6710B ROCKLEDGE DRIVE
BETHESDA MD 20817
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Rajeshwari Sundaram, M.Stat., Ph.D., is a senior investigator in the Biostatistics and Bioinformatics Branch. Dr Sundaram earned her master's degree in statistics from Indian Statistical Institute, Kolkata, and Ph.D. from Michigan State University. Her research interests include development of statistical methods for survival outcomes, especially multivariate survival outcomes and recurrent events, joint modeling of longitudinal data and survival data with application to reproductive outcomes. She is also interested in developing statistical methods for assessing the effects of chemical mixtures on survival outcomes, as well as methods for analyzing exposome type of data. Additionally, she is working on Machine Learning methods for analyzing data coming from continuous monitoring devices, like actigraphy.
Her research is at the intersection of statistical methods and public health. She has long-standing research interests in survival analysis, longitudinal and hierarchical data with applications to reproductive epidemiology and perinatal epidemiology. Over past few years, she has worked to develop statistical methods aimed at building biologically meaningful models to better assess fecundity and fertility, with a focus on building individualized risk prediction for conception delay and infertility, issues of considerable interest in light of the changing profile of couples attempting pregnancy for the first time. She is also currently interested in contributing to better guidelines for labor management in pregnant women by developing methods to address labor progression and by assessing labor arrests and other factors leading to medical intervention. Her interests also encompass developing methods to assess the effects of chemical toxicant mixtures on various reproductive and child development outcomes.
Joint modelling of competing risks and current status data: an application to a spontaneous labour study
Youjin Lee, Mei‐Cheng Wang, Katherine L. Grantz, Rajeshwari Sundaram
Appl. Statist. (2019) 68, Par t 4, pp . 1167–1182
Analysis of Gap Times Based on Panel Count Data With Informative Observation Times and Unknown Start Time
Ling Ma & Rajeshwari Sundaram
Journal of the American Statistical Association (2018), 113:521, 294-305
Couples' body composition and time-to-pregnancy
Sundaram R, Mumford SL, Buck Louis GM.
Hum Reprod. 2017;32(3):662‐668. doi:10.1093/humrep/dex001
Accounting for length-bias and selection effects in estimating the distribution of menstrual cycle length
Kirsten J. Lum, Rajeshwari Sundaram, Thomas A. Louis
Biostatistics, Volume 16, Issue 1, January 2015, Pages 113–128
A survival analysis approach to modeling human fecundity
Sundaram R, McLain AC, Buck Louis GM