistant Prostate Cancer

<jats:p>&lt;p&gt;Kaplan Meier plots depict the association between PSMA+ CTC enumeration and OS and PFS. The associations of PSMA+ CTC enumeration with overall survival (OS) and progression-free survival (PFS) were explored using the proportional hazard model. A) The median OS in months for CTC=0 (reference), CTC+ PSMA-CTC, CTC+ PSMA+CTC but heterogeneous (&lt;100%), and CTC+ PSMA+ 100% (homogeneous), respectively, were 25.7 (95% CI=19.8-NR), 24.5 (95% CI=16.7-30.4),15.6 (95% CI=14.4-20.7), and 35.0 (95% CI=16.9-NA). Univariate hazard ratio (HR) was 1.3 (95% CI=0.7-2.6), 2.0 (95% CI=1.0-3.8) and 0.7; 95% CI = 0.2-2.1 for CTC+, PSMA-CTC, CTC+ PSMA CTC+ but heterogeneous (&lt;100%), and CTC+ PSMA+ 100% (homogeneous) vs. CTC=0, respectively. B) The median PFS for CTC=0 (reference), CTC+, PSMA-CTC, CTC+ PSMA+ CTC but heterogeneous (&lt;100%), and CTC+ PSMA+ 100%, respectively, were 7.6 (95% CI=6.7-16.5), 6.0 (95% CI=3.5-11.4),5.5 (95% CI=3.6-7.6), and 5.7 (95% CI=3.0-NA). Univariate HR was 1.4 (95% CI=0.8-2.6), 2.0 (95% CI=1.1-3.6) and 1.5; 95% CI = 0.7-3.6 for CTC+, PSMA-CTC, CTC+ PSMA+ CTC but heterogeneous (&lt;100%), and CTC+ PSMA+ 100% (homogeneous) vs. CTC=0, respectively.&lt;/p&gt;</jats:p>
Authors
Gupta, S; Halabi, S; Yang, Q; Roy, A; Tubbs, A; Gore, Y; George, DJ; Nanus, DM; Antonarakis, ES; Danila, DC; Szmulewitz, RZ; Wenstrup, R; Armstrong, AJ
MLA Citation
URI
https://scholars.duke.edu/individual/pub1578373
Source
crossref
Published Date
DOI
10.1158/1078-0432.22820179.v1

Factorial Trials

Factorial clinical trials test the effects of two or more therapies using a design that can estimate interaction between therapies (Piantadosi 2017). (This chapter revises, updates, and expands upon reference (Piantadosi 2017)) A factorial structure is the only design that can assess treatment interactions, so this type of trial is required for those important therapeutic questions. When interactions between treatments are absent, which is not a trivial requirement, a factorial design can estimate each of several treatment effects from the same data. For example, two treatments can sometimes be evaluated using the same number of subjects ordinarily used to test a single therapy. When possible, this demonstrates a striking efficiency. For these reasons, factorial designs have an important place in clinical trial methodology, and have been applied in a variety of setting, but in particular in disease prevention.
Authors
Piantadosi, S; Halabi, S
MLA Citation
Piantadosi, S., and S. Halabi. “Factorial Trials.” Principles and Practice of Clinical Trials, 2022, pp. 1353–76. Scopus, doi:10.1007/978-3-319-52636-2_100.
URI
https://scholars.duke.edu/individual/pub1584373
Source
scopus
Published Date
Start Page
1353
End Page
1376
DOI
10.1007/978-3-319-52636-2_100

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
James B. Duke Distinguished Professor

Contact:

Profile

https://scholars.duke.edu/person/susan.halabi

Email

susan.halabi@duke.edu
2424 Erwin Road 11th Floor, 11088 Hock Plaza, Durham, NC 27705
Duke Box 2717, Durham, NC 27710