Michael Griswold | Searcharxiv

arXiv2024

Gerontologic Biostatistics 2.0: Developments over 10+ years in the age of data science

Background: Introduced in 2010, the sub-discipline of gerontologic biostatistics (GBS) was conceptualized to address the specific challenges in analyzing data from research studies involving older adults. However, the evolving technological landscape has catalyzed data science and statistical advancements since the original GBS publication, greatly expanding the scope of gerontologic research. There is a need to describe how these advancements enhance the analysis of multi-modal data and complex phenotypes that are hallmarks of gerontologic research. Methods: This paper introduces GBS 2.0, an updated and expanded set of analytical methods reflective of the practice of gerontologic biostatistics in contemporary and future research. Results: GBS 2.0 topics and relevant software resources include cutting-edge methods in experimental design; analytical techniques that include adaptations of machine learning, quantifying deep phenotypic measurements, high-dimensional -omics analysis; the integration of information from multiple studies, and strategies to foster reproducibility, replicability, and open science. Discussion: The methodological topics presented here seek to update and expand GBS. By facilitating the synthesis of biostatistics and data science in gerontology, we aim to foster the next generation of gerontologic researchers.

stat.ME↗

arXiv2019

Estimating the association between blood pressure variability and cardiovascular disease: An application using the ARIC Study

The association between visit-to-visit systolic blood pressure variability and cardiovascular events has recently received a lot of attention in the cardiovascular literature. But blood pressure variability is usually estimated on a person-by-person basis, and is therefore subject to considerable measurement error. We demonstrate that hazard ratios estimated using this approach are subject to bias due to regression dilution and we propose alternative methods to reduce this bias: a two-stage method and a joint model. For the two-stage method, in stage one repeated measurements are modelled using a mixed effects model with a random component on the residual standard deviation. The mixed effects model is used to estimate the blood pressure standard deviation for each individual, which in stage two is used as a covariate in a time-to-event model. For the joint model, the mixed effects sub-model and time-to-event sub-model are fitted simultaneously using shared random effects. We illustrate the methods using data from the Atherosclerosis Risk in Communities (ARIC) study.

stat.AP↗