A comprehensive survey of collaborative biostatistics units in academic health centers

Abstract

The organizational structures of collaborative biostatistics units in academic health centers (AHCs) in the United States and their important contributions to research are an evolving and active area of discussion and inquiry. Collaborative biostatistics units may serve as a centralized resource to investigators across various disciplines or as shared infrastructure for investigators within a discipline (e.g., cancer), or a combination of both. The characteristics of such units vary greatly, and there has been no comprehensive review of their organizational structures described in the literature to date. This manuscript summarizes the current infrastructure of such units using responses from 129 leaders. Most leaders were over 45 years old, held doctoral degrees, and were on a 12‐month appointment. Over half were tenured or on a tenure track and held primary appointments in a school of medicine. Career advancement metrics most important included being funded as co‐investigator on NIH grants and being either first or second author on peer‐reviewed publications. Team composition was diverse in terms of expertise and training, and funding sources were typically hybrid. These results provide a benchmark for collaboration models and evaluation and may be used by institutional administrators as they build, evaluate, or restructure current collaborative quantitative support infrastructure.

Biostatistics deserts and scholarly productivity among US medical schools

“…Despite the documented success of BERD programmes in providing comprehensive and effective statistical expertise, additional strategies for optimizing biostatistical support are needed for historically under-resourced schools (particularly schools that currently lack a BERD programme or a significant portfolio of NIH funding) and schools located in the biostatistics deserts described in our study. In a recent survey of collaborative biostatistics units, 21% included no NIH-funded cores, 14% received no grant funding from the NIH and the median team included only three total members (Hanlon et al, 2022). Small, agile teams of biostatisticians and other research faculty or staff can maximize their impact with the biostatistician role (Tumin, Baumgarten, et al, 2022;Tumin et al, 2020).…”

Documenting and communicating the contributions of embedded statisticians: Show me the value!

“…These burdens are experienced by faculty and staff at every degree level. The metrics that have been previously proposed for collaborative statisticians often assume that the same language is being spoken between the statistician and supervisor (Griffith et al, 2022; Hanlon et al, 2022; Rubio et al, 2011) and may not encapsulate the activities performed by an embedded statistician. Thus, there is a knowledge gap regarding metrics tailored to meet the needs of the embedded statistician and clinical supervisor.…”

“…Embedded statisticians (Figure 1) are isolated statisticians who are employed by other domain‐specific departments where there is a sufficient volume of research being performed to justify an in‐house statistician or analyst. In academic health centres, 18% of statisticians reported primary appointments in clinical departments rather than biostatistics or statistics/data science departments (Hanlon et al, 2022). There are both advantages and disadvantages for statisticians to be embedded in a clinical department (Spratt et al, 2017).…”

A guide to successful management of collaborative partnerships in quantitative research: An illustration of the science of team science

“…Quantitative scientists can have a diverse set of formal training and may identify as biostatisticians, informaticists, data scientists, psychometricians, bioinformaticians, epidemiologists, implementation scientists, and/or engineers. To meet the needs of a data‐rich research environment, many academic health centres have developed “Quantitative Science Units” that house these scientists in a centralized entity that coordinates collaboration between quantitative experts and domain scientists (Hanlon et al, 2022). Recent literature has established strong arguments for Domain Science Units to engage with Quantitative Science Units rather than hiring quantitative scientists on their own (Desai et al, 2022; Welty et al, 2013).…”