Research Summary

Shanshan Zhao, Ph.D., joined the NIEHS Biostatistics and Computational Biology Branch as a principal investigator in January 2015. She holds a secondary appointment in the NIEHS Epidemiology Branch, and an adjunct associate professorship at the Department of Biostatistics, University of North Carolina at Chapel Hill.

The overarching goal of Zhao’s research is to develop novel statistical methods to discover how humans’ interaction with the physical and social environments influence their health and well-being. By developing and applying powerful statistical tools, she aims to elucidate the etiology of various health outcomes through assessing risk factors and further building robust risk prediction models. These findings can be used to facilitate decision-making for patients, clinicians, and policymakers. Toward achieving this overarching goal, Zhao and her group employ a dynamic approach by integrating statistical methodological developments and collaborative population-based studies to reveal the social and physical environmental determinants for health outcomes in two major directions.

First, Zhao’s group develop general statistical methods for disease risk assessment, with a focus on time-to-event outcomes and longitudinal data. A lot of biological studies follow time to occurrences for multiple events (e.g., onset of diseases or death), which allows researchers to model the relationships between multiple diseases throughout time and across risk factors. Using such data, she developed joint models for multivariate time-to-event outcomes. Building upon the in-depth understanding of multivariate censored data, she developed innovative correction methods for multiple correlated biomarkers subject to limit of detection (LOD). Another area of long-term interest is statistical learning methods for survival data, especially in the context of joint modeling of survival and high-dimensional longitudinal data and dynamic risk prediction.

Secondly, Zhao’s group develop statistical methods for cancer and environmental epidemiological studies, motivated by the collaborations with other NIEHS researchers. Based on the NIEHS Sister Study, they developed efficient tools for several aspects of breast cancer, including characterizing the spatial and temporal distributions of cancer incidence and mortality, addressing the impact of pre-invasive conditions, and developing risk prediction models for family-history-enriched population. They are also engaged in building analysis pipelines for emerging biological data types, such as environmental mixtures and microbiome. An area of special interest is on developing statistical methods for environmental mixtures to account for the synergistic and antagonistic interactions between chemicals, which take scientific understandings from animal and toxicological studies into account .

Through these projects, Zhao and her group contribute to advance environmental health sciences, to promote data to knowledge to action and to enhance stewardship and support in environmental health sciences, which are the three missions of NIEHS.

Software

• mhazard
  A R package for multivariate survival function estimation and regression
• LAmortBrCaShiny
  R Shiny App for an application of the AFT survival model to SEER breast cancer data in Louisiana
• Semi-mLOD
  R code for handling a generalized linear model with multiple covariates subject to limit-of-detection through semiparametric AFT models of the true covariate values.
• STAFT
  BUGS code for a spatio-temporal accelerated failure time model. Some of these models include an automation step as part of the temporal fitting component.

