Biostatistics Weekly Seminar

Biomarkers play a critical role in the early diagnosis of disease and can serve as targets for disease interventions. As such, biomarker discovery is of primary scientific interest in many disease settings. One example of this occurs in Alzheimer’s disease (AD), a neurodegenerative disease that affects memory, thinking, and behavior. Amyloid beta (Aβ) and phosphorylated tau (p-tau) are protein biomarkers that have become key to the early diagnosis of AD. In fact, the first novel therapy since 2003, Aduhelm, recently received accelerated approval from the US FDA based on demonstrated changes in Aβ. Despite this recent success, Aβ and p-tau are not perfect discriminators of disease and, hence, biomarker discovery for time-to-progression of disease remains a primary objective in AD research. Analysis of time-to-event data using Cox's proportional hazards (PH) model is ubiquitous in the discovery process. Most commonly, a sample is taken from the population of interest and covariate information is collected on everyone. If the event of interest is rare and it is difficult or not feasible to collect full covariate information for all study participants, the nested case-control design reduces costs with minimal impact on inferential precision. However, no work has been done to investigate the performance of the nested case-control design under model mis-specification. In this talk we show that outside of the semi-parametric PH assumption, the statistical estimand under the nested case-control design will depend not only on the censoring distribution, but also on the number of controls sampled at each event time. This is true in the case of a binary covariate when the proportional hazards assumption is not satisfied, and in the case of a continuous covariate where the functional form is mis-specified. We propose estimators that allow us to recover the statistic that would have been computed under the full cohort data as well as a censoring-robust estimator. Asymptotic distributional theory for the estimators is provided along with empirical simulation results to assess finite samples properties of the estimators. We conclude with examples considering common biomarkers for AD progression using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

Zoom (Link to Follow)
03 November 2021
1:30pm