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      Improving the evaluation of COPD exacerbation treatment effects by accounting for early treatment discontinuations: a post-hoc analysis of randomized clinical trials

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          Abstract

          Background

          Chronic obstructive pulmonary disease (COPD) clinical trials aimed at evaluating treatment effects on exacerbations often suffer from early discontinuations of randomized treatment. Treatment discontinuations imply a loss of information and should ideally be considered in the statistical analysis of trial results, particularly if the discontinuations are related to the disease or treatment itself. Here, we explore this issue by investigating (1) whether there exists an association between the risks of exacerbation and treatment discontinuation in COPD clinical trials and (2) whether disregarding this association can cause bias in exacerbation treatment effect estimates. We focus on the hypothetical estimand, i.e. the treatment effect that would have been observed had all subjects completed the trial as planned.

          Methods

          The association between exacerbation and discontinuation risks was analysed by applying a joint frailty (random effect) model – allowing for the simultaneous analysis of multiple types of correlated events – to data from five Phase III-IV COPD clinical trials. Specifically, the impact of the association on exacerbation treatment effect estimates was assessed by comparing the treatment hazard ratios of the joint frailty model to the rate/hazard ratios of two related statistical models (the negative binomial and shared frailty models), which both assume discontinuations to be unrelated to the trial outcome. The models were also compared using simulated data.

          Results

          A statistically significant ( p < 0.0001), positive association between exacerbation and discontinuation risks was found in all trials. Importantly, simulations confirmed that – with such an association – models disregarding the association risk producing biased results (> 5 percentage point difference in hazard/rate ratio). For some treatment comparisons in the clinical trials, the difference in treatment effect estimates between the joint frailty and the other models was as high as 10–15 percentage points. The difference was affected by the strength of the exacerbation-discontinuation association, the population heterogeneity in exacerbation risk, and the difference in discontinuation rates between treatment arms.

          Conclusions

          We have identified an association between the risks of exacerbation and treatment discontinuation in five COPD clinical trials. We recommend using the joint frailty model to account for this association when estimating exacerbation treatment effects, particularly when targeting the hypothetical estimand.

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          Most cited references23

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          Susceptibility to exacerbation in chronic obstructive pulmonary disease.

          Although we know that exacerbations are key events in chronic obstructive pulmonary disease (COPD), our understanding of their frequency, determinants, and effects is incomplete. In a large observational cohort, we tested the hypothesis that there is a frequent-exacerbation phenotype of COPD that is independent of disease severity. We analyzed the frequency and associations of exacerbation in 2138 patients enrolled in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study. Exacerbations were defined as events that led a care provider to prescribe antibiotics or corticosteroids (or both) or that led to hospitalization (severe exacerbations). Exacerbation frequency was observed over a period of 3 years. Exacerbations became more frequent (and more severe) as the severity of COPD increased; exacerbation rates in the first year of follow-up were 0.85 per person for patients with stage 2 COPD (with stage defined in accordance with Global Initiative for Chronic Obstructive Lung Disease [GOLD] stages), 1.34 for patients with stage 3, and 2.00 for patients with stage 4. Overall, 22% of patients with stage 2 disease, 33% with stage 3, and 47% with stage 4 had frequent exacerbations (two or more in the first year of follow-up). The single best predictor of exacerbations, across all GOLD stages, was a history of exacerbations. The frequent-exacerbation phenotype appeared to be relatively stable over a period of 3 years and could be predicted on the basis of the patient's recall of previous treated events. In addition to its association with more severe disease and prior exacerbations, the phenotype was independently associated with a history of gastroesophageal reflux or heartburn, poorer quality of life, and elevated white-cell count. Although exacerbations become more frequent and more severe as COPD progresses, the rate at which they occur appears to reflect an independent susceptibility phenotype. This has implications for the targeting of exacerbation-prevention strategies across the spectrum of disease severity. (Funded by GlaxoSmithKline; ClinicalTrials.gov number, NCT00292552.)
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            Blood eosinophil count and exacerbations in severe chronic obstructive pulmonary disease after withdrawal of inhaled corticosteroids: a post-hoc analysis of the WISDOM trial

            Blood eosinophil counts might predict response to inhaled corticosteroids (ICS) in patients with chronic obstructive pulmonary disease (COPD) and a history of exacerbations. We used data from the WISDOM trial to assess whether patients with COPD with higher blood eosinophil counts would be more likely to have exacerbations if ICS treatment was withdrawn.
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              Clinical COPD phenotypes: a novel approach using principal component and cluster analyses.

              Classification of chronic obstructive pulmonary disease (COPD) is usually based on the severity of airflow limitation, which may not reflect phenotypic heterogeneity. Here, we sought to identify COPD phenotypes using multiple clinical variables. COPD subjects recruited in a French multicentre cohort were characterised using a standardised process. Principal component analysis (PCA) was performed using eight variables selected for their relevance to COPD: age, cumulative smoking, forced expiratory volume in 1 s (FEV(1)) (% predicted), body mass index, exacerbations, dyspnoea (modified Medical Research Council scale), health status (St George's Respiratory Questionnaire) and depressive symptoms (hospital anxiety and depression scale). Patient classification was performed using cluster analysis based on PCA-transformed data. 322 COPD subjects were analysed: 77% were male; median (interquartile range) age was 65.0 (58.0-73.0) yrs; FEV(1) was 48.9 (34.1-66.3)% pred; and 21, 135, 107 and 59 subjects were classified in Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages 1, 2, 3 and 4, respectively. PCA showed that three independent components accounted for 61% of variance. PCA-based cluster analysis resulted in the classification of subjects into four clinical phenotypes that could not be identified using GOLD classification. Importantly, subjects with comparable airflow limitation (FEV(1)) belonged to different phenotypes and had marked differences in age, symptoms, comorbidities and predicted mortality. These analyses underscore the need for novel multidimensional COPD classification for improving patient care and quality of clinical trials.
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                Author and article information

                Contributors
                alexandra.jauhiainen@astrazeneca.com
                Journal
                Respir Res
                Respir. Res
                Respiratory Research
                BioMed Central (London )
                1465-9921
                1465-993X
                22 June 2020
                22 June 2020
                2020
                : 21
                : 158
                Affiliations
                [1 ]GRID grid.418151.8, ISNI 0000 0001 1519 6403, Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, ; Gothenburg, Sweden
                [2 ]GRID grid.412041.2, ISNI 0000 0001 2106 639X, Biostatistics Team, INSERM CR1219, , University of Bordeaux, ; Bordeaux, France
                [3 ]GRID grid.417815.e, ISNI 0000 0004 5929 4381, BioPharmaceuticals R&D, AstraZeneca, ; Cambridge, UK
                [4 ]GRID grid.266813.8, ISNI 0000 0001 0666 4105, University of Nebraska Medical Center, ; Omaha, NE USA
                [5 ]GRID grid.418151.8, ISNI 0000 0001 1519 6403, BioPharma Early Biometrics and Statistical Innovation, Data Science & AI, BioPharmaceuticals R&D, AstraZeneca, ; Pepparedsleden 1, SE-431 83 Mölndal, Sweden
                Author information
                http://orcid.org/0000-0001-9714-7962
                Article
                1419
                10.1186/s12931-020-01419-8
                7310001
                8e7c7856-f1e3-4c6f-80c0-8dbe94ea9502
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 17 October 2019
                : 9 June 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004325, AstraZeneca;
                Award ID: Not applicable
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Respiratory medicine
                copd,early treatment discontinuations,dropouts,exacerbations,joint frailty model,recurrent events,survival analysis

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