Sin-Ho Jung

Overview:

Design of Clinical Trials
Survival Analysis
Longitudinal Data Analysis
Clustered Data Analysis
ROC Curve Analysis
Design and Analysis of Microarray Studies

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. 1992

University of Wisconsin - Madison

Grants:

Role of the tumor NLRP3 inflammasome in the generation of anti-PD-1 antibody immunotherapy-associated toxicities

Administered By
Medicine, Medical Oncology
Awarded By
National Institutes of Health
Role
Biostatistician
Start Date
End Date

Alliance NCORP Research Base - Clinical Trials - CALGB 70807

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

ETIOLOGY OF COPD AMONG CONSTRUCTION WORKERS

Administered By
Family Medicine & Community Health,Occupational & Environmental Medicine
Awarded By
National Institute for Occupational Safety and Health
Role
Biostatistician
Start Date
End Date

Publications:

Sample size calculation for clustered survival data under subunit randomization.

Each cluster consists of multiple subunits from which outcome data are collected. In a subunit randomization trial, subunits are randomized into different intervention arms. Observations from subunits within each cluster tend to be positively correlated due to the shared common frailties, so that the outcome data from a subunit randomization trial have dependency between arms as well as within each arm. For subunit randomization trials with a survival endpoint, few methods have been proposed for sample size calculation showing the clear relationship between the joint survival distribution between subunits and the sample size, especially when the number of subunits from each cluster is variable. In this paper, we propose a closed form sample size formula for weighted rank test to compare the marginal survival distributions between intervention arms under subunit randomization, possibly with variable number of subunits among clusters. We conduct extensive simulations to evaluate the performance of our formula under various design settings, and demonstrate our sample size calculation method with some real clinical trials.
Authors
Li, J; Jung, S-H
MLA Citation
Li, Jianghao, and Sin-Ho Jung. “Sample size calculation for clustered survival data under subunit randomization.Lifetime Data Anal, Oct. 2021. Pubmed, doi:10.1007/s10985-021-09538-0.
URI
https://scholars.duke.edu/individual/pub1500695
PMID
34716530
Source
pubmed
Published In
Lifetime Data Anal
Published Date
DOI
10.1007/s10985-021-09538-0

Long-term outcomes of aortic root operations in the United States among Medicare beneficiaries.

OBJECTIVE: The best method of aortic root repair in older patients remains unknown given a lack of comparative effectiveness of long-term outcomes data. The objective of this study was to compare long-term outcomes of different surgical approaches for aortic root repair in Medicare patients using The Society of Thoracic Surgeons Adult Cardiac Surgery Database-Centers for Medicare & Medicaid Services-linked data. METHODS: A retrospective cohort study was performed by querying the Society of Thoracic Surgeons Adult Cardiac Surgery Database for patients aged 65 years or more who underwent elective aortic root repair with or without aortic valve replacement. Primary long-term end points were mortality, any stroke, and aortic valve reintervention. Short-term outcomes and long-term survival were compared among each root repair strategy. Additional risk factors for mortality after aortic root repair were assessed with a multivariable Cox proportional hazards model. RESULTS: A total of 4173 patients aged 65 years or more underwent elective aortic root repair. Patients were stratified by operative strategy: mechanical Bentall, stented bioprosthetic Bentall, stentless bioprosthetic Bentall, or valve-sparing root replacement. Mean follow-up was 5.0 (±4.6) years. Relative to mechanical Bentall, stented bioprosthetic Bentall (adjusted hazard ratio, 0.80; confidence interval, 0.66-0.97) and stentless bioprosthetic Bentall (adjusted hazard ratio, 0.70; confidence interval, 0.59-0.84) were associated with better long-term survival. In addition, stentless bioprosthetic Bentall (adjusted hazard ratio, 0.64; confidence interval, 0.47-0.80) and valve-sparing root replacement (adjusted hazard ratio, 0.51; confidence interval, 0.29-0.90) were associated with lower long-term risk of stroke. Aortic valve reintervention risk was 2-fold higher after valve-sparing root replacement compared with other operative strategies. CONCLUSIONS: In the Medicare population, there was poorer late survival and greater late stroke risk for patients undergoing mechanical Bentall and a higher rate of reintervention for valve-sparing root replacement. Bioprosthetic Bentall may be the procedure of choice in older patients undergoing aortic root repair, particularly in the era of transcatheter aortic valve replacement.
Authors
Yerokun, BA; Vallabhajosyula, P; Vekstein, AM; Grau-Sepulveda, MV; Benrashid, E; Xian, Y; Ranney, DN; Jung, S-H; Jacobs, JP; Badhwar, V; Thourani, VH; Bavaria, JE; Hughes, GC
MLA Citation
Yerokun, Babatunde A., et al. “Long-term outcomes of aortic root operations in the United States among Medicare beneficiaries.J Thorac Cardiovasc Surg, Feb. 2021. Pubmed, doi:10.1016/j.jtcvs.2021.02.068.
URI
https://scholars.duke.edu/individual/pub1478396
PMID
33814173
Source
pubmed
Published In
The Journal of Thoracic and Cardiovascular Surgery
Published Date
DOI
10.1016/j.jtcvs.2021.02.068

