Borrowing strength from clinical trials in analysing longitudinal data from a treated cohort: Investigating the effectiveness of acetylcholinesterase inhibitors in the management of dementia

Knight, Ruth, Stewart, Robert, Khondoker, Mizanur ORCID: https://orcid.org/0000-0002-1801-1635 and Landau, Sabine (2023) Borrowing strength from clinical trials in analysing longitudinal data from a treated cohort: Investigating the effectiveness of acetylcholinesterase inhibitors in the management of dementia. International Journal of Epidemiology, 52 (3). 827–836. ISSN 0300-5771

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Abstract

Background: Health care professionals seek information about effectiveness of treatments in patients who would be offered them in routine clinical practice. Electronic medical records (EMRs) and randomized controlled trials (RCTs) can both provide data on treatment effects; however, each data source has limitations when considered in isolation. Methods: A novel modelling methodology which incorporates RCT estimates in the analysis of EMR data via informative prior distributions is proposed. A Bayesian mixed modelling approach is used to model outcome trajectories among patients in the EMR dataset receiving the treatment of interest. This model incorporates an estimate of treatment effect based on a meta-analysis of RCTs as an informative prior distribution. This provides a combined estimate of treatment effect based on both data sources. Results: The superior performance of the novel combined estimator is demonstrated via a simulation study. The new approach is applied to estimate the effectiveness at 12 months after treatment initiation of acetylcholinesterase inhibitors in the management of the cognitive symptoms of dementia in terms of Mini-Mental State Examination scores. This demonstrated that estimates based on either trials data only (1.10, SE = 0.316) or cohort data only (1.56, SE = 0.240) overestimated this compared with the estimate using data from both sources (0.86, SE = 0.327). Conclusions: It is possible to combine data from EMRs and RCTs in order to provide better estimates of treatment effectiveness.

Item Type: Article
Additional Information: Funding information: R.S. is part-funded by: (i) the National Institute for Health Research (NIHR) Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and King’s College London; (ii) the Medical Research Council (MRC) HDR UK DATAMIND hub; (iii) an NIHR Senior Investigator Award; (iv) the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust.
Uncontrolled Keywords: bayesian modelling,randomized controlled trial,acetylcholinesterase inhibitors,cognition,dementia,electronic medical record,epidemiology ,/dk/atira/pure/subjectarea/asjc/2700/2713
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups: Faculty of Science > Research Groups > Norwich Epidemiology Centre
Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Faculty of Medicine and Health Sciences > Research Groups > Epidemiology and Public Health
Faculty of Medicine and Health Sciences > Research Groups > Public Health and Health Services Research (former - to 2023)
Faculty of Medicine and Health Sciences > Research Centres > Population Health
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Depositing User: LivePure Connector
Date Deposited: 21 Sep 2022 10:30
Last Modified: 19 Oct 2023 03:25
URI: https://ueaeprints.uea.ac.uk/id/eprint/88546
DOI: 10.1093/ije/dyac185

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