Selected Publications

1. Prentice R.L., Zhao S. (2019). The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach. Chapman & Hall/CRC Press. 
2. Carroll R., Ish J.L., Sandler D.P., White A.J., Zhao S. (2023). Understanding the Role of Environmental and Socioeconomic Factors in the Geographic Variation of Breast Cancer Risk in the US-Wide Sister Study. Environmental Research, 239(Pt1): 117349. PMID: 37821066; PMCID: PMC10841999; DOI: 10.1016/j.envres.2023.117349. [Abstract]
   • NIEHS Paper of the Year — 2023
   • NIEHS Paper of the Month — December 2023
3. Chen L., Fine J.P., Bair E., Ritter V.S., McElrath T.F., Cantonwine D.E., Meeker J.D., Ferguson K.K, Zhao S. (2022). Semiparametric Analysis of a Generalized Linear Model with Multiple Covariates Subject to Detection Limits. Statistics in Medicine, 41(24): 4791-4808. PMID: 35909228; PMC9588684; DOI: 10.1002/sim.9536. [Abstract]
4. Kim J., Fine J.P., Sandler D.P., Zhao S. (2022). Accounting for Preinvasive Conditions in Analysis of Invasive Cancer Risk: Application to Breast Cancer. Epidemiology, 33(1): 48-54. PMID: 34561346; PMCID: PMC8633059; DOI: 10.1097/EDE.0000000000001423. [Abstract]
5. Prentice R., Zhao S. (2021). Regression Models and Multivariate Life Tables. Journal of American Statistical Association, 116(535): 1330-1345. PMID: 34629570; PMCID: PMC8494047; DOI: 10.1080/01621459.2020.1713792. [Abstract]
6. Carroll R., White A.J., Keil A.P., Meeker J.D., McElrath T.F., Zhao S., Ferguson K.K. (2019). Latent Classes for Chemical Mixtures Analyses in Epidemiology: An Example Using Phthalate and Phenol Exposure Biomarkers in Pregnant Women. Journal of Exposure Science & Environmental Epidemiology. PMID: 31636370; PMCID: PMC6917962; DOI: 10.1038/s41370-019-0181-y. [Abstract Carroll R., White A.J., Keil A.P., Meeker J.D., McElrath T.F., Zhao S., Ferguson K.K. (2019). Latent Classes for Chemical Mixtures Analyses in Epidemiology: An Example Using Phthalate and Phenol Exposure Biomarkers in Pregnant Women. Journal of Exposure Science & Environmental Epidemiology. PMID: 31636370; PMCID: PMC6917962; DOI: 10.1038/s41370-019-0181-y.] 
7. Carroll R., Lawson A.B., Zhao S. (2019). A data-driven approach for estimating the change-points and impact of major events on disease risk. Spat Spatiotemporal Epidemiol 29:111-118. [Abstract Carroll R., Lawson A.B., Zhao S. (2019). A data-driven approach for estimating the change-points and impact of major events on disease risk. Spat Spatiotemporal Epidemiol 29:111-118.] 
8. Carroll R, Zhao S. 2019. Trends in Colorectal Cancer Incidence and Survival in Iowa SEER Data: The Timing of It All. Clin Colorectal Cancer 18(2):e261-e274. [Abstract Carroll R, Zhao S. 2019. Trends in Colorectal Cancer Incidence and Survival in Iowa SEER Data: The Timing of It All. Clin Colorectal Cancer 18(2):e261-e274.] 
9. Carroll R., Lawson A.B., Zhao S. (2018). Temporally dependent accelerated failure time model for capturing the impact of events that alter survival in disease mapping. Biostatistics. PMID: 29939209; PMCID: PMC Journal - In Process; DOI:10.1093/biostatistics/kxy023. [Abstract Carroll R., Lawson A.B., Zhao S. (2018). Temporally dependent accelerated failure time model for capturing the impact of events that alter survival in disease mapping. Biostatistics. PMID: 29939209; PMCID: PMC Journal - In Process; DOI:10.1093/biostatistics/kxy023.] 
10. Jiang Y., Weinberg C.R., Sandler D.P., Zhao S. (2019). Use of Detailed Family History Data to Improve Risk Prediction, with Application to Breast Cancer. PLOS One. PMID: 31846476; PMCID: PMC6917296; DOI: 10.1371/journal.pone.0226407. [Abstract Jiang Y., Weinberg C.R., Sandler D.P., Zhao S. (2019). Use of Detailed Family History Data to Improve Risk Prediction, with Application to Breast Cancer. PLOS One. PMID: 31846476; PMCID: PMC6917296; DOI: 10.1371/journal.pone.0226407.] 
11. Zhao S., Leonardson A., Geybels M., McDaniel A., Yu M., Kolb S., Zong H., Carter K., Siddiqui J., Cheng, A., Wright J.L., Pritchard C.C., Lance R., Troyer D., Fan J., Ostrander E.A., Dai J., Tomlins S., Feng Z., Stanford J.L. (2018). A Five-CpG DNA Methylation Score to Predict Metastatic-Lethal Outcomes in Men Treated with Radical Prostatectomy for Localized Prostate Cancer. Prostate. PMID: 29956356; PMCID: PMC6120526; DOI: 10/1002/pros.23667. [Abstract Zhao S., Leonardson A., Geybels M., McDaniel A., Yu M., Kolb S., Zong H., Carter K., Siddiqui J., Cheng, A., Wright J.L., Pritchard C.C., Lance R., Troyer D., Fan J., Ostrander E.A., Dai J., Tomlins S., Feng Z., Stanford J.L. (2018). A Five-CpG DNA Methylation Score to Predict Metastatic-Lethal Outcomes in Men Treated with Radical Prostatectomy for Localized Prostate Cancer. Prostate. PMID: 29956356; PMCID: PMC6120526; DOI: 10/1002/pros.23667.] 
