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, 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
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
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:

Score and deviance residuals based on the full likelihood approach in survival analysis.

Assuming the proportional hazards model and non-informative censoring, the full likelihood approach is used to obtain two new residuals. The first residual is based on the ideas used in obtaining score-type residuals similar to the partial likelihood approach. The second type of residual is based on the concept of deviance residuals. Extensive simulations are conducted to compare the performance of the residuals from the full likelihood-based approach with those of the partial likelihood method. We demonstrate through simulation studies that the full likelihood-based residuals are more efficient than their partial likelihood counterpart in identifying potential outliers when the censoring proportion is high. The graphical techniques are used to illustrate the applications of these residuals using some examples.
Authors
Halabi, S; Dutta, S; Wu, Y; Liu, A
MLA Citation
Halabi, Susan, et al. “Score and deviance residuals based on the full likelihood approach in survival analysis.Pharm Stat, Aug. 2020. Pubmed, doi:10.1002/pst.2047.
URI
https://scholars.duke.edu/individual/pub1453808
PMID
32776412
Source
pubmed
Published In
Pharm Stat
Published Date
DOI
10.1002/pst.2047

Abiraterone acetate (AA) with or without cabazitaxel (CBZ) in treatment of chemotherapy naive metastatic castration-resistant prostate cancer (mCRPC)

Authors
Slovin, SF; Knudsen, KE; Halabi, S; Fleming, MT; Molina, AM; Wolf, SP; de Leeuw, R; Fernandez, C; Kang, P; Southwell, T; Jones, CL; Fernandez, E; Kelly, WK
MLA Citation
Slovin, Susan F., et al. “Abiraterone acetate (AA) with or without cabazitaxel (CBZ) in treatment of chemotherapy naive metastatic castration-resistant prostate cancer (mCRPC).” Journal of Clinical Oncology, vol. 38, no. 6, AMER SOC CLINICAL ONCOLOGY, 2020.
URI
https://scholars.duke.edu/individual/pub1441573
Source
wos
Published In
Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology
Volume
38
Published Date

Cancer and Leukemia Group B 90203 (Alliance): Radical Prostatectomy With or Without Neoadjuvant Chemohormonal Therapy in Localized, High-Risk Prostate Cancer.

PURPOSE: Radical prostatectomy (RP) alone is often inadequate in curing men with clinically localized, high-risk prostate cancer (PC). We hypothesized that chemohormonal therapy (CHT) with androgen-deprivation therapy plus docetaxel before RP would improve biochemical progression-free survival (BPFS) over RP alone. PATIENTS AND METHODS: Men with clinically localized, high-risk PC were assigned to RP alone or neoadjuvant CHT with androgen deprivation plus docetaxel (75 mg/m2 body surface area every 3 weeks for 6 cycles) and RP. The primary end point was 3-year BPFS. Biochemical failure was defined as a serum prostate-specific antigen level > 0.2 ng/mL that increased on 2 consecutive occasions that were at least 3 months apart. Secondary end points included 5-year BPFS, overall BPFS, local recurrence, metastasis-free survival (MFS), PC-specific mortality, and overall survival (OS). RESULTS: In total, 788 men were randomly assigned. Median follow-up time was 6.1 years. The overall rates of grade 3 and 4 adverse events during chemotherapy were 26% and 19%, respectively. No difference was seen in 3-year BPFS between neoadjuvant CHT plus RP and RP alone (0.89 v 0.84, respectively; 95% CI for the difference, -0.01 to 0.11; P = .11). Neoadjuvant CHT was associated with improved overall BPFS (hazard ratio [HR], 0.69; 95% CI, 0.48 to 0.99), improved MFS (HR, 0.70; 95% CI, 0.51 to 0.95), and improved OS (HR, 0.61; 95% CI, 0.40 to 0.94) compared with RP alone. CONCLUSION: The primary study end point, 3-year BPFS, was not met. Although some improvement was seen in secondary end points, any potential benefit must be weighed against toxicity. Our data do not support the routine use of neoadjuvant CHT and RP in patients with clinically localized, high-risk PC at this time.
Authors
Eastham, JA; Heller, G; Halabi, S; Monk, JP; Beltran, H; Gleave, M; Evans, CP; Clinton, SK; Szmulewitz, RZ; Coleman, J; Hillman, DW; Watt, CR; George, S; Sanda, MG; Hahn, OM; Taplin, M-E; Parsons, JK; Mohler, JL; Small, EJ; Morris, MJ
MLA Citation
Eastham, James A., et al. “Cancer and Leukemia Group B 90203 (Alliance): Radical Prostatectomy With or Without Neoadjuvant Chemohormonal Therapy in Localized, High-Risk Prostate Cancer.J Clin Oncol, July 2020, p. JCO2000315. Pubmed, doi:10.1200/JCO.20.00315.
URI
https://scholars.duke.edu/individual/pub1452573
PMID
32706639
Source
pubmed
Published In
Journal of Clinical Oncology
Published Date
Start Page
JCO2000315
DOI
10.1200/JCO.20.00315

Cobimetinib plus vemurafenib (C plus V) in patients (Pts) with colorectal cancer (CRC) with BRAF V600E mutations: Results from the TAPUR Study

Authors
Klute, K; Garrett-Mayer, E; Halabi, S; Mangat, PK; Nazemzadeh, R; Yost, KJ; Butler, NL; Perla, V; Schilsky, RL
MLA Citation
URI
https://scholars.duke.edu/individual/pub1443550
Source
wos-lite
Published In
Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology
Volume
38
Published Date

Best (but oft-forgotten) practices: sample size and power calculation for a dietary intervention trial with episodically consumed foods.

Dietary interventions often target foods that are underconsumed relative to dietary guidelines, such as vegetables, fruits, and whole grains. Because these foods are only consumed episodically for some participants, data from such a study often contains a disproportionally large number of zeros due to study participants who do not consume any of the target foods on the days that dietary intake is assessed, thus generating semicontinuous data. These zeros need to be properly accounted for when calculating sample sizes to ensure that the study is adequately powered to detect a meaningful intervention effect size. Nonetheless, this issue has not been well addressed in the literature. Instead, methods that are common for continuous outcomes are typically used to compute the sample sizes, resulting in a substantially under- or overpowered study. We propose proper approaches to calculating the sample size needed for dietary intervention studies that target episodically consumed foods. Sample size formulae are derived for detecting the mean difference in the amount of intake of an episodically consumed food between an intervention and a control group. Numerical studies are conducted to investigate the accuracy of the sample size formulae as compared with the ad hoc methods. The simulation results show that the proposed formulae are appropriate for estimating the sample sizes needed to achieve the desired power for the study. The proposed method for sample size is recommended for designing dietary intervention studies targeting episodically consumed foods.
Authors
Zhang, W; Liu, A; Zhang, Z; Nansel, T; Halabi, S
MLA Citation
URI
https://scholars.duke.edu/individual/pub1450780
PMID
32644103
Source
pubmed
Published In
The American Journal of Clinical Nutrition
Published Date
DOI
10.1093/ajcn/nqaa176

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