K-Sample comparisons using propensity analysis.

In this paper, we investigate K-group comparisons on survival endpoints for observational studies. In clinical databases for observational studies, treatment for patients are chosen with probabilities varying depending on their baseline characteristics. This often results in noncomparable treatment groups because of imbalance in baseline characteristics of patients among treatment groups. In order to overcome this issue, we conduct propensity analysis and match the subjects with similar propensity scores across treatment groups or compare weighted group means (or weighted survival curves for censored outcome variables) using the inverse probability weighting (IPW). To this end, multinomial logistic regression has been a popular propensity analysis method to estimate the weights. We propose to use decision tree method as an alternative propensity analysis due to its simplicity and robustness. We also propose IPW rank statistics, called Dunnett-type test and ANOVA-type test, to compare 3 or more treatment groups on survival endpoints. Using simulations, we evaluate the finite sample performance of the weighted rank statistics combined with these propensity analysis methods. We demonstrate these methods with a real data example. The IPW method also allows us for unbiased estimation of population parameters of each treatment group. In this paper, we limit our discussions to survival outcomes, but all the methods can be easily modified for any type of outcomes, such as binary or continuous variables.
Authors
Jung, S-H; Chi, SA; Ahn, HJ
MLA Citation
Jung, Sin-Ho, et al. “K-Sample comparisons using propensity analysis.Biom J, vol. 61, no. 3, May 2019, pp. 698–713. Pubmed, doi:10.1002/bimj.201800049.
URI
https://scholars.duke.edu/individual/pub1364977
PMID
30614546
Source
pubmed
Published In
Biometrical Journal
Volume
61
Published Date
Start Page
698
End Page
713
DOI
10.1002/bimj.201800049

Perioperative Fresh Red Blood Cell Transfusion May Negatively Affect Recipient Survival After Liver Transplantation.

