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

TL;DR

This paper updates gerontologic biostatistics to GBS 2.0, incorporating modern data science techniques like machine learning, multi-modal data analysis, and reproducibility strategies to advance research involving older adults.

Contribution

It introduces an expanded set of analytical methods and software resources reflecting recent advances in data science for gerontologic research.

Findings

Inclusion of machine learning and high-dimensional -omics analysis.

Strategies for integrating multi-study data.

Emphasis on reproducibility and open science practices.

Abstract

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.