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Selby PJ, Banks RE, Gregory W, Hewison J, Rosenberg W, Altman DG, Deeks JJ, McCabe C, Parkes J, Sturgeon C, Thompson D, Twiddy M, Bestall J, Bedlington J, Hale T, Dinnes J, Jones M, Lewington A, Messenger MP, Napp V, Sitch A, Tanwar S, Vasudev NS, Baxter P, Bell S, Cairns DA, Calder N, Corrigan N, Del Galdo F, Heudtlass P, Hornigold N, Hulme C, Hutchinson M, Lippiatt C, Livingstone T, Longo R, Potton M, Roberts S, Sim S, Trainor S, Welberry Smith M, Neuberger J, Thorburn D, Richardson P, Christie J, Sheerin N, McKane W, Gibbs P, Edwards A, Soomro N, Adeyoju A, Stewart GD, Hrouda D. Methods for the evaluation of biomarkers in patients with kidney and liver diseases: multicentre research programme including ELUCIDATE RCT. PROGRAMME GRANTS FOR APPLIED RESEARCH 2018. [DOI: 10.3310/pgfar06030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BackgroundProtein biomarkers with associations with the activity and outcomes of diseases are being identified by modern proteomic technologies. They may be simple, accessible, cheap and safe tests that can inform diagnosis, prognosis, treatment selection, monitoring of disease activity and therapy and may substitute for complex, invasive and expensive tests. However, their potential is not yet being realised.Design and methodsThe study consisted of three workstreams to create a framework for research: workstream 1, methodology – to define current practice and explore methodology innovations for biomarkers for monitoring disease; workstream 2, clinical translation – to create a framework of research practice, high-quality samples and related clinical data to evaluate the validity and clinical utility of protein biomarkers; and workstream 3, the ELF to Uncover Cirrhosis as an Indication for Diagnosis and Action for Treatable Event (ELUCIDATE) randomised controlled trial (RCT) – an exemplar RCT of an established test, the ADVIA Centaur® Enhanced Liver Fibrosis (ELF) test (Siemens Healthcare Diagnostics Ltd, Camberley, UK) [consisting of a panel of three markers – (1) serum hyaluronic acid, (2) amino-terminal propeptide of type III procollagen and (3) tissue inhibitor of metalloproteinase 1], for liver cirrhosis to determine its impact on diagnostic timing and the management of cirrhosis and the process of care and improving outcomes.ResultsThe methodology workstream evaluated the quality of recommendations for using prostate-specific antigen to monitor patients, systematically reviewed RCTs of monitoring strategies and reviewed the monitoring biomarker literature and how monitoring can have an impact on outcomes. Simulation studies were conducted to evaluate monitoring and improve the merits of health care. The monitoring biomarker literature is modest and robust conclusions are infrequent. We recommend improvements in research practice. Patients strongly endorsed the need for robust and conclusive research in this area. The clinical translation workstream focused on analytical and clinical validity. Cohorts were established for renal cell carcinoma (RCC) and renal transplantation (RT), with samples and patient data from multiple centres, as a rapid-access resource to evaluate the validity of biomarkers. Candidate biomarkers for RCC and RT were identified from the literature and their quality was evaluated and selected biomarkers were prioritised. The duration of follow-up was a limitation but biomarkers were identified that may be taken forward for clinical utility. In the third workstream, the ELUCIDATE trial registered 1303 patients and randomised 878 patients out of a target of 1000. The trial started late and recruited slowly initially but ultimately recruited with good statistical power to answer the key questions. ELF monitoring altered the patient process of care and may show benefits from the early introduction of interventions with further follow-up. The ELUCIDATE trial was an ‘exemplar’ trial that has demonstrated the challenges of evaluating biomarker strategies in ‘end-to-end’ RCTs and will inform future study designs.ConclusionsThe limitations in the programme were principally that, during the collection and curation of the cohorts of patients with RCC and RT, the pace of discovery of new biomarkers in commercial and non-commercial research was slower than anticipated and so conclusive evaluations using the cohorts are few; however, access to the cohorts will be sustained for future new biomarkers. The ELUCIDATE trial was slow to start and recruit to, with a late surge of recruitment, and so final conclusions about the impact of the ELF test on long-term outcomes await further follow-up. The findings from the three workstreams were used to synthesise a strategy and framework for future biomarker evaluations incorporating innovations in study design, health economics and health informatics.Trial registrationCurrent Controlled Trials ISRCTN74815110, UKCRN ID 9954 and UKCRN ID 11930.FundingThis project was funded by the NIHR Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 6, No. 3. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Peter J Selby
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Rosamonde E Banks
