Xiaofei Wang

Overview:

Design and Analysis of Clinical Trials
Nonparametric and Semiparametric Methods
Survival Analysis
Statistical Methods for Diagnostic and Predictive Medicine
Biomarker Discovery and Validation
Health Outcomes Research

Positions:

Professor of Biostatistics and Bioinformatics

Biostatistics & Bioinformatics
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 2003

University of North Carolina at Chapel Hill

Graduate Research Assistant, Computer Sciences

University of North Carolina at Chapel Hill

Graduate Research Assistant, Biostatistics

University of North Carolina at Chapel Hill

Grants:

Translational meta-analysis for elderly lung cancer patients

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

National Clinical Trials Network - Network Group Statistics and DMCs

Administered By
Duke Cancer Institute
Awarded By
Mayo Clinic
Role
Statistician
Start Date
End Date

Cancer and Leukemia Group B Statistical Center

Administered By
Duke Cancer Institute
Awarded By
National Institutes of Health
Role
Statistician
Start Date
End Date

Semiparametric ROC Curve Regression for Cancer Screening Studies

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

Innovative Biomarker-Integrated Clinical Trial Design and Analysis

Administered By
Integrative Genomics
Awarded By
University of North Carolina - Chapel Hill
Role
Principal Investigator
Start Date
End Date

Publications:

Phase II trial of atezolizumab before and after definitive chemoradiation for unresectable stage III NSCLC.

Authors
Ross, HJ; Kozono, DE; Urbanic, JJ; Williams, TM; Dufrane, C; Bara, I; Gandhi, M; Schulze, K; Brockman, JM; Wang, XF; Vokes, EE; Stinchcombe, T
MLA Citation
Ross, Helen J., et al. “Phase II trial of atezolizumab before and after definitive chemoradiation for unresectable stage III NSCLC.Journal of Clinical Oncology, vol. 36, no. 15_suppl, American Society of Clinical Oncology (ASCO), 2018, pp. TPS8585–TPS8585. Crossref, doi:10.1200/jco.2018.36.15_suppl.tps8585.
URI
https://scholars.duke.edu/individual/pub1441373
Source
crossref
Published In
Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology
Volume
36
Published Date
Start Page
TPS8585
End Page
TPS8585
DOI
10.1200/jco.2018.36.15_suppl.tps8585

The importance of body composition and physical function on overall survival (OS) in non-small cell lung cancer (NSCLC) patients receiving platinum based chemotherapy (PBCT).

Authors
Kinsey, E; Ajazi, E; Wang, XF; Johnston, MA; Crawford, J
MLA Citation
Kinsey, Emily, et al. “The importance of body composition and physical function on overall survival (OS) in non-small cell lung cancer (NSCLC) patients receiving platinum based chemotherapy (PBCT).Journal of Clinical Oncology, vol. 36, no. 15_suppl, American Society of Clinical Oncology (ASCO), 2018, pp. e21088–e21088. Crossref, doi:10.1200/jco.2018.36.15_suppl.e21088.
URI
https://scholars.duke.edu/individual/pub1441475
Source
crossref
Published In
Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology
Volume
36
Published Date
Start Page
e21088
End Page
e21088
DOI
10.1200/jco.2018.36.15_suppl.e21088

Endpoint surrogacy in oncology Phase 3 randomised controlled trials.

Endpoint surrogacy is an important concept in oncology trials. Using a surrogate endpoint like progression-free survival as the primary endpoint-instead of overall survival-would lead to a potential faster drug approval and therefore more cancer patients with an earlier opportunity to receive the newly approved drugs.
Authors
Zhang, J; Pilar, MR; Wang, X; Liu, J; Pang, H; Brownson, RC; Colditz, GA; Liang, W; He, J
MLA Citation
Zhang, Jianrong, et al. “Endpoint surrogacy in oncology Phase 3 randomised controlled trials.Br J Cancer, May 2020. Pubmed, doi:10.1038/s41416-020-0896-5.
URI
https://scholars.duke.edu/individual/pub1442163
PMID
32451466
Source
pubmed
Published In
Br J Cancer
Published Date
DOI
10.1038/s41416-020-0896-5

Predictive accuracy of markers or risk scores for interval censored survival data.

Methods for the evaluation of the predictive accuracy of biomarkers with respect to survival outcomes subject to right censoring have been discussed extensively in the literature. In cancer and other diseases, survival outcomes are commonly subject to interval censoring by design or due to the follow up schema. In this article, we present an estimator for the area under the time-dependent receiver operating characteristic (ROC) curve for interval censored data based on a nonparametric sieve maximum likelihood approach. We establish the asymptotic properties of the proposed estimator and illustrate its finite-sample properties using a simulation study. The application of our method is illustrated using data from a cancer clinical study. An open-source R package to implement the proposed method is available on Comprehensive R Archive Network.
MLA Citation
Wu, Yuan, et al. “Predictive accuracy of markers or risk scores for interval censored survival data.Stat Med, Apr. 2020. Pubmed, doi:10.1002/sim.8547.
URI
https://scholars.duke.edu/individual/pub1437823
PMID
32293745
Source
pubmed
Published In
Stat Med
Published Date
DOI
10.1002/sim.8547

Predicting risk of chemotherapy-induced severe neutropenia: A pooled analysis in individual patients data with advanced lung cancer.

OBJECTIVES: Neutropenia is associated with the risk of life-threatening infections, chemotherapy dose reductions and delays that may compromise outcomes. This analysis was conducted to develop a prediction model for chemotherapy-induced severe neutropenia in lung cancer. MATERIALS AND METHODS: Individual patient data from existing cooperative group phase II/III trials of stages III/IV non-small cell lung cancer or extensive small-cell lung cancer were included. The data were split into training and testing sets. In order to enhance the prediction accuracy and the reliability of the prediction model, lasso method was used for both variable selection and regularization on the training set. The selected variables was fit to a logistic model to obtain regression coefficients. The performance of the final prediction model was evaluated by the area under the ROC curve in both training and testing sets. RESULTS: The dataset was randomly separated into training [7606 (67 %) patients] and testing [3746 (33 %) patients] sets. The final model included: age (>65 years), gender (male), weight (kg), BMI, insurance status (yes/unknown), stage (IIIB/IV/ESSCLC), number of metastatic sites (1, 2 or ≥3), individual drugs (gemcitabine, taxanes), number of chemotherapy agents (2 or ≥3), planned use of growth factors, associated radiation therapy, previous therapy (chemotherapy, radiation, surgery), duration of planned treatment, pleural effusion (yes/unknown), performance status (1, ≥2) and presence of symptoms (yes/unknown). CONCLUSIONS: We have developed a relatively simple model with routinely available pre-treatment variables, to predict for neutropenia. This model should be independently validated prospectively.
Authors
Cao, X; Ganti, AK; Stinchcombe, T; Wong, ML; Ho, JC; Shen, C; Liu, Y; Crawford, J; Pang, H; Wang, X
MLA Citation
Cao, Xiaowen, et al. “Predicting risk of chemotherapy-induced severe neutropenia: A pooled analysis in individual patients data with advanced lung cancer.Lung Cancer, vol. 141, Mar. 2020, pp. 14–20. Pubmed, doi:10.1016/j.lungcan.2020.01.004.
URI
https://scholars.duke.edu/individual/pub1428030
PMID
31926983
Source
pubmed
Published In
Lung Cancer
Volume
141
Published Date
Start Page
14
End Page
20
DOI
10.1016/j.lungcan.2020.01.004