11 research outputs found
Bayesian Analysis of Diagnostic Test Accuracy When Disease State is Unverified for Some Subjects
Statistical issues in the comparison of quantitative imaging biomarker algorithms using pulmonary nodule volume as an example
Quantitative imaging biomarkers: A review of statistical methods for computer algorithm comparisons
Quantitative biomarkers from medical images are becoming important tools for clinical diagnosis, staging, monitoring, treatment planning, and development of new therapies. While there is a rich history of the development of quantitative imaging biomarker (QIB) techniques, little attention has been paid to the validation and comparison of the computer algorithms that implement the QIB measurements. In this paper we provide a framework for QIB algorithm comparisons. We first review and compare various study designs, including designs with the true value (e.g. phantoms, digital reference images, and zero-change studies), designs with a reference standard (e.g. studies testing equivalence with a reference standard), and designs without a reference standard (e.g. agreement studies and studies of algorithm precision). The statistical methods for comparing QIB algorithms are then presented for various study types using both aggregate and disaggregate approaches. We propose a series of steps for establishing the performance of a QIB algorithm, identify limitations in the current statistical literature, and suggest future directions for research
A Causal Roadmap for Generating High-Quality Real-World Evidence
Increasing emphasis on the use of real-world evidence (RWE) to support
clinical policy and regulatory decision-making has led to a proliferation of
guidance, advice, and frameworks from regulatory agencies, academia,
professional societies, and industry. A broad spectrum of studies use
real-world data (RWD) to produce RWE, ranging from randomized controlled trials
with outcomes assessed using RWD to fully observational studies. Yet many RWE
study proposals lack sufficient detail to evaluate adequacy, and many analyses
of RWD suffer from implausible assumptions, other methodological flaws, or
inappropriate interpretations. The Causal Roadmap is an explicit, itemized,
iterative process that guides investigators to pre-specify analytic study
designs; it addresses a wide range of guidance within a single framework. By
requiring transparent evaluation of causal assumptions and facilitating
objective comparisons of design and analysis choices based on pre-specified
criteria, the Roadmap can help investigators to evaluate the quality of
evidence that a given study is likely to produce, specify a study to generate
high-quality RWE, and communicate effectively with regulatory agencies and
other stakeholders. This paper aims to disseminate and extend the Causal
Roadmap framework for use by clinical and translational researchers, with
companion papers demonstrating application of the Causal Roadmap for specific
use cases.Comment: 51 pages, 2 figures, 4 table