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Walter Gregory
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Jenny Hewison
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - William Rosenberg
- Institute for Liver and Digestive Health, Division of Medicine, University College London, London, UK
| | - Douglas G Altman
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Jonathan J Deeks
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Christopher McCabe
- Department of Emergency Medicine, University of Alberta Hospital, Edmonton, AB, Canada
| | - Julie Parkes
- Primary Care and Population Sciences Academic Unit, University of Southampton, Southampton, UK
| | | | | | - Maureen Twiddy
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Janine Bestall
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | | | - Tilly Hale
- LIVErNORTH Liver Patient Support, Newcastle upon Tyne, UK
| | - Jacqueline Dinnes
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Marc Jones
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | | | | | - Vicky Napp
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Alice Sitch
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Sudeep Tanwar
- Institute for Liver and Digestive Health, Division of Medicine, University College London, London, UK
| | - Naveen S Vasudev
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Paul Baxter
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Sue Bell
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - David A Cairns
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | | | - Neil Corrigan
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Francesco Del Galdo
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
| | - Peter Heudtlass
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Nick Hornigold
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Claire Hulme
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Michelle Hutchinson
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Carys Lippiatt
- Department of Specialist Laboratory Medicine, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | - Roberta Longo
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Matthew Potton
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Stephanie Roberts
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Sheryl Sim
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Sebastian Trainor
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Matthew Welberry Smith
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - James Neuberger
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | | | - Paul Richardson
- Royal Liverpool and Broadgreen University Hospitals NHS Trust, Liverpool, UK
| | - John Christie
- Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Neil Sheerin
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - William McKane
- Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Paul Gibbs
- Portsmouth Hospitals NHS Trust, Portsmouth, UK
| | | | - Naeem Soomro
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | | | - Grant D Stewart
- NHS Lothian, Edinburgh, UK
- Academic Urology Group, University of Cambridge, Cambridge, UK
| | - David Hrouda
- Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK
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Tilling K, Lawton M, Robertson N, Tremlett H, Zhu F, Harding K, Oger J, Ben-Shlomo Y. Modelling disease progression in relapsing-remitting onset multiple sclerosis using multilevel models applied to longitudinal data from two natural history cohorts and one treated cohort. Health Technol Assess 2018; 20:1-48. [PMID: 27817792 DOI: 10.3310/hta20810] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND The ability to better predict disease progression represents a major unmet need in multiple sclerosis (MS), and would help to inform therapeutic and management choices. OBJECTIVES To develop multilevel models using longitudinal data on disease progression in patients with relapsing-remitting MS (RRMS) or secondary-progressive MS (SPMS); and to use these models to estimate the association of disease-modifying therapy (DMT) with progression. DESIGN Secondary analysis of three MS cohorts. SETTING Two natural history cohorts: University of Wales Multiple Sclerosis (UoWMS) cohort, UK, and British Columbia Multiple Sclerosis (BCMS) cohort, Canada. One observational DMT-treated cohort: UK MS risk-sharing scheme (RSS). PARTICIPANTS The UoWMS database has > 2000 MS patients and the BCMS database (as of 2009) has > 5900 MS patients. All participants who had definite MS (RRMS/SPMS), who reached the criteria set out by the Association of British Neurologists (ABN) for eligibility for DMT [i.e. age ≥ 18 years, Expanded Disability Status Scale (EDSS) score of ≤ 6.5, occurrence of two or more relapses in the previous 2 years] and who had at least two repeated outcome measures were included: 404 patients for the UoWMS cohort and 978 patients for the BCMS cohort. Through the UK MS RSS scheme, 5583 DMT-treated patients were recruited, with the analysis sample being the 4137 who had RRMS and were eligible and treated at baseline, with at least one valid EDSS score post baseline. MAIN OUTCOME MEASURES EDSS score observations post ABN eligibility. METHODS