12. Prentice R.L., Zhao S. (2018). Nonparametric Estimation of the Multivariate Survivor Function: The Multivariate Kaplan-Meier Estimator. Lifetime Data Analysis, 24 (1): 3-27. PMID: 27677472; PMCID: PMC5373162; DOI: 10.1007/s10985-016-93830y. [Abstract Prentice R.L., Zhao S. (2018). Nonparametric Estimation of the Multivariate Survivor Function: The Multivariate Kaplan-Meier Estimator. Lifetime Data Analysis, 24 (1): 3-27. PMID: 27677472; PMCID: PMC5373162; DOI: 10.1007/s10985-016-93830y.] 
13. Carroll R, Zhao S. 2018. Gaining relevance from the random: Interpreting observed spatial heterogeneity. Spat Spatiotemporal Epidemiol 25:11-17. [Abstract Carroll R, Zhao S. 2018. Gaining relevance from the random: Interpreting observed spatial heterogeneity. Spat Spatiotemporal Epidemiol 25:11-17.] 
14. Zhao S., Geybels M.S., Leonardson A., Rubicz R., Kolb S., Yan Q., Klotzle B., Bibikova M., Hurtado-Coll A., Troyer D., Lance R., Lin D.W., Wright J.L., Ostrander E.A., Fan J.B., Feng Z., Stanford J.L. (2017). Epigenome-wide Tumor DNA Methylation Profiling Identifies Novel Prognostic Biomarkers of Metastatic-lethal Progression in Men Diagnosed with Clinically Localized Prostate Cancer. Clinical Cancer Research, 23: 311-319. PMID: 27358489; PMCID: PMC5199634; DOI: 10.1158/10780432.CCR-16-0549. [Abstract Zhao S., Geybels M.S., Leonardson A., Rubicz R., Kolb S., Yan Q., Klotzle B., Bibikova M., Hurtado-Coll A., Troyer D., Lance R., Lin D.W., Wright J.L., Ostrander E.A., Fan J.B., Feng Z., Stanford J.L. (2017). Epigenome-wide Tumor DNA Methylation Profiling Identifies Novel Prognostic Biomarkers of Metastatic-lethal Progression in Men Diagnosed with Clinically Localized Prostate Cancer. Clinical Cancer Research, 23: 311-319. PMID: 27358489; PMCID: PMC5199634; DOI: 10.1158/10780432.CCR-16-0549.]
    • NIEHS Paper of the Month — September 2016
15. Carroll R., Lawson A.B., Zhao S. (2017). Assessment of Spatial Variation in Breast Cancer-Specific Mortality Using Louisiana SEER Data. Social Science & Medicine, 193: 1-7. PMID 28985516; PMCID: PMC5659900; DOI: 10.1016/j.socscimed.2017.09.045. [Abstract]
16. Zhao S., Zheng Y., Prentice R.L., Feng, Z. (2015). Estimation from a Two-Stage Biomarker Study Allowing Early Termination for Futility. Biostatistics. doi: 10.1093/biostatistics/kxv017.
17. Zhao S., Prentice R.L. (2014). Covariate Measurement Error Correction Methods in Mediation Analysis with Failure Time Outcome. Biometrics, 70: 835-844. doi: 10.1111/biom.12205. 
    (An earlier version won the 2014 ASA biometrics section David P. Byar travel award.) [Abstract Zhao S., Prentice R.L. (2014). Covariate Measurement Error Correction Methods in Mediation Analysis with Failure Time Outcome. Biometrics, 70: 835-844. doi: 10.1111/biom.12205. 
    (An earlier version won the 2014 ASA biometrics section David P. Byar travel award.)]
18. Zhao S., Chlebowski R.T., Anderson G., Kuller L.H., Manson J.E., Gass M., Patterson R., Rohan T.E., Lane D.S., Beresford S.A.A, Lavasani, S., Rossouw, J.E., Prentice R.L. (2014). Sex Hormone Associations with Breast Cancer Risk and the Mediation of Randomized Trial Postmenopausal Hormone Therapy Effect. Breast Cancer Research. 16: R30. doi:10.1186/bcr3632. [Abstract Zhao S., Chlebowski R.T., Anderson G., Kuller L.H., Manson J.E., Gass M., Patterson R., Rohan T.E., Lane D.S., Beresford S.A.A, Lavasani, S., Rossouw, J.E., Prentice R.L. (2014). Sex Hormone Associations with Breast Cancer Risk and the Mediation of Randomized Trial Postmenopausal Hormone Therapy Effect. Breast Cancer Research. 16: R30. doi:10.1186/bcr3632.]
19. Stott-Miller, M., Zhao S.*, Wright, J.L., Bibikova, M., Klotzle, B., Fan, J., Ostrander, E.A., Feng, Z., and Stanford, J.L. (2014). Validation Study of Candidate Genes with Hypermethylated Promoter Regions Associated with Prostate Cancer Recurrence. Cancer Epidemiology, Biomarkers & Prevention, 23: 1331-1339. doi: 10.1158/1055-9965.EPI-13-1000. (*co-first authors) [Abstract Stott-Miller, M., Zhao S.*, Wright, J.L., Bibikova, M., Klotzle, B., Fan, J., Ostrander, E.A., Feng, Z., and Stanford, J.L. (2014). Validation Study of Candidate Genes with Hypermethylated Promoter Regions Associated with Prostate Cancer Recurrence. Cancer Epidemiology, Biomarkers & Prevention, 23: 1331-1339. doi: 10.1158/1055-9965.EPI-13-1000. (*co-first authors)]
20. Zhao S., Cook ;A.J., Jackson L.A., and Nelson J.C. (2012). Statistical Performance of Group Sequential Methods for Post-Licensure Medical Product Safety Surveillance: A Simulation Study. Statistics and Its Interface. 5: 381-390. doi: http://dx.doi.org/10.4310/SII.2012.v5.n4.a1. [Abstract]