Talks focused on translating recent advances in biostatistics into practice. Biostatistics Seminar Series
At a glance
Opportunity for
  • Continuing education on recent advances in biostatistics
  • All members of the Harvard Catalyst community, but primarily geared toward biostatisticians
Time commitment
  • 2 hours
Session Dates
  • Varies; see below for details

During the 2014-15 academic year, the Harvard Catalyst Biostatistics Program will present a series of talks based on recent advances in biostatistics, but with a focus on translation of those ideas to biostatistical practice. Speakers will provide detailed examples of the application of methods, often including discussion of software, code, and worked examples.

Upcoming Seminars

Survival Analysis with Uncertain Endpoints Using an Internal Validation Subsample

Sharon Xie, PhD
Associate Professor of Biostatistics, Perelman School of Medicine, University of Pennsylvania

October 8, 2014
Harvard School of Public Health, Building 2, Room 426

When a true survival endpoint cannot be assessed for some subjects, an alternative endpoint that measures the true endpoint with error may be collected, which often occurs when obtaining the true endpoint is too invasive or costly. We develop nonparametric and semiparametric estimated likelihood functions that incorporate both uncertain endpoints available for all participants and true endpoints available for only a subset of participants. We propose maximum estimated likelihood estimators of the discrete survival function of time to the true endpoint and of a hazard ratio representing the effect of a binary or continuous covariate assuming a proportional hazards model. We show that the proposed estimators are consistent and asymptotically normal and develop the analytical forms of the variance estimators. Through extensive simulations, we also show that the proposed estimators have little bias compared to the naïve estimator, which uses only uncertain endpoints, and are more efficient with moderate missingness compared to the complete-case estimator, which uses only available true endpoints. We illustrate the proposed method by estimating the risk of developing Alzheimer's disease using data from the Alzheimer's Disease Neuroimaging Initiative.

This event is co-sponsored by the Massachusetts Alzheimer's Disease Research Center (MADRC) at Massachusetts General Hospital.