We used multilevel models in the development cohort (UoWMS) to develop a model for EDSS score with time since ABN eligibility, allowing for covariates and appropriate transformation of outcome and/or time. These methods were then applied to the BCMS cohort to obtain a 'natural history' model for changes in the EDSS score with time. We then used this natural history model to predict the trajectories of EDSS score in treated patients in the UK MS RSS database. Differences between the progression predicted by the natural history model and the progression observed at 6 years' follow-up for the UK MS RSS cohort were used as indicators of the effectiveness of the DMTs. Previously developed utility scores were assigned to each EDSS score, and differences in utility also examined. RESULTS The model best fitting the UoWMS data showed a non-linear increase in EDSS score over time since ABN eligibility. This model fitted the BCMS cohort data well, with similar coefficients, and the BCMS model predicted EDSS score in UoWMS data with little evidence of bias. Using the natural history model predicts EDSS score in a treated cohort (UK MS RSS) higher than that observed [by 0.59 points (95% confidence interval 0.54 to 0.64 points)] at 6 years post treatment. LIMITATIONS Only two natural history cohorts were compared, limiting generalisability. The comparison of a treated cohort with untreated cohorts is observational, thus limiting conclusions about causality. CONCLUSIONS EDSS score progression in two natural history cohorts of MS patients showed a similar pattern. Progression in the natural history cohorts was slightly faster than EDSS score progression in the DMT-treated cohort, up to 6 years post treatment. FUTURE WORK Long-term follow-up of randomised controlled trials is needed to replicate these findings and examine duration of any treatment effect. FUNDING DETAILS The National Institute for Health Research Health Technology Assessment programme.
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Affiliation(s)
- Kate Tilling
- School of Social and Community Medicine, Bristol University, Bristol, UK
| | - Michael Lawton
- School of Social and Community Medicine, Bristol University, Bristol, UK
| | - Neil Robertson
- Department of Neurology, Institute of Psychological Medicine and Clinical Neuroscience, Cardiff University, Cardiff, UK
| | - Helen Tremlett
- Faculty of Medicine, Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Feng Zhu
- Faculty of Medicine, Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Katharine Harding
- Department of Neurology, Institute of Psychological Medicine and Clinical Neuroscience, Cardiff University, Cardiff, UK
| | - Joel Oger
- Faculty of Medicine, Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Yoav Ben-Shlomo
- School of Social and Community Medicine, Bristol University, Bristol, UK
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Burny C, Rabilloud M, Golfier F, Massardier J, Hajri T, Schott AM, Subtil F. Early diagnosis of gestational trophoblastic neoplasia based on trajectory classification with compartment modeling. BMC Med Res Methodol 2016; 16:3. [PMID: 26732086 PMCID: PMC4702411 DOI: 10.1186/s12874-015-0106-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 12/22/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In randomized clinical trials or observational studies, it is common to collect biomarker values longitudinally on a cohort of individuals. The investigators may be interested in grouping individuals that share similar changes of biomarker values and use these groups for diagnosis or therapeutic purposes. However, most classical model-based classification methods rely mainly on empirical models such as splines or polynomials and do not reflect the physiological processes. METHODS A model-based classification method was developed for longitudinal biomarker measurements through a pharmacokinetic model that describes biomarker changes over time. The method is illustrated using data on human Chorionic Gonadotrophic Hormone measurements after curettage of hydatidiform moles. RESULTS The resulting classification was linked to the evolution toward gestational trophoblastic neoplasia and may be used as a tool for early diagnosis. The diagnostic accuracy of the pharmacokinetic model was more reproducible than the one of a purely mathematical model that did not take into account the biological processes. CONCLUSION The use of pharmacokinetic models in model-based classification approaches can lead to clinically useful classifications.
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Affiliation(s)
- Claire Burny
- Service de Biostatistique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France. .,Université de Lyon, F-69000, Lyon, France. .,Université Lyon 1, F-69100, Villeurbanne, France. .,CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique-Santé, F-69100, Villeurbanne, France.
| | - Muriel Rabilloud
- Service de Biostatistique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France. .,Université de Lyon, F-69000, Lyon, France. .,Université Lyon 1, F-69100, Villeurbanne, France. .,CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique-Santé, F-69100, Villeurbanne, France.
