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Mary D. Sammel, ScD

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Mary D. Sammel, ScD

Professor of Biostatistics

Dr. Sammel's statistical specialties are in multivariate and longitudinal data methods as well as analysis of mixtures of discrete and continuous data. Her research-content areas of interest include environmental and reproductive health. She has extensive experience as a collaborative researcher and has worked with a variety of biomedical investigators (see below).

Dr. Sammel's current methodological work is in latent variable modelling, where the focus is to reduce multivariate data to a smaller number of dimensions and estimate the impact of covariates. This is one way to address the issue of multiple testing for multivariate data. It also provides a means of producing a relative ranking of the subjects with respect to the latent variable. Current manuscripts in progress will evaluate the performance of this global test to standard methods. Work is also in progress to expand programs to deal with ordered categorical responses, which are commonly used to develop health scales.

Dr. Sammel’s statistical scientific leadership in women’s and maternal health research has contributed substantially to the understanding of many issues in this medical area: the understanding of the natural history of menopause, including  more precise prediction of  future health outcomes;  the evaluation of the impact of cancer treatments on markers  of ovarian aging that quantify the influence such treatments have on subsequent reproductive function; the rapid assessment of the risks to fertility of newly developed cancer therapeutics; and the success of major clinical trials of the pathophysiology and treatment of menopausal symptoms; and the evaluation of  biomarkers for early diagnosis of ectopic pregnancy.  She has earned national and international recognition as a statistician  who has greatly influenced and enhanced the understanding of many medical issues relating to women’s health. 

Selected Publications:

  1. Levine LD, Downes KL, Elovitz MA, Parry S, Sammel MD, Srinivas SK.  Mechanical and Pharmacologic Methods of Labor Induction: A Randomized Controlled Trial. Obstetrics and gynecology. 2016; 128(6):1357-1364. NIHMSID: NIHMS822551
  2. Senapati S, Sammel MD, Morse C, Barnhart KT. Impact of endometriosis on in vitro fertilization outcomes: an evaluation of the Society for Assisted Reproductive Technologies Database.  Fertility and sterility. 2016; 106(1):164-171.e1. NIHMSID: NIHMS801246 PMID: 27060727 PMCID:   PMC5173290
  3. Jiang* B, Sammel* MD, Freeman EW, Wang N. Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period.  BMC medical research methodology. 2015; 15:106.  *Equal contributions.
  4. Jiang B, Elliott MR, Sammel MD, Wang N. Joint modeling of cross-sectional health outcomes and longitudinal predictors via mixtures of means and variances.  Biometrics. 2015; 71(2):487-97. NIHMSID: NIHMS680619  PMID: 25652674 PMCID: PMC4480207
  5. Leiby, B.E., TenHave, T.R., Lynch, K.G., Sammel, M.D.  Bayesian multivariate growth curve latent class models for mixed outcomes.  Statistics in Medicine,  Sep 10; 33(20):3434-52, 2014.

My bibliography:

http://www.ncbi.nlm.nih.gov/sites/myncbi/1jiHdt5Ed8L5A/bibliography/47999379/public/?sort=date&direction=descending

Content Area Specialties

Aging, public health, women's health

Methodology Specialties

Categorical data, clustered data, longitudinal methods, multivariate analysis,repeated measures

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