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      Later-age neutering causes lower risk of early‐onset urinary incontinence than early neutering–a VetCompass target trial emulation study

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          Abstract

          There is growing evidence supporting clinically important associations between age at neutering in bitches and subsequent urinary incontinence (UI), although much of this evidence to date is considered weak. Target trial emulation is an innovative approach in causal inference that has gained substantial attention in recent years, aiming to simulate a hypothetical randomised controlled trial by leveraging observational data. Using anonymised veterinary clinical data from the VetCompass Programme, this study applied the target trial emulation framework to determine whether later-age neutering (≥ 7 to ≤ 18 months) causes decreased odds of early-onset UI (diagnosed < 8.5 years) compared to early-age neutering (3 to < 7 months). The study included bitches in the VetCompass database born from January 1, 2010, to December 31, 2012, and neutered between 3 and 18 months old. Bitches were retrospectively confirmed from the electronic health records as neutered early or later. The primary outcome was a diagnosis of early-onset UI. Informed from a directed acyclic graph, data on the following covariates were extracted: breed, insurance status, co-morbidities and veterinary group. Inverse probability of treatment weighting was used to adjust for confounding, with inverse probability of censoring weighting accounting for censored bitches. The emulated trial included 612 early-age neutered bitches and 888 later-age neutered bitches. A pooled logistic regression outcome model identified bitches neutered later at 0.80 times the odds (95% CI 0.54 to 0.97) of early-onset UI compared with bitches neutered early. The findings show that later-age neutering causes reduced odds of early-onset UI diagnosis compared with early-age neutering. Decision-making on the age of neutering should be carefully considered, with preference given to delaying neutering until after 7 months of age unless other major reasons justify earlier surgery. The study is one of the first to demonstrate successful application of the target trial framework to veterinary observational data.

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          An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

          The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
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            Sensitivity Analysis in Observational Research: Introducing the E-Value.

            Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
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              Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

              The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Data curationRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                3 July 2024
                2024
                : 19
                : 7
                : e0305526
                Affiliations
                [1 ] Pathobiology and Population Sciences, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, Herts, United Kingdom
                [2 ] Department of Statistical Science, University College London, London, United Kingdom
                [3 ] Research Support Office, The Royal Veterinary College, Hatfield, Herts, United Kingdom
                [4 ] School of Veterinary Medicine and Science, University of Nottingham, Nottingham, Sutton Bonington, United Kingdom
                [5 ] Clinical Science and Services, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield, Herts, United Kingdom
                University of Life Sciences in Lublin, POLAND
                Author notes

                Competing Interests: The authors have declared no competing interests exist.

                Author information
                https://orcid.org/0000-0002-8367-5294
                Article
                PONE-D-24-08378
                10.1371/journal.pone.0305526
                11221680
                38959183
                1acbadbb-32db-4d41-9944-7c05e303d855
                © 2024 Pegram et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 6 March 2024
                : 2 June 2024
                Page count
                Figures: 2, Tables: 3, Pages: 18
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100021270, Dogs Trust;
                Award ID: 5654
                Award Recipient :
                CP is supported at the RVC by an award from the Dogs Trust Canine Welfare Grants (number 5654). URL: https://www.dogstrust.org.uk/how-we-help/the-future/research The funders reviewed the manuscript and were involved in the decision to publish, but did not play a role in study design, data collection or analysis.
                Categories
                Research Article
                Medicine and Health Sciences
                Health Care
                Health Information Technology
                Electronic Medical Records
                Computer and Information Sciences
                Information Technology
                Health Information Technology
                Electronic Medical Records
                Biology and Life Sciences
                Veterinary Science
                Veterinary Medicine
                Veterinary Diagnostics
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Biology and Life Sciences
                Organisms
                Eukaryota
                Animals
                Vertebrates
                Amniotes
                Mammals
                Dogs
                Biology and Life Sciences
                Zoology
                Animals
                Vertebrates
                Amniotes
                Mammals
                Dogs
                Engineering and Technology
                Management Engineering
                Risk Management
                Insurance
                Computer and Information Sciences
                Information Theory
                Graph Theory
                Directed Graphs
                Directed Acyclic Graphs
                Physical Sciences
                Mathematics
                Graph Theory
                Directed Graphs
                Directed Acyclic Graphs
                Biology and Life Sciences
                Veterinary Science
                Veterinary Medicine
                Veterinary Surgery
                Medicine and Health Sciences
                Clinical Medicine
                Clinical Trials
                Randomized Controlled Trials
                Medicine and Health Sciences
                Pharmacology
                Drug Research and Development
                Clinical Trials
                Randomized Controlled Trials
                Research and Analysis Methods
                Clinical Trials
                Randomized Controlled Trials
                Custom metadata
                The data underlying the results presented in the study are available from: https://github.com/cpegram92/causal-inference-phd.

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