| | - François Golfier
- Université de Lyon, F-69000, Lyon, France. .,Université Lyon 1, F-69100, Villeurbanne, France. .,Department of Gynaecological and Oncological Surgery-Obstetrics, Lyon Sud University Hospital, Lyon, France. .,French Trophoblastic Disease Reference Centre, Lyon Sud University Hospital, Lyon, France.
| | - Jérôme Massardier
- French Trophoblastic Disease Reference Centre, Lyon Sud University Hospital, Lyon, France. .,Department of Obstetrics, University Hospital Femme-Mère-Enfant, Lyon, France.
| | - Touria Hajri
- Université de Lyon, F-69000, Lyon, France. .,Université Lyon 1, F-69100, Villeurbanne, France. .,French Trophoblastic Disease Reference Centre, Lyon Sud University Hospital, Lyon, France. .,Pôle Information Médicale Evaluation Recherche, Equipe d'Accueil 4129, Hospices Civils de Lyon, Lyon, France.
| | - Anne-Marie Schott
- Université de Lyon, F-69000, Lyon, France. .,Université Lyon 1, F-69100, Villeurbanne, France. .,Pôle Information Médicale Evaluation Recherche, Equipe d'Accueil 4129, Hospices Civils de Lyon, Lyon, France.
| | - Fabien Subtil
- Service de Biostatistique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France. .,Université de Lyon, F-69000, Lyon, France. .,Université Lyon 1, F-69100, Villeurbanne, France. .,CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique-Santé, F-69100, Villeurbanne, France.
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Strathe AB, Dijkstra J, France J, Lopez S, Yan T, Kebreab E. A Bayesian approach to analyze energy balance data from lactating dairy cows. J Dairy Sci 2011; 94:2520-31. [PMID: 21524544 DOI: 10.3168/jds.2010-3836] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2010] [Accepted: 01/23/2011] [Indexed: 11/19/2022]
Abstract
The objective of the present investigation was to develop a Bayesian framework for updating and integrating covariate information into key parameters of metabolizable energy (ME) systems for dairy cows. The study addressed specifically the effects of genetic improvements and feed quality on key parameters in current ME systems. These are net and metabolizable energy for maintenance (NE(M) and ME(M), respectively), efficiency of utilization of ME for milk production (k(L)) and growth (k(G)), and efficiency of utilization of body stores for milk production (k(T)). Data were collated from 38 studies, yielding 701 individual cow observations on milk energy, ME intake, and tissue gain and loss. A function based on a linear relationship between milk energy and ME intake and correcting for tissue energy loss or gain served as the basis of a full Bayesian hierarchical model. The within-study variability was modeled by a Student t-distribution and the between-study variability in the structural parameters was modeled by a multivariate normal distribution. A meaningful relationship between genetic improvements in milk production and the key parameters could not be established. The parameter k(L) was linearly related to feed metabolizability, and the slope predicted a 0.010 (-0.0004; 0.0210) change per 0.1-unit change in metabolizability. The effect of metabolizability on k(L) was smaller than assumed in present feed evaluation systems and its significance was dependent on collection of studies included in the analysis. Three sets of population estimates (with 95% credible interval in parentheses) were generated, reflecting different degrees of prior belief: (1) Noninformative priors yielded 0.28 (0.23; 0.33) MJ/(kg(0.75)d), 0.55 (0.51; 0.58), 0.86 (0.81; 0.93) and 0.66 (0.58; 0.75), for NE(M), k(L), k(G), and k(T), respectively; (2) Introducing an informative prior that was derived from a fasting metabolism study served to combine the most recent information on energy metabolism in modern dairy cows. The new estimates of NE(M), k(L), k(G) and k(T) were 0.34 (0.28; 0.39) MJ/(kg(0.75)d), 0.58 (0.54; 0.62), 0.89 (0.85; 0.95), and 0.69 (0.60; 0.79), respectively; (3) finally, all informative priors were used that were established from literature, yielding estimates for NE(M), k(L), k(G), and k(T) of 0.29 (0.11; 0.46) MJ/(kg(0.75)d), 0.60 (0.54; 0.70), 0.70 (0.50; 0.88), and 0.80 (0.67; 0.97), respectively. Bayesian methods are especially applicable in meta-analytical studies as information can enter at various stages in the hierarchical model.
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Affiliation(s)
- A B Strathe
- Department of Animal Science, University of California, Davis 95616, USA.
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