Please use this identifier to cite or link to this item: http://hdl.handle.net/11054/1738
Title: Could simulation methods solve the curse of sparse data within clinical studies of antibiotic resistance?
Author: Hurley, James C.
Brownridge, David
Issue Date: 2021
Publication Title: JAC-Antimicrobial Resistance
Volume: 3
Issue: 1
Abstract: Infectious disease (ID) physicians and ID pharmacists commonly confront therapeutic questions relating to antibiotic resistance. Randomized controlled trial data are few and meta-analytic-based approaches to develop the evidence-base from several small studies that might relate to an antibiotic resistance question are not simple. The overriding challenge is the sparsity of data which is problematic for traditional frequentist methods, being the paradigm underlying the derivation of ‘P value’ inferential statistics. In other sparse data contexts, simulation methods enable answers to key questions that are meaningful, quantitative and potentially relevant. How these simulation methods ‘work’ and how Bayesian-based methods, being not ‘P value based’, can facilitate simulation are reviewed. These methods are becoming increasingly accessible. This review highlights why sparse data is less of an issue within Bayesian versus frequentist paradigms. A fictional pharmacokinetic study with sparse data illustrates a simplistic application of Bayesian and simulation methods to antibiotic dosing. Whether within epidemiological projections or clinical studies, simulation methods are likely to play an increasing role in antimicrobial resistance research within both hospital and community studies of either rare infectious disease or infections within specific population groups.
URI: http://hdl.handle.net/11054/1738
DOI: https://doi.org/10.1093/jacamr/dlab016
Internal ID Number: 01700
Health Subject: ANTIBIOTICS
ANTIBIOTIC RESISTANCE
BACTERIAL
COMMUNICABLE DISEASES
DRUG RESISTANCE
MICROBIAL
PHARMACISTS
INFECTIONS
EVIDENCE-BASED PRACTICE
COMMUNITY
Type: Journal Article
Article
Appears in Collections:Research Output

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