Publications by authors named "Tim Reason"
Background: Generative artificial intelligence (GenAI) shows promise in automating key tasks involved in conducting systematic literature reviews (SLRs), including screening, bias assessment and data extraction. This potential automation is increasingly relevant as pharmaceutical developers face challenging requirements for timely and precise SLRs using the population, intervention, comparator and outcome (PICO) framework, such as those under the impending European Union (EU) Health Technology Assessment Regulation 2021/2282 (HTAR). This proof-of-concept study aimed to evaluate the feasibility, accuracy and efficiency of using GenAI for mass extraction of PICOs from PubMed abstracts.
View Article and Find Full Text PDFBackground: The emergence of artificial intelligence, capable of human-level performance on some tasks, presents an opportunity to revolutionise development of systematic reviews and network meta-analyses (NMAs). In this pilot study, we aim to assess use of a large-language model (LLM, Generative Pre-trained Transformer 4 [GPT-4]) to automatically extract data from publications, write an R script to conduct an NMA and interpret the results.
Methods: We considered four case studies involving binary and time-to-event outcomes in two disease areas, for which an NMA had previously been conducted manually.
Background: Current generation large language models (LLMs) such as Generative Pre-Trained Transformer 4 (GPT-4) have achieved human-level performance on many tasks including the generation of computer code based on textual input. This study aimed to assess whether GPT-4 could be used to automatically programme two published health economic analyses.
Methods: The two analyses were partitioned survival models evaluating interventions in non-small cell lung cancer (NSCLC) and renal cell carcinoma (RCC).
Introduction: Ertugliflozin is a new sodium-glucose co-transporter-2 inhibitor (SGLT2i) for the treatment of type 2 diabetes mellitus. As there are no head-to-head trials comparing the efficacy of SGLT2is, the primary objective of this analysis was to indirectly compare ertugliflozin to other SGLT2i in patient populations with inadequately controlled glycated hemoglobin (HbA1c > 7.0%) and previously treated with either diet/exercise, metformin alone or metformin plus a dipeptidyl peptidase-4 inhibitor (DPP4i).
View Article and Find Full Text PDFBackground: This was an updated network meta-analysis (NMA) of anti-vascular endothelial growth factor (VEGF) agents and laser photocoagulation in patients with diabetic macular edema (DME). Unlike previous NMA that used meta-regression to account for potential confounding by systematic variation in treatment effect modifiers across studies, this update incorporated individual patient-level data (IPD) regression to provide more robust adjustment.
Methods: An updated review was conducted to identify randomised controlled trials for inclusion in a Bayesian NMA.
Background: A number of long-acting muscarinic antagonist (LAMA)/long-acting β2-agonist (LABA) fixed-dose combinations (FDCs) for treatment of moderate-to-very severe chronic obstructive pulmonary disease (COPD) have recently become available, but none have been directly compared in head-to-head randomized controlled trials (RCTs). The purpose of this study was to assess the relative clinical benefit of all currently available LAMA/LABA FDCs using a Bayesian network meta-analysis (NMA).
Methods: A systematic literature review identified RCTs investigating the efficacy, safety and quality of life associated with licensed LAMA/LABA FDCs for the treatment of moderate-to-very severe COPD.