The Postdoctoral Research Fellow in Biostatistics at the Harvard T. H. Chan School of Public Health will develop methodology in the areas of longitudinal and survival data analyses. The application of the methods will be used in epidemiologic studies. The selected candidate will work with Prof. Molin Wang (HSPH), Prof. Donna Spiegelman (Yale), Prof. Bernard Rosner and their collaborators. More specifically, the selected candidate be involved with two main projects:

1) Statistical Methods to Account for Exposure Uncertainty in Environmental Epidemiology (MEEE): The selected candidate will focus on methodologic innovations in several critical areas of environmental health related to the effects of multiple constituents of air pollution and of aspects of the neighborhood environment on risks of cardiovascular disease incidence, its precursors and consequences. The methodologic innovations to be developed will address the unique measurement error features of these data, and will make it possible to accurately quantify the effects of complex single and multiple simultaneous exposure effects across space and time, responding to another NIEHS strategic priority, on critical health outcomes across the life course, overcoming bias and loss of efficiency otherwise present due to the presence of substantial measurement error.

2) Hearing loss method grant: The selected candidate will develop entirely novel analytical methods for quality control of hearing data, and validly and efficiently quantifying not only the exposure-hearing loss associations but also their causal relationships, while handling the various layers of correlation and multivariate outcomes arising from the measurement of multiple frequencies in audiometrically-assessed hearing data. In addition, they will correct for measurement error-induced bias in the estimates of the associations and causal effects in studies where hearing outcomes are evaluated in non-clinical settings. They will apply the hearing data analysis methods to the Conservation of Hearing Study based within the Nurses’ Health Study II.

Education Requirements
A doctoral degree, such as Ph.D., Sc.D., Dr.PH. in statistics, biostatistics, or a related field.

Technical Skill Requirements
Strong background/expertise in clustered data, survival data, causal inference or measurement error

Preferred Experience/Skills
Strong computing skill desired
Excellent communication and writing skills