OBJECTIVE: The aim of this study is to evaluate the association between fresh red blood cell (RBC) transfusion and recipient survival after liver transplantation. BACKGROUND: Fresh RBC products contain many viable leukocytes. Allogeneic leukocytes are responsible for adverse transfusion reactions in the immunocompromised host. METHODS: Among 343 liver transplant recipients who underwent perioperative RBC transfusion, 91 of 226 who did not receive fresh RBCs were matched with 91 of 117 who received fresh RBCs with 1:1 matching ratio using the propensity score based on the amount of transfused blood products and others. Survival analysis was performed using the Cox model. RESULTS: All transfused 3230 RBCs were leukoreduced and irradiated. Before matching, recipients in fresh RBC group received 3 U (2-6 U) of fresh RBCs. After a median follow-up of 60 months, 60 of 343 recipients (17.5%) died. Survival probability at 1/2/5 years after transplantation was 94.7%/92.0%/85.8% for nonfresh RBC group and 82.9%/76.0%/72.0% for fresh RBC group [death hazard ratio (HR) = 2.37 (1.43-3.94), P = 0.001]. In multivariable analysis, fresh RBC transfusion was significantly associated with increased death risk [HR = 2.33 (1.35-4.01), P = 0.002]. After matching, recipients in fresh RBC group received 3 U (2-5 U) of fresh RBCs. After a median follow-up of 56 months, 35 of 182 recipients (19.2%) died. Survival probability at 1/2/5 years was 95.6%/93.2%/86.0% for nonfresh RBC group and 85.7%/78.0%/73.0% for fresh RBC group [HR = 2.23 (1.43-3.94), P = 0.028]. Multivariable analysis confirmed a significance of fresh RBC transfusion [HR = 3.20 (1.51-6.78), P = 0.002]. CONCLUSION: Our findings suggest a potential negative impact of fresh RBC transfusion on the survival of patients undergoing liver transplantation.
Authors
Han, S; Kwon, JH; Jung, SH; Seo, JY; Jo, YJ; Jang, JS; Yeon, SM; Jung, SH; Ko, JS; Gwak, MS; Cho, D; Son, HJ; Kim, GS
MLA Citation
Han, Sangbin, et al. “Perioperative Fresh Red Blood Cell Transfusion May Negatively Affect Recipient Survival After Liver Transplantation.Ann Surg, vol. 267, no. 2, 2018, pp. 346–51. Pubmed, doi:10.1097/SLA.0000000000002062.
URI
https://scholars.duke.edu/individual/pub1488851
PMID
27805962
Source
pubmed
Published In
Ann Surg
Volume
267
Published Date
Start Page
346
End Page
351
DOI
10.1097/SLA.0000000000002062

A phase II trial of concurrent chemoradiotherapy with weekly docetaxel plus cisplatin treatment for unresectable locally advanced head and neck cancer

Purpose Although concurrent chemoradiotherapy (CCRT) is the standard of care for locally advanced unresectable squamous cell carcinoma of the head and neck (SCCHN), the optimal CCRT regimen is not yet defined. We conducted a phase II study of weekly docetaxel and cisplatin treatment with concurrent radiotherapy (RT) to investigate the efficacy and toxicity profiles. Material and methods Forty-one patients with locally advanced SCCHN were treated with 20 mg/m2 docetaxel plus 20 mg/m2 cisplatin weekly for 6 cycles, concurrent with RT, between April 2010 and March 2013. Results The mean total doses of docetaxel and cisplatin were 109.3 mg/m2 and 110.7 mg/m2, respectively. The mean total delivered dose of radiation was 67.7 Gy. Thirty-seven patients (90.3%) received 5 or more cycles of treatment. At the 1-month post-CCRT tumor response evaluation, 13 patients (39.9%) achieved a complete response (CR) [95% confidence interval (95% CI), 20.1–56.6]. Thirty-two patients (78.0%) ultimately achieved CR during the post-treatment follow-up period. With a median follow-up of 3.4 years, the 2-year overall survival (OS), disease-free survival (DFS), and distant disease-free survival (DDFS) were 85.4% (95% CI, 74.6–96.2%), 72.8% (95% CI, 59.2–86.4%), and 82.4% (95% CI, 70.7–94.1%), respectively. Overall, grade 3 toxicities occurred in 21 patients (51.2%), most commonly mucositis (39.0%), neutropenia (9.8%), or dysphagia (4.9%). A grade 4 adverse event was observed in only one patient with neutropenia. Conclusions CCRT with weekly docetaxel and cisplatin shows promising antitumor activity with manageable toxicity profiles for patients with locally advanced SCCHN.
Authors
Lee, JY; Sun, JM; Oh, D; Lim, SH; Chi, S; Lee, SH; Jung, SH; Ahn, MJ; Ahn, YC; Park, K
MLA Citation
Lee, J. Y., et al. “A phase II trial of concurrent chemoradiotherapy with weekly docetaxel plus cisplatin treatment for unresectable locally advanced head and neck cancer.” Radiotherapy and Oncology, vol. 122, no. 2, Feb. 2017, pp. 217–23. Scopus, doi:10.1016/j.radonc.2016.09.015.
URI
https://scholars.duke.edu/individual/pub1165802
Source
scopus
Published In
Radiotherapy and Oncology
Volume
122
Published Date
Start Page
217
End Page
223
DOI
10.1016/j.radonc.2016.09.015