Molin Wang

Assistant Professor in the Department of Epidemiology

Channing Laboratory, Department of Medicine, Harvard Medical School
677 Huntington Avenue
Boston, Massachusetts 02115
Phone: 617.432.1412


Dr. Wang’s current methodological research interests are mainly in the areas of  assessment of timing of effect in survival analysis, evaluation of etiological disease heterogeneity and measurement error correction in epidemiological studies. She has also developed semi-parametric methods for reducing the impact of nuisance parameters.

She is the lead statistician for the Pooling Project on Diet and Cancer in Women and Men, the Vitamin D and Breast Cancer/Colorectal Cancer Consortium, and several HIV studies conducted in Uganda and Tanzania. She has also been a lead statistician for the Nurses’ Health Study II and the Health Professionals Follow-up Study, and the lead statistician for several projects based on the Harvard cohorts. She has been actively working on analyses, providing input into the development of analytic procedures and their interpretation, and overseeing software development for the routine implementation of advanced and novel statistical methods.

Before October 2010, she had worked on statistical collaborations in various oncology projects with Harvard – Dana Farber Cancer Institute biomedical investigators and the Eastern Cooperative Oncology Group (ECOG), with a focus on the design and analysis of breast cancer clinical trials.


Ph.D. in Biostatistics, Emory University

Selected Publications

  1. Wang M, Hanfelt JJ. Adjusted profile estimating function. Biometrika 2003;90:845-858.
  2. Wang M, Williamson J, Redline S. A semiparametric method for analyzing matched case-control family studies with a continuous outcome and proband sampling. Biometrics 2004;60(3):644-650.
  3. Wang M, Williamson JM. Generalization of the Mantel-Haenszel estimating function for sparse clustered binary data. Biometrics 2005; 61:973-981.
  4. Wang M, Berger V. Breslow-Day-Statistic. In: Everitt, B, Howell DC, ed. Encyclopedia of Behavioral Statistics, Hoboken, John Wiley & Sons, 2005. DOI: 10.1002/0470013192.bsa062
  5. Wang M, Fitzmaurice G. A simple imputation method for longitudinal studies with non-ignorable non-responses. Biom J 2006;48:302-318.
  6. Wang M, Hanfelt JJ. Orthogonal locally ancillary estimating functions for matched-pair studies and errors-in-covariates. J R Stat Soc-Series B 2007;69:411-428.
  7. Dahlberg SE, Wang M. A proportional hazards cure model for the analysis of time to event with frequently unidentifiable causes. Biometrics 2007; 63(4):1237-1244.
  8. Miller K, Wang M, Gralow J, Dickler M, Cobleigh M, Perez E, Shenkier T, Davidson NE. Paclitaxel Compared with Paclitaxel plus Bevacizumab for Metastatic Breast Cancer: A trial coordinated initial by the Eastern Cooperative Oncology Group (E2100). N Engl J Med 2007; 357:2666-2676.
  9. Sparano JA, Wang M, Martino S, Jones V, Perez EA, Saphner T, Wolff AC, Sledge GW, Wood WC, Davidson NE. Weekly paclitaxel in the adjuvant treatment of breast cancer. N Engl J Med 2008; 358:1663-1671. PMCID: PMC2743943
  10. Wang M, Hanfelt JJ. Robust modified profile estimating function with application to the generalized estimating equation. J Stat Planning Inference. 2008;.138:2029-2044.
  11. Wang M, Hanfelt JJ. Orthogonal locally ancillary estimating function with application to stratified clustered data. Commun in Statistics-Theory and Methods 2008;37:2836-2853.
  12. Wang M, Hanfelt JJ. A robust method for finely stratified familial studies with proband-based sampling. Biostatistics 2009;10:364-373. PMCID: PMC2648900
  13. Zhang JJ, Wang M. An accelerated failure time cure model for time-to-event data with masked cause of failure. Biom J 2009; 51:932-945.
  14. Zhang JJ, Wang M. Latent class joint model of ovarian function suppression and DFS for premenopausal breast cancer patients. Statistics in Medicine 2010;29: 2310-2324. PMCID: PMC3786368
  15. Joseph A. Sparano, Wang M, Fengmin Zhao, Vered Stearns, Silvana Martino, Jennifer A. Ligibel, Edith A. Perez, Tom Saphner, Antonio C. Wolff, George W. Sledge, Jr., William C. Wood, and Nancy E. Davidson. Race and Hormone Receptor–Positive Breast Cancer Outcomes in a Randomized Chemotherapy Trial, Journal of the National Cancer Institute. 2012; 104(5):406-14. PMCID:PMC3295746
  16. Wang, M, Liao, X. Spiegelman, D. Can efficiency be gained by correcting for misclassification? Journal of Statistical Planning and Inference. 2013; 143 (11): 1980-87. PMC3810993
  17. Nishihara R*, Wang M*, Qian ZR*, Baba Y, Yamauchi M, Mima K, Sukawa Y, Kim SA, Inamura K, Zhang X, Wu K, Giovannucci EL, Chan AT, Fuchs CS, Ogino S, Schernhammer ES. Alcohol, one-carbon nutrient intake, and risk of colorectal cancer according to tumor methylation level of IGF2 differentially methylated region. Am J Clin Nutr 2014; 100(6):1479-88. (The first 3 authors contributed equally.  The last 2 authors contributed equally.)
  18. Wang, M., Kuchiba, A, Ogino, S. A Meta-Regression Method for Studying Etiologic Heterogeneity across Disease Subtypes Classified by Multiple Biomarkers. American Journal of Epidemiology 2015; 182(3):263-70.
  19. Ogino S, Campbell PT, Nishihara R, Phipps AI, Beck AH, Sherman ME, Chan AT, Troester MA, Bass AJ, Fitzgerald KC, Irizarry RA, Kelsey KT, Nan H, Peters U, Poole EM, Qian ZR, Tamimi RM, Tchetgen Tchetgen EJ, Tworoger SS, Zhang X, Giovannucci EL, van den Brandt PA, Rosner BA*, Wang M*, Chatterjee N*, Begg CB*.  Proceedings of The Second International Molecular Pathological Epidemiology (MPE) Meeting.  Cancer Causes Cont. In press. *The first 4 and the last 4 authors contributed equally
  20. Wang M, Spiegelman D, Kuchiba A, Lochhead P, Kim S, Chan AT, Poole EM, Tamimi R, Tworoger SS, Giovannucci E, Rosner B, Ogino S. Statistical methods for studying disease subtype heterogeneity. Statistics in Medicine. In press.
  21. Wang M., Xiaomei liao, Laden, F., Spiegelman D. Quantifying risk over the life course-latency, age-related susceptibility, and other time-varying exposure metrics: estimation and inference in prospective cohort studies. Statistics in Medicine. In press.